X. He
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1
Perceived risk is crucial in designing trustworthy and acceptable vehicle automation systems. However, our understanding of perceived risk dynamics remains limited, and corresponding computational models are scarce. This study formulates a new computational perceived risk model based on potential collision avoidance difficulty (PCAD) for drivers of SAE Level 2 automated vehicles. PCAD quantifies task difficulty using the gap between the current velocity and the safe velocity region in 2D, and accounts for the minimal control effort (braking and/or steering) needed to avoid a potential collision, based on visual looming, behavioural uncertainties of neighbouring vehicles, imprecise control of the subject vehicle, and collision severity. The PCAD model predicts both continuous-time perceived risk and peak perceived risk per event. We analyse model properties both theoretically and empirically with two unique datasets: Datasets Merging and Obstacle Avoidance. The PCAD model generally outperforms three state-of-the-art models in terms of model error, detection rate, and the ability to accurately capture the tendencies of human drivers’ perceived risk, albeit at the cost of longer computation time. Our findings reveal that perceived risk varies with the position, velocity, and acceleration of the subject and neighbouring vehicles, and is influenced by uncertainties in their velocities.
...
Perceived risk is crucial in designing trustworthy and acceptable vehicle automation systems. However, our understanding of perceived risk dynamics remains limited, and corresponding computational models are scarce. This study formulates a new computational perceived risk model based on potential collision avoidance difficulty (PCAD) for drivers of SAE Level 2 automated vehicles. PCAD quantifies task difficulty using the gap between the current velocity and the safe velocity region in 2D, and accounts for the minimal control effort (braking and/or steering) needed to avoid a potential collision, based on visual looming, behavioural uncertainties of neighbouring vehicles, imprecise control of the subject vehicle, and collision severity. The PCAD model predicts both continuous-time perceived risk and peak perceived risk per event. We analyse model properties both theoretically and empirically with two unique datasets: Datasets Merging and Obstacle Avoidance. The PCAD model generally outperforms three state-of-the-art models in terms of model error, detection rate, and the ability to accurately capture the tendencies of human drivers’ perceived risk, albeit at the cost of longer computation time. Our findings reveal that perceived risk varies with the position, velocity, and acceleration of the subject and neighbouring vehicles, and is influenced by uncertainties in their velocities.
Driver's perceived risk in relation to automated vehicle behaviour
Evaluation and mitigation of perceived risk through simulator studies, computational models, and user interface design
Automated vehicles (AVs) represent a significant leap forward in transportation, aiming to enhance road safety, increase comfort, and improve traffic efficiency. As technology progresses to SAE Level 3 and higher, drivers are increasingly able to engage in non-driving-related activities. However, this (r)evolution raises challenges concerning driver's perceived risk and trust in AVs, which are crucial factors influencing the acceptance of AVs. This dissertation aims to enhance perceived safety and trust in AVs through experimental studies, computational modelling and user interfaces (UIs) design.
The initial phase of this dissertation focused on how drivers' perceived risk and trust when using AVs evolve in close encounters with other road users. We developed regression-based perceived risk and trust models based on a simulator study with 25 participants involving merging and hard braking scenarios on motorways. The proposed models reveal that perceived risk is dynamically influenced by driving conditions and sensitive to individual factors such as driving experience and gender with experienced and male drivers generally perceiving lower risk. Notably, a decrease in trust after high-risk encounters was observed, indicating a close relationship between perceived risk and trust in AVs. Additionally, physiological responses were observed as potential indicators of perceived risk in critical driving scenarios.
To develop a tool for gaining insights on perceived risk, we put forward a novel computational model called potential collision avoidance difficulty (PCAD) model. Drawing inspiration from Fuller's Risk Allostasis Theory and the looming phenomenon, PCAD evaluates the difficulty of avoiding potential collisions by calculating minimal control effort through braking or/and steering needed to navigate safely. By integrating visual looming, factors in the uncertain behaviour of surrounding vehicles, control inaccuracies of the subject vehicle, and potential collision severity, PCAD provided an accurate population-level fitting of perceived risk in our own dataset on highway merging and a published dataset on obstacle avoidance. The findings highlight the need to account for both the longitudinal and lateral dimensions of driving condition, and uncertain behaviours of surrounding vehicles when interpreting perceived risk.
Further exploration of perceived risk was achieved through the creation of a large-scale dataset of perceived risk using an online survey. This new dataset provided time-continuous perceived risk in dynamic driving conditions. A total of 105 events was created including merging, hard braking and lane changes on motorways, while systematically varying multiple control parameters (such as relative speed and distance) to achieve different levels of event criticalities. Deep neural networks (DNNs) were then trained on this dataset to fit perceived risk, and SHapley Additive exPlanations (SHAP) was used to identify the key contributors to perceived risk in the continuous time domain. Aligned with the PCAD model developed previously, the results highlighted the importance of the relative motion information, particularly the distance to other road users and the uncertainty of surrounding vehicle behaviour in shaping perceived risk. This approach not only discerns the dynamics of perceived risk by systematically analysing interactions with other road users but also provides a guide for future modelling of perceived risk. The development of this extensive dataset fills the gap by providing the lacking continuous perceived risk data, thereby supporting further research on perceived risk.
The last contribution of this dissertation was on enhancing perceived safety and trust through optimised design of UIs. A simulator experiment demonstrated that multimodal UIs incorporating both visual and auditory modalities enhanced perceived safety and trust the most. Manoeuvre information delivered through the auditory modality was particularly effective in enhancing trust and acceptance. The findings indicate the benefits of the UIs in enhancing perceived safety and trust but also showed the limitations of using UIs alone during highly critical events. This part of the work suggests that the design of UIs for partially automated vehicles shall include automation information via visual and auditory modalities to enhance perceived safety and trust.
This dissertation makes several contributions to the field of perceived risk research in AVs. First, it provides foundational insights into perceived risk, demonstrating the significant influences of driving conditions, manoeuvre uncertainties and individual personal characteristics. The computational perceived risk models demonstrate strong predictive power in perceived risk and offer a deep understanding of how perceived risk is shaped in dynamic driving conditions. Additionally, the rich dataset obtained in this dissertation, which includes event-based discrete data and time-continuous data on perceived risk, serves as a new and open resource for future perceived risk research. Lastly, the practical evaluation of the design of UI provided actionable recommendations in enhancing trust and perceived safety, particularly through manoeuvre information delivered using auditory modality. These contributions advance the understanding, modelling, and practical application of perceived risk in automated driving environments, supporting the broader acceptance and integration of AVs.
The dissertation presents various opportunities for the advancement of AV technology and its integration with human factors. Building on the comprehensive datasets, computational models and insights gained in this dissertation, future studies should focus on further refining computational models to capture perceived risk in general scenarios. Expanding data collection efforts to include on-road tests, and more diverse participants will also enhance the generalisability of the findings. Additionally, the design of adaptive UIs that fit individual preferences remains a promising direction for future research. ...
The initial phase of this dissertation focused on how drivers' perceived risk and trust when using AVs evolve in close encounters with other road users. We developed regression-based perceived risk and trust models based on a simulator study with 25 participants involving merging and hard braking scenarios on motorways. The proposed models reveal that perceived risk is dynamically influenced by driving conditions and sensitive to individual factors such as driving experience and gender with experienced and male drivers generally perceiving lower risk. Notably, a decrease in trust after high-risk encounters was observed, indicating a close relationship between perceived risk and trust in AVs. Additionally, physiological responses were observed as potential indicators of perceived risk in critical driving scenarios.
