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F.A. Mullakkal-Babu
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12 records found
1
Journal article
(2021)
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Freddy Mullakkal-Babu, Meng Wang, Bart van Arem, Barys Shyrokau, Riender Happee
Current lane-based microscopic traffic simulators combine car-following and lane changing logic to describe the (often discrete) lateral vehicle motion on multi-lane road segments. However, the simulated lateral trajectories are physically unplausible and inside-lane behavior such as lane-keeping and curve negotiation cannot be modelled. In this work, we integrate lateral vehicle dynamics and yaw motion into a traffic simulation framework, aiming to describe lateral motion and vehicle interactions with more precision. The resulting framework consists of two coupled layers, an upper tactical level that plans maneuvers such as lane-changing; and a lower operational layer with a control module (steering and acceleration control) that operates in a closed loop with the bicycle model of vehicle dynamics. The feedback mechanism between the layers allows for dynamic trajectory re-planning. Unlike the microscopic traffic models, the proposed framework accounts for lateral vehicle dynamics and yaw motion; provides additional variables such as vehicle heading and front wheel steering angle; and is hence termed as submicroscopic. Case study results demonstrate the power of the framework to include lateral maneuvers such as curve negotiation, corrective steering, lane change abortion and fragmented lane changing. The framework was operationalized to model multi-lane traffic flow consisting of human-driven vehicles. At the macroscopic level, the traffic flow simulation can reproduce phenomena such as capacity drop. Thus the framework preserves the properties of the component models and at the same time describe the continuous 2-D planar movement of vehicles.
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Current lane-based microscopic traffic simulators combine car-following and lane changing logic to describe the (often discrete) lateral vehicle motion on multi-lane road segments. However, the simulated lateral trajectories are physically unplausible and inside-lane behavior such as lane-keeping and curve negotiation cannot be modelled. In this work, we integrate lateral vehicle dynamics and yaw motion into a traffic simulation framework, aiming to describe lateral motion and vehicle interactions with more precision. The resulting framework consists of two coupled layers, an upper tactical level that plans maneuvers such as lane-changing; and a lower operational layer with a control module (steering and acceleration control) that operates in a closed loop with the bicycle model of vehicle dynamics. The feedback mechanism between the layers allows for dynamic trajectory re-planning. Unlike the microscopic traffic models, the proposed framework accounts for lateral vehicle dynamics and yaw motion; provides additional variables such as vehicle heading and front wheel steering angle; and is hence termed as submicroscopic. Case study results demonstrate the power of the framework to include lateral maneuvers such as curve negotiation, corrective steering, lane change abortion and fragmented lane changing. The framework was operationalized to model multi-lane traffic flow consisting of human-driven vehicles. At the macroscopic level, the traffic flow simulation can reproduce phenomena such as capacity drop. Thus the framework preserves the properties of the component models and at the same time describe the continuous 2-D planar movement of vehicles.
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.
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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 a simulation-based approach to assess the safety impacts of vehicles equipped with Automated Driving Systems (ADS) in mixed traffic with Human-driven Vehicles (HV). Specifically, we compare two generic longitudinal strategies of ADS to handle a cut-in: Reactive ADS acting only when the cut-in vehicle crosses the target lane boundary, and Predictive ADS acting at the onset of the cut-in manoeuvre. We identify their distinctive effects on the traffic safety under cut-in maneuvers of adjacent human-driven vehicles at highway merges. We employ a microscopic traffic flow simulator that describes the lane changing process with high detail, accounting for the vehicle interaction and consequent trajectory updates. These high-resolution trajectories are post-processed to estimate a set of relevant surrogate measures of safety. By analyzing these measures, we find that the predictive ADS significantly outperforms the reactive ADS in aspects such as temporal proximity to crash, expected crash severity and the driving risk (combining the two aspects), and the number of aborted lane changes by HV. The negative safety impact of reactive ADS becomes prominent at penetration rate > 10%. The major difference between the two ADS approaches appears in the dynamics of risk during the lane changing. When a vehicle cuts in ahead of Reactive ADS, the risk peaks approximately halfway through the maneuver; whereas with Predictive ADS the risk remains marginal throughout. This work demonstrates the potential of simulation-based safety assessment to differentiate the safety impacts of automation functionalities at an early stage of product development.