To develop a tool for gaining insights on perceived risk, we put forward a novel computational model called potential collision avoidance difficulty (PCAD) model. Drawing inspiration from Fuller's Risk Allostasis Theory and the looming phenomenon, PCAD evaluates the difficulty of avoiding potential collisions by calculating minimal control effort through braking or/and steering needed to navigate safely. By integrating visual looming, factors in the uncertain behaviour of surrounding vehicles, control inaccuracies of the subject vehicle, and potential collision severity, PCAD provided an accurate population-level fitting of perceived risk in our own dataset on highway merging and a published dataset on obstacle avoidance. The findings highlight the need to account for both the longitudinal and lateral dimensions of driving condition, and uncertain behaviours of surrounding vehicles when interpreting perceived risk.
Further exploration of perceived risk was achieved through the creation of a large-scale dataset of perceived risk using an online survey. This new dataset provided time-continuous perceived risk in dynamic driving conditions. A total of 105 events was created including merging, hard braking and lane changes on motorways, while systematically varying multiple control parameters (such as relative speed and distance) to achieve different levels of event criticalities. Deep neural networks (DNNs) were then trained on this dataset to fit perceived risk, and SHapley Additive exPlanations (SHAP) was used to identify the key contributors to perceived risk in the continuous time domain. Aligned with the PCAD model developed previously, the results highlighted the importance of the relative motion information, particularly the distance to other road users and the uncertainty of surrounding vehicle behaviour in shaping perceived risk. This approach not only discerns the dynamics of perceived risk by systematically analysing interactions with other road users but also provides a guide for future modelling of perceived risk. The development of this extensive dataset fills the gap by providing the lacking continuous perceived risk data, thereby supporting further research on perceived risk.
The last contribution of this dissertation was on enhancing perceived safety and trust through optimised design of UIs. A simulator experiment demonstrated that multimodal UIs incorporating both visual and auditory modalities enhanced perceived safety and trust the most. Manoeuvre information delivered through the auditory modality was particularly effective in enhancing trust and acceptance. The findings indicate the benefits of the UIs in enhancing perceived safety and trust but also showed the limitations of using UIs alone during highly critical events. This part of the work suggests that the design of UIs for partially automated vehicles shall include automation information via visual and auditory modalities to enhance perceived safety and trust.
This dissertation makes several contributions to the field of perceived risk research in AVs. First, it provides foundational insights into perceived risk, demonstrating the significant influences of driving conditions, manoeuvre uncertainties and individual personal characteristics. The computational perceived risk models demonstrate strong predictive power in perceived risk and offer a deep understanding of how perceived risk is shaped in dynamic driving conditions. Additionally, the rich dataset obtained in this dissertation, which includes event-based discrete data and time-continuous data on perceived risk, serves as a new and open resource for future perceived risk research. Lastly, the practical evaluation of the design of UI provided actionable recommendations in enhancing trust and perceived safety, particularly through manoeuvre information delivered using auditory modality. These contributions advance the understanding, modelling, and practical application of perceived risk in automated driving environments, supporting the broader acceptance and integration of AVs.
The dissertation presents various opportunities for the advancement of AV technology and its integration with human factors. Building on the comprehensive datasets, computational models and insights gained in this dissertation, future studies should focus on further refining computational models to capture perceived risk in general scenarios. Expanding data collection efforts to include on-road tests, and more diverse participants will also enhance the generalisability of the findings. Additionally, the design of adaptive UIs that fit individual preferences remains a promising direction for future research. ...
Automated vehicles (AVs) represent a significant leap forward in transportation, aiming to enhance road safety, increase comfort, and improve traffic efficiency. As technology progresses to SAE Level 3 and higher, drivers are increasingly able to engage in non-driving-related activities. However, this (r)evolution raises challenges concerning driver's perceived risk and trust in AVs, which are crucial factors influencing the acceptance of AVs. This dissertation aims to enhance perceived safety and trust in AVs through experimental studies, computational modelling and user interfaces (UIs) design.
The initial phase of this dissertation focused on how drivers' perceived risk and trust when using AVs evolve in close encounters with other road users. We developed regression-based perceived risk and trust models based on a simulator study with 25 participants involving merging and hard braking scenarios on motorways. The proposed models reveal that perceived risk is dynamically influenced by driving conditions and sensitive to individual factors such as driving experience and gender with experienced and male drivers generally perceiving lower risk. Notably, a decrease in trust after high-risk encounters was observed, indicating a close relationship between perceived risk and trust in AVs. Additionally, physiological responses were observed as potential indicators of perceived risk in critical driving scenarios.
To develop a tool for gaining insights on perceived risk, we put forward a novel computational model called potential collision avoidance difficulty (PCAD) model. Drawing inspiration from Fuller's Risk Allostasis Theory and the looming phenomenon, PCAD evaluates the difficulty of avoiding potential collisions by calculating minimal control effort through braking or/and steering needed to navigate safely. By integrating visual looming, factors in the uncertain behaviour of surrounding vehicles, control inaccuracies of the subject vehicle, and potential collision severity, PCAD provided an accurate population-level fitting of perceived risk in our own dataset on highway merging and a published dataset on obstacle avoidance. The findings highlight the need to account for both the longitudinal and lateral dimensions of driving condition, and uncertain behaviours of surrounding vehicles when interpreting perceived risk.
Further exploration of perceived risk was achieved through the creation of a large-scale dataset of perceived risk using an online survey. This new dataset provided time-continuous perceived risk in dynamic driving conditions. A total of 105 events was created including merging, hard braking and lane changes on motorways, while systematically varying multiple control parameters (such as relative speed and distance) to achieve different levels of event criticalities. Deep neural networks (DNNs) were then trained on this dataset to fit perceived risk, and SHapley Additive exPlanations (SHAP) was used to identify the key contributors to perceived risk in the continuous time domain. Aligned with the PCAD model developed previously, the results highlighted the importance of the relative motion information, particularly the distance to other road users and the uncertainty of surrounding vehicle behaviour in shaping perceived risk. This approach not only discerns the dynamics of perceived risk by systematically analysing interactions with other road users but also provides a guide for future modelling of perceived risk. The development of this extensive dataset fills the gap by providing the lacking continuous perceived risk data, thereby supporting further research on perceived risk.
The last contribution of this dissertation was on enhancing perceived safety and trust through optimised design of UIs. A simulator experiment demonstrated that multimodal UIs incorporating both visual and auditory modalities enhanced perceived safety and trust the most. Manoeuvre information delivered through the auditory modality was particularly effective in enhancing trust and acceptance. The findings indicate the benefits of the UIs in enhancing perceived safety and trust but also showed the limitations of using UIs alone during highly critical events. This part of the work suggests that the design of UIs for partially automated vehicles shall include automation information via visual and auditory modalities to enhance perceived safety and trust.
This dissertation makes several contributions to the field of perceived risk research in AVs. First, it provides foundational insights into perceived risk, demonstrating the significant influences of driving conditions, manoeuvre uncertainties and individual personal characteristics. The computational perceived risk models demonstrate strong predictive power in perceived risk and offer a deep understanding of how perceived risk is shaped in dynamic driving conditions. Additionally, the rich dataset obtained in this dissertation, which includes event-based discrete data and time-continuous data on perceived risk, serves as a new and open resource for future perceived risk research. Lastly, the practical evaluation of the design of UI provided actionable recommendations in enhancing trust and perceived safety, particularly through manoeuvre information delivered using auditory modality. These contributions advance the understanding, modelling, and practical application of perceived risk in automated driving environments, supporting the broader acceptance and integration of AVs.
The dissertation presents various opportunities for the advancement of AV technology and its integration with human factors. Building on the comprehensive datasets, computational models and insights gained in this dissertation, future studies should focus on further refining computational models to capture perceived risk in general scenarios. Expanding data collection efforts to include on-road tests, and more diverse participants will also enhance the generalisability of the findings. Additionally, the design of adaptive UIs that fit individual preferences remains a promising direction for future research.
The initial phase of this dissertation focused on how drivers' perceived risk and trust when using AVs evolve in close encounters with other road users. We developed regression-based perceived risk and trust models based on a simulator study with 25 participants involving merging and hard braking scenarios on motorways. The proposed models reveal that perceived risk is dynamically influenced by driving conditions and sensitive to individual factors such as driving experience and gender with experienced and male drivers generally perceiving lower risk. Notably, a decrease in trust after high-risk encounters was observed, indicating a close relationship between perceived risk and trust in AVs. Additionally, physiological responses were observed as potential indicators of perceived risk in critical driving scenarios.
To develop a tool for gaining insights on perceived risk, we put forward a novel computational model called potential collision avoidance difficulty (PCAD) model. Drawing inspiration from Fuller's Risk Allostasis Theory and the looming phenomenon, PCAD evaluates the difficulty of avoiding potential collisions by calculating minimal control effort through braking or/and steering needed to navigate safely. By integrating visual looming, factors in the uncertain behaviour of surrounding vehicles, control inaccuracies of the subject vehicle, and potential collision severity, PCAD provided an accurate population-level fitting of perceived risk in our own dataset on highway merging and a published dataset on obstacle avoidance. The findings highlight the need to account for both the longitudinal and lateral dimensions of driving condition, and uncertain behaviours of surrounding vehicles when interpreting perceived risk.
Further exploration of perceived risk was achieved through the creation of a large-scale dataset of perceived risk using an online survey. This new dataset provided time-continuous perceived risk in dynamic driving conditions. A total of 105 events was created including merging, hard braking and lane changes on motorways, while systematically varying multiple control parameters (such as relative speed and distance) to achieve different levels of event criticalities. Deep neural networks (DNNs) were then trained on this dataset to fit perceived risk, and SHapley Additive exPlanations (SHAP) was used to identify the key contributors to perceived risk in the continuous time domain. Aligned with the PCAD model developed previously, the results highlighted the importance of the relative motion information, particularly the distance to other road users and the uncertainty of surrounding vehicle behaviour in shaping perceived risk. This approach not only discerns the dynamics of perceived risk by systematically analysing interactions with other road users but also provides a guide for future modelling of perceived risk. The development of this extensive dataset fills the gap by providing the lacking continuous perceived risk data, thereby supporting further research on perceived risk.
The last contribution of this dissertation was on enhancing perceived safety and trust through optimised design of UIs. A simulator experiment demonstrated that multimodal UIs incorporating both visual and auditory modalities enhanced perceived safety and trust the most. Manoeuvre information delivered through the auditory modality was particularly effective in enhancing trust and acceptance. The findings indicate the benefits of the UIs in enhancing perceived safety and trust but also showed the limitations of using UIs alone during highly critical events. This part of the work suggests that the design of UIs for partially automated vehicles shall include automation information via visual and auditory modalities to enhance perceived safety and trust.
This dissertation makes several contributions to the field of perceived risk research in AVs. First, it provides foundational insights into perceived risk, demonstrating the significant influences of driving conditions, manoeuvre uncertainties and individual personal characteristics. The computational perceived risk models demonstrate strong predictive power in perceived risk and offer a deep understanding of how perceived risk is shaped in dynamic driving conditions. Additionally, the rich dataset obtained in this dissertation, which includes event-based discrete data and time-continuous data on perceived risk, serves as a new and open resource for future perceived risk research. Lastly, the practical evaluation of the design of UI provided actionable recommendations in enhancing trust and perceived safety, particularly through manoeuvre information delivered using auditory modality. These contributions advance the understanding, modelling, and practical application of perceived risk in automated driving environments, supporting the broader acceptance and integration of AVs.
The dissertation presents various opportunities for the advancement of AV technology and its integration with human factors. Building on the comprehensive datasets, computational models and insights gained in this dissertation, future studies should focus on further refining computational models to capture perceived risk in general scenarios. Expanding data collection efforts to include on-road tests, and more diverse participants will also enhance the generalisability of the findings. Additionally, the design of adaptive UIs that fit individual preferences remains a promising direction for future research.
Designing user interfaces for partially automated Vehicles
Effects of information and modality on trust and acceptance
Trust and perceived safety are pivotal in the acceptance of automated vehicles and can be enhanced by providing users with automation information on the (safe) operation of the vehicle. This study aims to identify how user interfaces (UI) can enhance drivers' trust and acceptance and reduce perceived risk in partially automated vehicles. Four interfaces were designed with different levels of complexity. These levels were achieved by combining automation information (surrounding information vs surrounding and manoeuvre information) and modality (visual vs visual and auditory). These interfaces were evaluated in a driving simulator in which a partially automated vehicle reacted to an event of a merging and braking vehicle in its front. The criticality of the events was manipulated by the factors merging gap (in meters) and deceleration (m/s2) of the vehicle in front. The reaction of the automation was either to brake or to change lanes. The results show that an optimal combination of automation information and modality enhances drivers' trust and acceptance. More specifically, the most advanced UI, which provided surrounding and manoeuvre information via the visual and auditory modalities, was associated with the highest trust and acceptance ranking and the lowest perceived risk. Manoeuvre information delivered through the auditory modality was particularly effective in enhancing trust and acceptance. The benefits of the UIs were consistent over events. However, in the most critical events, drivers did not feel entirely safe and did not trust the automation completely. This study suggests that the design of UIs for partially automated vehicles shall include automation information via visual and auditory modalities.
...
Trust and perceived safety are pivotal in the acceptance of automated vehicles and can be enhanced by providing users with automation information on the (safe) operation of the vehicle. This study aims to identify how user interfaces (UI) can enhance drivers' trust and acceptance and reduce perceived risk in partially automated vehicles. Four interfaces were designed with different levels of complexity. These levels were achieved by combining automation information (surrounding information vs surrounding and manoeuvre information) and modality (visual vs visual and auditory). These interfaces were evaluated in a driving simulator in which a partially automated vehicle reacted to an event of a merging and braking vehicle in its front. The criticality of the events was manipulated by the factors merging gap (in meters) and deceleration (m/s2) of the vehicle in front. The reaction of the automation was either to brake or to change lanes. The results show that an optimal combination of automation information and modality enhances drivers' trust and acceptance. More specifically, the most advanced UI, which provided surrounding and manoeuvre information via the visual and auditory modalities, was associated with the highest trust and acceptance ranking and the lowest perceived risk. Manoeuvre information delivered through the auditory modality was particularly effective in enhancing trust and acceptance. The benefits of the UIs were consistent over events. However, in the most critical events, drivers did not feel entirely safe and did not trust the automation completely. This study suggests that the design of UIs for partially automated vehicles shall include automation information via visual and auditory modalities.
Do driver’s characteristics, system performance, perceived safety, and trust influence how drivers use partial automation?
A structural equation modelling analysis
The present study surveyed actual extensive users of SAE Level 2 partially automated cars to investigate how driver’s characteristics (i.e., socio-demographics, driving experience, personality), system performance, perceived safety, and trust in partial automation influence use of partial automation. 81% of respondents stated that they use their automated car with speed (ACC) and steering assist (LKA) at least 1–2 times a week, and 84 and 92% activate LKA and ACC at least occasionally. Respondents positively rated the performance of Adaptive Cruise Control (ACC) and Lane Keeping Assistance (LKA). ACC was rated higher than LKA and detection of lead vehicles and lane markings was rated higher than smooth control for ACC and LKA, respectively. Respondents reported to primarily disengage (i.e., turn off) partial automation due to a lack of trust in the system and when driving is fun. They rarely disengaged the system when they noticed they become bored or sleepy. Structural equation modelling revealed that trust had a positive effect on driver’s propensity for secondary task engagement during partially automated driving, while the effect of perceived safety was not significant. Regarding driver’s characteristics, we did not find a significant effect of age on perceived safety and trust in partial automation. Neuroticism negatively correlated with perceived safety and trust, while extraversion did not impact perceived safety and trust. The remaining three personality dimensions ‘openness’, ‘conscientiousness’, and ‘agreeableness’ did not form valid and reliable scales in the confirmatory factor analysis, and could thus not be subjected to the structural equation modelling analysis. Future research should re-assess the suitability of the short 10-item scale as measure of the Big-Five personality traits, and investigate the impact on perceived safety, trust, use and use of automation.
...
The present study surveyed actual extensive users of SAE Level 2 partially automated cars to investigate how driver’s characteristics (i.e., socio-demographics, driving experience, personality), system performance, perceived safety, and trust in partial automation influence use of partial automation. 81% of respondents stated that they use their automated car with speed (ACC) and steering assist (LKA) at least 1–2 times a week, and 84 and 92% activate LKA and ACC at least occasionally. Respondents positively rated the performance of Adaptive Cruise Control (ACC) and Lane Keeping Assistance (LKA). ACC was rated higher than LKA and detection of lead vehicles and lane markings was rated higher than smooth control for ACC and LKA, respectively. Respondents reported to primarily disengage (i.e., turn off) partial automation due to a lack of trust in the system and when driving is fun. They rarely disengaged the system when they noticed they become bored or sleepy. Structural equation modelling revealed that trust had a positive effect on driver’s propensity for secondary task engagement during partially automated driving, while the effect of perceived safety was not significant. Regarding driver’s characteristics, we did not find a significant effect of age on perceived safety and trust in partial automation. Neuroticism negatively correlated with perceived safety and trust, while extraversion did not impact perceived safety and trust. The remaining three personality dimensions ‘openness’, ‘conscientiousness’, and ‘agreeableness’ did not form valid and reliable scales in the confirmatory factor analysis, and could thus not be subjected to the structural equation modelling analysis. Future research should re-assess the suitability of the short 10-item scale as measure of the Big-Five personality traits, and investigate the impact on perceived safety, trust, use and use of automation.
Journal article
(2023)
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A. El Jouhri, A. El Sharkawy, H. Paksoy, O.K.S.A. Youssif, X. He, S. Kim, R. Happee
Introduction: SAE Level 3 is known as conditional driving automation. As long as certain conditions are met, there is no need to supervise the technology and the driver can engage in non-driving related tasks (NDRTs). However, a human driver must be present and alert to take over when the automation is facing its system limits. When such an emergency takes place, the automation uses the human machine interface (HMI) to send a take-over request (TOR) to the driver.
Methods: We investigated the influence of a color themed HMI on the trust and take-over performance in automated vehicles. Using a driving simulator, we tested 45 participants divided in three groups with a baseline auditory HMI and two advanced color themed HMIs consisting of a display and ambient lighting with the colors red and blue. Trust in automation was assessed using questionnaires while take-over performance was assessed through response time and success rate.
Results: Compared to the baseline HMI, the color themed HMI is more trustworthy, and participants understood their driving tasks better. Results show that the color themed HMI is perceived as more pleasant compared to the baseline HMI and leads to shorter reaction times. Red ambient lighting is seen as more urging than blue, but HMI color did not significantly affect the general HMI perception and TOR performance.
Discussion: Further research can explore the use of color and other modalities to express varying urgency levels and validate findings in complex on road driving conditions. ...
Methods: We investigated the influence of a color themed HMI on the trust and take-over performance in automated vehicles. Using a driving simulator, we tested 45 participants divided in three groups with a baseline auditory HMI and two advanced color themed HMIs consisting of a display and ambient lighting with the colors red and blue. Trust in automation was assessed using questionnaires while take-over performance was assessed through response time and success rate.
Results: Compared to the baseline HMI, the color themed HMI is more trustworthy, and participants understood their driving tasks better. Results show that the color themed HMI is perceived as more pleasant compared to the baseline HMI and leads to shorter reaction times. Red ambient lighting is seen as more urging than blue, but HMI color did not significantly affect the general HMI perception and TOR performance.
Discussion: Further research can explore the use of color and other modalities to express varying urgency levels and validate findings in complex on road driving conditions. ...
Introduction: SAE Level 3 is known as conditional driving automation. As long as certain conditions are met, there is no need to supervise the technology and the driver can engage in non-driving related tasks (NDRTs). However, a human driver must be present and alert to take over when the automation is facing its system limits. When such an emergency takes place, the automation uses the human machine interface (HMI) to send a take-over request (TOR) to the driver.
Methods: We investigated the influence of a color themed HMI on the trust and take-over performance in automated vehicles. Using a driving simulator, we tested 45 participants divided in three groups with a baseline auditory HMI and two advanced color themed HMIs consisting of a display and ambient lighting with the colors red and blue. Trust in automation was assessed using questionnaires while take-over performance was assessed through response time and success rate.
Results: Compared to the baseline HMI, the color themed HMI is more trustworthy, and participants understood their driving tasks better. Results show that the color themed HMI is perceived as more pleasant compared to the baseline HMI and leads to shorter reaction times. Red ambient lighting is seen as more urging than blue, but HMI color did not significantly affect the general HMI perception and TOR performance.
Discussion: Further research can explore the use of color and other modalities to express varying urgency levels and validate findings in complex on road driving conditions.
Methods: We investigated the influence of a color themed HMI on the trust and take-over performance in automated vehicles. Using a driving simulator, we tested 45 participants divided in three groups with a baseline auditory HMI and two advanced color themed HMIs consisting of a display and ambient lighting with the colors red and blue. Trust in automation was assessed using questionnaires while take-over performance was assessed through response time and success rate.
Results: Compared to the baseline HMI, the color themed HMI is more trustworthy, and participants understood their driving tasks better. Results show that the color themed HMI is perceived as more pleasant compared to the baseline HMI and leads to shorter reaction times. Red ambient lighting is seen as more urging than blue, but HMI color did not significantly affect the general HMI perception and TOR performance.
Discussion: Further research can explore the use of color and other modalities to express varying urgency levels and validate findings in complex on road driving conditions.
Passive Film Properties of Martensitic Steels in Alkaline Environment
Influence of the Prior Austenite Grain Size
Journal article
(2022)
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A. Yilmaz, X. He, Sven Pletincx, Tom Hauffman, J. Sietsma, Y. Gonzalez Garcia
The role of prior austenite grain size (PAGS) on the passive layer properties of martensitic steels is studied. Electron backscatter diffraction analysis shows that PAGS between 5 and 66 µm were obtained after applying different heat treatments. The barrier properties of passive film deteriorate with grain refinement up to 28 µm, attributed to increased donor density and a decrease in the fraction of γ-Fe2O3 in passive films. However, the further refinement of PAGS to 5 µm leads to improvement in the barrier properties due to the changes within the martensite structure. This improvement stems from the increase in γ-Fe2O3 fraction.
...
The role of prior austenite grain size (PAGS) on the passive layer properties of martensitic steels is studied. Electron backscatter diffraction analysis shows that PAGS between 5 and 66 µm were obtained after applying different heat treatments. The barrier properties of passive film deteriorate with grain refinement up to 28 µm, attributed to increased donor density and a decrease in the fraction of γ-Fe2O3 in passive films. However, the further refinement of PAGS to 5 µm leads to improvement in the barrier properties due to the changes within the martensite structure. This improvement stems from the increase in γ-Fe2O3 fraction.
Perceived risk and trust are crucial for user acceptance of driving automation. In this study, we identify important predictors of perceived risk and trust in a driving simulator experiment and develop models through stepwise regression to predict event-based changes in perceived risk and trust. 25 participants were tasked to monitor SAE Level 2 driving automation (ACC + LC) while experiencing merging and hard braking events with varying criticality on a motorway. Perceived risk and trust were rated verbally after each event, and continuous perceived risk, pupil diameter and ECG signals were explored as possible indictors for perceived risk and trust. The regression models show that relative motion with neighbouring road users accounts for most perceived risk and trust variations, and no difference was found between hard braking with merging and hard braking without merging. Drivers trust the automation more in the second exposure to events. Our models show modest effects of personal characteristics: experienced drivers are less sensitive to risk and trust the automation more, while female participants perceive more risk than males. Perceived risk and trust highly correlate and have similar determinants. Continuous perceived risk accurately reflects participants’ verbal post-event rating of perceived risk; the use of brakes is an effective indicator of high perceived risk and low trust, and pupil diameter correlates to perceived risk in the most critical events. The events increased heart rate, but we found no correlation with event criticality. The prediction models and the findings on physiological measures shed light on the event-based dynamics of perceived risk and trust and can guide human-centred automation design to reduce perceived risk and enhance trust.
...
Perceived risk and trust are crucial for user acceptance of driving automation. In this study, we identify important predictors of perceived risk and trust in a driving simulator experiment and develop models through stepwise regression to predict event-based changes in perceived risk and trust. 25 participants were tasked to monitor SAE Level 2 driving automation (ACC + LC) while experiencing merging and hard braking events with varying criticality on a motorway. Perceived risk and trust were rated verbally after each event, and continuous perceived risk, pupil diameter and ECG signals were explored as possible indictors for perceived risk and trust. The regression models show that relative motion with neighbouring road users accounts for most perceived risk and trust variations, and no difference was found between hard braking with merging and hard braking without merging. Drivers trust the automation more in the second exposure to events. Our models show modest effects of personal characteristics: experienced drivers are less sensitive to risk and trust the automation more, while female participants perceive more risk than males. Perceived risk and trust highly correlate and have similar determinants. Continuous perceived risk accurately reflects participants’ verbal post-event rating of perceived risk; the use of brakes is an effective indicator of high perceived risk and low trust, and pupil diameter correlates to perceived risk in the most critical events. The events increased heart rate, but we found no correlation with event criticality. The prediction models and the findings on physiological measures shed light on the event-based dynamics of perceived risk and trust and can guide human-centred automation design to reduce perceived risk and enhance trust.
The progress in technology development over the past decades, both with respect to software and hardware, offers the vision of automated vehicles as means of achieving zero fatalities in traffic. However, the promises of this new technology – an increase in road safety, traffic efficiency, and user comfort – can only be realized if this technology is smoothly introduced into the existing traffic system with all its complexities, constraints, and requirements. SHAPE- IT will contribute to this major undertaking by addressing research questions relevant for the development and introduction of automated vehicles in urban traffic scenarios. Previous research has pointed out several research areas that need more attention for a successful implementation and deployment of human-centred vehicle automation in urban environments.
In SHAPE-IT, for example, a better understanding of human behaviour and the underlying psychological mechanisms will lead to improved models of human behaviour that can help to predict the effects of automated systems on human behaviour already during system development. Such models can also be integrated into the algorithms of automated vehicles, enabling them to better understand the human interaction partners’ behaviours.
Further, the development of vehicle automation is much about technology (software and hardware), but the users will be humans and they will interact with humans both inside and outside of the vehicle. To be successful in the development of automated vehicles functionalities, research must be performed on a variety of aspects. Actually, a highly interdisciplinary team of researchers, bringing together expertise and background from various scientific fields related to traffic safety, human factors, human-machine interaction design and evaluation, automation, computational modelling, and artificial intelligence, is likely needed to consider the human-technology aspects of vehicle automation.
Accordingly, SHAPE-IT has recruited fifteen PhD candidates (Early Stage Researchers – ESRs), that work together to facilitate this integration of automated vehicles into complex urban traffic by performing research to support the development of transparent, cooperative, accepted, trustworthy, and safe automated vehicles. With their (and their supervisors’) different scientific background, the candidates bring different theoretical concepts and methodological approaches to the project. This interdisciplinarity of the project team offers the unique possibility for each PhD candidate to address research questions from a broad perspective – including theories and methodological approaches of other interrelated disciplines. This is the main reason why SHAPE-IT has been funded by the European Commission’s Marie Skłodowska-Curie Innovative Training Network (ITN) program that is aimed to train early state researchers in multidisciplinary aspects of research including transferable skills. With the unique scope of SHAPE-IT, including the human-vehicle perspective, considering different road-users (inside and outside of the vehicle), addressing for example trust, transparency, and safety, and including a wide range of methodological approaches, the project members can substantially contribute to the development and deployment of safe and appreciated vehicle automation in the cities of the future.
To achieve the goal of interdisciplinary research, it is necessary to provide the individual PhD candidate with a starting point, especially on the different and diverse methodological approaches of the different disciplines. The empirical, user-centred approach for the development and evaluation of innovative automated vehicle concepts is central to SHAPE- IT. This deliverable (D1.1 “Methodological Framework for Modelling and Empirical Approaches”) provides this starting point. That is, this document provides a broad overview of approaches and methodologies used and developed by the SHAPE-IT ESRs during their research. The SHAPE-IT PhD candidates, as well as other researchers and developers outside of SHAPE-IT, can use this document when searching for appropriate methodological approaches, or simply get a brief overview of research methodologies often employed in automated vehicle research.
The first chapter of the deliverable shortly describes the major methodological approaches to collect data relevant for investigating road user behaviour. Each subchapter describes one approach, ranging from naturalistic driving studies to controlled experiments in driving simulators, with the goal to provide the unfamiliar reader with a broad overview of the approach, including its scope, the type of data collected, and its limitations. Each subchapter ends with recommendations for further reading – literature that provide much more detail and examples.
The second chapter explains four different highly relevant tools for data collection, such as interviews, questionnaires, physiological measures, and as other current tools (the Wizard of Oz paradigm and Augmented and Virtual Reality). As in the first chapter this chapter provides the reader with information about advantages and disadvantages of the different tools and with proposed further readings.
The third chapter deals with computational models of human/agent interaction and presents in four subchapters different modelling approaches, ranging from models based on psychological mechanisms, rule-based and artificial intelligence models to simulation models of traffic interaction.
The fourth chapter is devoted to Requirements Engineering and the challenge of communicating knowledge (e.g., human factors) to developers of automated vehicles. When forming the SHAPE-IT proposal it was identified that there is a lack of communication of human factors knowledge about the highly technical development of automated vehicles. This is why it is highly important that the SHAPE-IT ESRs get training in requirement engineering. Regardless of the ESRs working in academia or industry after their studies it is important to learn how to communicate and disseminate the findings to engineers.
The deliverable ends with the chapter “Method Champions”. Here the expertise and association of the different PhD candidates with the different topics are made explicit to facilitate and encourage networking between PhDs with special expertise and those seeking support, especially with regards to methodological questions. ...
In SHAPE-IT, for example, a better understanding of human behaviour and the underlying psychological mechanisms will lead to improved models of human behaviour that can help to predict the effects of automated systems on human behaviour already during system development. Such models can also be integrated into the algorithms of automated vehicles, enabling them to better understand the human interaction partners’ behaviours.
Further, the development of vehicle automation is much about technology (software and hardware), but the users will be humans and they will interact with humans both inside and outside of the vehicle. To be successful in the development of automated vehicles functionalities, research must be performed on a variety of aspects. Actually, a highly interdisciplinary team of researchers, bringing together expertise and background from various scientific fields related to traffic safety, human factors, human-machine interaction design and evaluation, automation, computational modelling, and artificial intelligence, is likely needed to consider the human-technology aspects of vehicle automation.
Accordingly, SHAPE-IT has recruited fifteen PhD candidates (Early Stage Researchers – ESRs), that work together to facilitate this integration of automated vehicles into complex urban traffic by performing research to support the development of transparent, cooperative, accepted, trustworthy, and safe automated vehicles. With their (and their supervisors’) different scientific background, the candidates bring different theoretical concepts and methodological approaches to the project. This interdisciplinarity of the project team offers the unique possibility for each PhD candidate to address research questions from a broad perspective – including theories and methodological approaches of other interrelated disciplines. This is the main reason why SHAPE-IT has been funded by the European Commission’s Marie Skłodowska-Curie Innovative Training Network (ITN) program that is aimed to train early state researchers in multidisciplinary aspects of research including transferable skills. With the unique scope of SHAPE-IT, including the human-vehicle perspective, considering different road-users (inside and outside of the vehicle), addressing for example trust, transparency, and safety, and including a wide range of methodological approaches, the project members can substantially contribute to the development and deployment of safe and appreciated vehicle automation in the cities of the future.
To achieve the goal of interdisciplinary research, it is necessary to provide the individual PhD candidate with a starting point, especially on the different and diverse methodological approaches of the different disciplines. The empirical, user-centred approach for the development and evaluation of innovative automated vehicle concepts is central to SHAPE- IT. This deliverable (D1.1 “Methodological Framework for Modelling and Empirical Approaches”) provides this starting point. That is, this document provides a broad overview of approaches and methodologies used and developed by the SHAPE-IT ESRs during their research. The SHAPE-IT PhD candidates, as well as other researchers and developers outside of SHAPE-IT, can use this document when searching for appropriate methodological approaches, or simply get a brief overview of research methodologies often employed in automated vehicle research.
The first chapter of the deliverable shortly describes the major methodological approaches to collect data relevant for investigating road user behaviour. Each subchapter describes one approach, ranging from naturalistic driving studies to controlled experiments in driving simulators, with the goal to provide the unfamiliar reader with a broad overview of the approach, including its scope, the type of data collected, and its limitations. Each subchapter ends with recommendations for further reading – literature that provide much more detail and examples.
The second chapter explains four different highly relevant tools for data collection, such as interviews, questionnaires, physiological measures, and as other current tools (the Wizard of Oz paradigm and Augmented and Virtual Reality). As in the first chapter this chapter provides the reader with information about advantages and disadvantages of the different tools and with proposed further readings.
The third chapter deals with computational models of human/agent interaction and presents in four subchapters different modelling approaches, ranging from models based on psychological mechanisms, rule-based and artificial intelligence models to simulation models of traffic interaction.
The fourth chapter is devoted to Requirements Engineering and the challenge of communicating knowledge (e.g., human factors) to developers of automated vehicles. When forming the SHAPE-IT proposal it was identified that there is a lack of communication of human factors knowledge about the highly technical development of automated vehicles. This is why it is highly important that the SHAPE-IT ESRs get training in requirement engineering. Regardless of the ESRs working in academia or industry after their studies it is important to learn how to communicate and disseminate the findings to engineers.
The deliverable ends with the chapter “Method Champions”. Here the expertise and association of the different PhD candidates with the different topics are made explicit to facilitate and encourage networking between PhDs with special expertise and those seeking support, especially with regards to methodological questions. ...
The progress in technology development over the past decades, both with respect to software and hardware, offers the vision of automated vehicles as means of achieving zero fatalities in traffic. However, the promises of this new technology – an increase in road safety, traffic efficiency, and user comfort – can only be realized if this technology is smoothly introduced into the existing traffic system with all its complexities, constraints, and requirements. SHAPE- IT will contribute to this major undertaking by addressing research questions relevant for the development and introduction of automated vehicles in urban traffic scenarios. Previous research has pointed out several research areas that need more attention for a successful implementation and deployment of human-centred vehicle automation in urban environments.
In SHAPE-IT, for example, a better understanding of human behaviour and the underlying psychological mechanisms will lead to improved models of human behaviour that can help to predict the effects of automated systems on human behaviour already during system development. Such models can also be integrated into the algorithms of automated vehicles, enabling them to better understand the human interaction partners’ behaviours.
Further, the development of vehicle automation is much about technology (software and hardware), but the users will be humans and they will interact with humans both inside and outside of the vehicle. To be successful in the development of automated vehicles functionalities, research must be performed on a variety of aspects. Actually, a highly interdisciplinary team of researchers, bringing together expertise and background from various scientific fields related to traffic safety, human factors, human-machine interaction design and evaluation, automation, computational modelling, and artificial intelligence, is likely needed to consider the human-technology aspects of vehicle automation.
Accordingly, SHAPE-IT has recruited fifteen PhD candidates (Early Stage Researchers – ESRs), that work together to facilitate this integration of automated vehicles into complex urban traffic by performing research to support the development of transparent, cooperative, accepted, trustworthy, and safe automated vehicles. With their (and their supervisors’) different scientific background, the candidates bring different theoretical concepts and methodological approaches to the project. This interdisciplinarity of the project team offers the unique possibility for each PhD candidate to address research questions from a broad perspective – including theories and methodological approaches of other interrelated disciplines. This is the main reason why SHAPE-IT has been funded by the European Commission’s Marie Skłodowska-Curie Innovative Training Network (ITN) program that is aimed to train early state researchers in multidisciplinary aspects of research including transferable skills. With the unique scope of SHAPE-IT, including the human-vehicle perspective, considering different road-users (inside and outside of the vehicle), addressing for example trust, transparency, and safety, and including a wide range of methodological approaches, the project members can substantially contribute to the development and deployment of safe and appreciated vehicle automation in the cities of the future.
To achieve the goal of interdisciplinary research, it is necessary to provide the individual PhD candidate with a starting point, especially on the different and diverse methodological approaches of the different disciplines. The empirical, user-centred approach for the development and evaluation of innovative automated vehicle concepts is central to SHAPE- IT. This deliverable (D1.1 “Methodological Framework for Modelling and Empirical Approaches”) provides this starting point. That is, this document provides a broad overview of approaches and methodologies used and developed by the SHAPE-IT ESRs during their research. The SHAPE-IT PhD candidates, as well as other researchers and developers outside of SHAPE-IT, can use this document when searching for appropriate methodological approaches, or simply get a brief overview of research methodologies often employed in automated vehicle research.
The first chapter of the deliverable shortly describes the major methodological approaches to collect data relevant for investigating road user behaviour. Each subchapter describes one approach, ranging from naturalistic driving studies to controlled experiments in driving simulators, with the goal to provide the unfamiliar reader with a broad overview of the approach, including its scope, the type of data collected, and its limitations. Each subchapter ends with recommendations for further reading – literature that provide much more detail and examples.
The second chapter explains four different highly relevant tools for data collection, such as interviews, questionnaires, physiological measures, and as other current tools (the Wizard of Oz paradigm and Augmented and Virtual Reality). As in the first chapter this chapter provides the reader with information about advantages and disadvantages of the different tools and with proposed further readings.
The third chapter deals with computational models of human/agent interaction and presents in four subchapters different modelling approaches, ranging from models based on psychological mechanisms, rule-based and artificial intelligence models to simulation models of traffic interaction.
The fourth chapter is devoted to Requirements Engineering and the challenge of communicating knowledge (e.g., human factors) to developers of automated vehicles. When forming the SHAPE-IT proposal it was identified that there is a lack of communication of human factors knowledge about the highly technical development of automated vehicles. This is why it is highly important that the SHAPE-IT ESRs get training in requirement engineering. Regardless of the ESRs working in academia or industry after their studies it is important to learn how to communicate and disseminate the findings to engineers.
The deliverable ends with the chapter “Method Champions”. Here the expertise and association of the different PhD candidates with the different topics are made explicit to facilitate and encourage networking between PhDs with special expertise and those seeking support, especially with regards to methodological questions.
In SHAPE-IT, for example, a better understanding of human behaviour and the underlying psychological mechanisms will lead to improved models of human behaviour that can help to predict the effects of automated systems on human behaviour already during system development. Such models can also be integrated into the algorithms of automated vehicles, enabling them to better understand the human interaction partners’ behaviours.
Further, the development of vehicle automation is much about technology (software and hardware), but the users will be humans and they will interact with humans both inside and outside of the vehicle. To be successful in the development of automated vehicles functionalities, research must be performed on a variety of aspects. Actually, a highly interdisciplinary team of researchers, bringing together expertise and background from various scientific fields related to traffic safety, human factors, human-machine interaction design and evaluation, automation, computational modelling, and artificial intelligence, is likely needed to consider the human-technology aspects of vehicle automation.
Accordingly, SHAPE-IT has recruited fifteen PhD candidates (Early Stage Researchers – ESRs), that work together to facilitate this integration of automated vehicles into complex urban traffic by performing research to support the development of transparent, cooperative, accepted, trustworthy, and safe automated vehicles. With their (and their supervisors’) different scientific background, the candidates bring different theoretical concepts and methodological approaches to the project. This interdisciplinarity of the project team offers the unique possibility for each PhD candidate to address research questions from a broad perspective – including theories and methodological approaches of other interrelated disciplines. This is the main reason why SHAPE-IT has been funded by the European Commission’s Marie Skłodowska-Curie Innovative Training Network (ITN) program that is aimed to train early state researchers in multidisciplinary aspects of research including transferable skills. With the unique scope of SHAPE-IT, including the human-vehicle perspective, considering different road-users (inside and outside of the vehicle), addressing for example trust, transparency, and safety, and including a wide range of methodological approaches, the project members can substantially contribute to the development and deployment of safe and appreciated vehicle automation in the cities of the future.
To achieve the goal of interdisciplinary research, it is necessary to provide the individual PhD candidate with a starting point, especially on the different and diverse methodological approaches of the different disciplines. The empirical, user-centred approach for the development and evaluation of innovative automated vehicle concepts is central to SHAPE- IT. This deliverable (D1.1 “Methodological Framework for Modelling and Empirical Approaches”) provides this starting point. That is, this document provides a broad overview of approaches and methodologies used and developed by the SHAPE-IT ESRs during their research. The SHAPE-IT PhD candidates, as well as other researchers and developers outside of SHAPE-IT, can use this document when searching for appropriate methodological approaches, or simply get a brief overview of research methodologies often employed in automated vehicle research.
The first chapter of the deliverable shortly describes the major methodological approaches to collect data relevant for investigating road user behaviour. Each subchapter describes one approach, ranging from naturalistic driving studies to controlled experiments in driving simulators, with the goal to provide the unfamiliar reader with a broad overview of the approach, including its scope, the type of data collected, and its limitations. Each subchapter ends with recommendations for further reading – literature that provide much more detail and examples.
The second chapter explains four different highly relevant tools for data collection, such as interviews, questionnaires, physiological measures, and as other current tools (the Wizard of Oz paradigm and Augmented and Virtual Reality). As in the first chapter this chapter provides the reader with information about advantages and disadvantages of the different tools and with proposed further readings.
The third chapter deals with computational models of human/agent interaction and presents in four subchapters different modelling approaches, ranging from models based on psychological mechanisms, rule-based and artificial intelligence models to simulation models of traffic interaction.
The fourth chapter is devoted to Requirements Engineering and the challenge of communicating knowledge (e.g., human factors) to developers of automated vehicles. When forming the SHAPE-IT proposal it was identified that there is a lack of communication of human factors knowledge about the highly technical development of automated vehicles. This is why it is highly important that the SHAPE-IT ESRs get training in requirement engineering. Regardless of the ESRs working in academia or industry after their studies it is important to learn how to communicate and disseminate the findings to engineers.
The deliverable ends with the chapter “Method Champions”. Here the expertise and association of the different PhD candidates with the different topics are made explicit to facilitate and encourage networking between PhDs with special expertise and those seeking support, especially with regards to methodological questions.
Surrogate measures of safety (SMoS) play an important role in detecting traffic conflicts and in traffic safety assessment. However, the underlying assumptions of SMoS are different and a certain SMoS may be adequate/inadequate for different applications. A comprehensive approach to evaluate the validity and applicability of SMoS is lacking in the literature. This study proposes such a framework that supports evaluating SMoS in multiple dimensions. We apply the framework to gain insights into the characteristics of six widely-used SMoS for longitudinal maneuvers, i.e., Time to Collision (TTC), single-step Probabilistic Driving Risk Field (S-PDRF), Deceleration Rate to Avoid a Crash (DRAC), Potential Index for Collision with Urgent Deceleration (PICUD), Proactive Fuzzy Surrogate Safety Metric (PFS), and the Critical Fuzzy Surrogate Safety Metric (CFS). To ensure comparability, all measures are calibrated with the same risk detection criterion. Four performance indicators, i.e., Prediction Accuracy, Timeliness, Robustness, and Efficiency are computed for all six SMoS and validated using naturalistic driving data. The strengths and weaknesses of all six measures are compared and analyzed elaborately. A key result is that not a single SMoS performs well in all performance dimensions. S-PDRF performs best in terms of Robustness but consumes the most time for computation. TTC is the most efficient but performs poorly in terms of Timeliness and Robustness. The proposed evaluation approach and the derived insights can support SMoS selection in active vehicle safety system design and traffic safety assessment.
...
Surrogate measures of safety (SMoS) play an important role in detecting traffic conflicts and in traffic safety assessment. However, the underlying assumptions of SMoS are different and a certain SMoS may be adequate/inadequate for different applications. A comprehensive approach to evaluate the validity and applicability of SMoS is lacking in the literature. This study proposes such a framework that supports evaluating SMoS in multiple dimensions. We apply the framework to gain insights into the characteristics of six widely-used SMoS for longitudinal maneuvers, i.e., Time to Collision (TTC), single-step Probabilistic Driving Risk Field (S-PDRF), Deceleration Rate to Avoid a Crash (DRAC), Potential Index for Collision with Urgent Deceleration (PICUD), Proactive Fuzzy Surrogate Safety Metric (PFS), and the Critical Fuzzy Surrogate Safety Metric (CFS). To ensure comparability, all measures are calibrated with the same risk detection criterion. Four performance indicators, i.e., Prediction Accuracy, Timeliness, Robustness, and Efficiency are computed for all six SMoS and validated using naturalistic driving data. The strengths and weaknesses of all six measures are compared and analyzed elaborately. A key result is that not a single SMoS performs well in all performance dimensions. S-PDRF performs best in terms of Robustness but consumes the most time for computation. TTC is the most efficient but performs poorly in terms of Timeliness and Robustness. The proposed evaluation approach and the derived insights can support SMoS selection in active vehicle safety system design and traffic safety assessment.
Perceived safety and trust in SAE Level 2 partially automated cars
Results from an online questionnaire
The present online study surveyed drivers of SAE Level 2 partially automated cars on automation use and attitudes towards automation. Respondents reported high levels of trust in their partially automated cars to maintain speed and distance to the car ahead (M = 4.41), and to feel safe most of the time (M = 4.22) on a scale from 1 to 5. Respondents indicated to always know when the car is in partially automated driving mode (M = 4.42), and to monitor the performance of their car most of the time (M = 4.34). A low rating was obtained for engaging in other activities while driving the partially automated car (M = 2.27). Partial automation did, however, increase reported engagement in secondary tasks that are already performed during manual driving (i.e., the proportion of respondents reporting to observe the landscape, use the phone for texting, navigation, music selection and calls, and eat during partially automated driving was higher in comparison to manual driving). Unsafe behaviour was rare with 1% of respondents indicating to rarely monitor the road, and another 1% to sleep during partially automated driving. Structural equation modeling revealed a strong, positive relationship between perceived safety and trust (β = 0.69, p = 0.001). Performance expectancy had the strongest effects on automation use, followed by driver engagement, trust, and non-driving related task engagement. Perceived safety interacted with automation use through trust. We recommend future research to evaluate the development of perceived safety and trust in time, and revisit the influence of driver engagement and non-driving related task engagement, which emerged as new constructs related to trust in partial automation.
...
The present online study surveyed drivers of SAE Level 2 partially automated cars on automation use and attitudes towards automation. Respondents reported high levels of trust in their partially automated cars to maintain speed and distance to the car ahead (M = 4.41), and to feel safe most of the time (M = 4.22) on a scale from 1 to 5. Respondents indicated to always know when the car is in partially automated driving mode (M = 4.42), and to monitor the performance of their car most of the time (M = 4.34). A low rating was obtained for engaging in other activities while driving the partially automated car (M = 2.27). Partial automation did, however, increase reported engagement in secondary tasks that are already performed during manual driving (i.e., the proportion of respondents reporting to observe the landscape, use the phone for texting, navigation, music selection and calls, and eat during partially automated driving was higher in comparison to manual driving). Unsafe behaviour was rare with 1% of respondents indicating to rarely monitor the road, and another 1% to sleep during partially automated driving. Structural equation modeling revealed a strong, positive relationship between perceived safety and trust (β = 0.69, p = 0.001). Performance expectancy had the strongest effects on automation use, followed by driver engagement, trust, and non-driving related task engagement. Perceived safety interacted with automation use through trust. We recommend future research to evaluate the development of perceived safety and trust in time, and revisit the influence of driver engagement and non-driving related task engagement, which emerged as new constructs related to trust in partial automation.
Journal article
(2020)
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Freddy A. Mullakkal-Babu, Meng Wang, Xiaolin He, Bart van Arem, Riender Happee
We present an approach to assess the risk taken by on-road vehicles within the framework of artificial field theory, envisioned for safety analysis and design of driving support/automation applications. Here, any obstacle (neighboring entity on the road) to the subject vehicle is treated as a finite scalar risk field that is formulated in the predicted configuration space of the subject vehicle. The driving risk estimate is the strength of the risk field at the subject vehicle's future location. This risk field is formulated as the product of two factors: collision probability and expected crash energy. The collision probability with neighboring vehicles is estimated based on probabilistic motion predictions. The risk can be assessed for a single time step or over multiple future time steps, depending on the required temporal resolution of the estimates. We verified the single step approach in three near-crash situations from a naturalistic dataset and in cut-in and hard-braking scenarios with simulation and showed the application of the multi-step approach in selecting the safest path in a lane-drop section. The risk descriptions from the proposed approach qualitatively reflect the narration of the situation and are in general consistent with Time To Collision. Compared to current surrogate measures of safety, the proposed risk estimate provides a better basis to assess the driving safety of an individual vehicle by considering the uncertainty over the future ambient traffic state and magnitude of expected crash consequences. The proposed driving risk model can be used as a component of intelligent vehicle safety applications and as a comprehensive surrogate measure for assessing traffic safety.
...
We present an approach to assess the risk taken by on-road vehicles within the framework of artificial field theory, envisioned for safety analysis and design of driving support/automation applications. Here, any obstacle (neighboring entity on the road) to the subject vehicle is treated as a finite scalar risk field that is formulated in the predicted configuration space of the subject vehicle. The driving risk estimate is the strength of the risk field at the subject vehicle's future location. This risk field is formulated as the product of two factors: collision probability and expected crash energy. The collision probability with neighboring vehicles is estimated based on probabilistic motion predictions. The risk can be assessed for a single time step or over multiple future time steps, depending on the required temporal resolution of the estimates. We verified the single step approach in three near-crash situations from a naturalistic dataset and in cut-in and hard-braking scenarios with simulation and showed the application of the multi-step approach in selecting the safest path in a lane-drop section. The risk descriptions from the proposed approach qualitatively reflect the narration of the situation and are in general consistent with Time To Collision. Compared to current surrogate measures of safety, the proposed risk estimate provides a better basis to assess the driving safety of an individual vehicle by considering the uncertainty over the future ambient traffic state and magnitude of expected crash consequences. The proposed driving risk model can be used as a component of intelligent vehicle safety applications and as a comprehensive surrogate measure for assessing traffic safety.