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We present a simulation-based approach to assess the safety impacts of vehicles equipped with Automated Driving Systems (ADS) in mixed traffic with Human-driven Vehicles (HV). Specifically, we compare two generic longitudinal strategies of ADS to handle a cut-in: Reactive ADS acting only when the cut-in vehicle crosses the target lane boundary, and Predictive ADS acting at the onset of the cut-in manoeuvre. We identify their distinctive effects on the traffic safety under cut-in maneuvers of adjacent human-driven vehicles at highway merges. We employ a microscopic traffic flow simulator that describes the lane changing process with high detail, accounting for the vehicle interaction and consequent trajectory updates. These high-resolution trajectories are post-processed to estimate a set of relevant surrogate measures of safety. By analyzing these measures, we find that the predictive ADS significantly outperforms the reactive ADS in aspects such as temporal proximity to crash, expected crash severity and the driving risk (combining the two aspects), and the number of aborted lane changes by HV. The negative safety impact of reactive ADS becomes prominent at penetration rate > 10%. The major difference between the two ADS approaches appears in the dynamics of risk during the lane changing. When a vehicle cuts in ahead of Reactive ADS, the risk peaks approximately halfway through the maneuver; whereas with Predictive ADS the risk remains marginal throughout. This work demonstrates the potential of simulation-based safety assessment to differentiate the safety impacts of automation functionalities at an early stage of product development.
Existing microscopic traffic models represent the lane-changing maneuver as a continuous and uninterrupted lateral movement of the vehicle from its original to the target lane. We term this representation as Continuous Lane-Changing (CLC). Recent empirical studies find that not all lane-changing maneuvers are continuous; the lane-changer may pause its lateral movement during the maneuver resulting in a Fragmented Lane-Changing (FLC). We analysed a set of 1064 lane changes from NGSIM dataset which contains 270 FLCs. In comparison to a CLC, this study investigates the distinction of an FLC in terms of its execution and its effects on neighbouring vehicles. We find that during the execution of an FLC, the lane-changer exhibits distinct kinematics and takes a longer duration to complete the lane-changing. We propose a trajectory model to describe the lateral kinematics during an FLC. Additionally, we find that the FLC induces a distinct effect on the follower in the target lane, and propose a model to describe the transient behavior of the target-follower during an FLC. The modelling results suggest that the accuracy of traffic flow models can be improved by deploying lane change execution and impact models that are specific to FLC and CLC. Besides, this study identifies a set of factors that might be related to the decision-making process behind FLC: an average driver executes an FLC when the preceding and following vehicles in the target lane are slower, and when the follower in the target lane is closer than those observed during the onset of a CLC. Our findings suggest that FLC is motivated by an increased necessity to change lane such as during a mandatory lane change.
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Existing microscopic traffic models represent the lane-changing maneuver as a continuous and uninterrupted lateral movement of the vehicle from its original to the target lane. We term this representation as Continuous Lane-Changing (CLC). Recent empirical studies find that not all lane-changing maneuvers are continuous; the lane-changer may pause its lateral movement during the maneuver resulting in a Fragmented Lane-Changing (FLC). We analysed a set of 1064 lane changes from NGSIM dataset which contains 270 FLCs. In comparison to a CLC, this study investigates the distinction of an FLC in terms of its execution and its effects on neighbouring vehicles. We find that during the execution of an FLC, the lane-changer exhibits distinct kinematics and takes a longer duration to complete the lane-changing. We propose a trajectory model to describe the lateral kinematics during an FLC. Additionally, we find that the FLC induces a distinct effect on the follower in the target lane, and propose a model to describe the transient behavior of the target-follower during an FLC. The modelling results suggest that the accuracy of traffic flow models can be improved by deploying lane change execution and impact models that are specific to FLC and CLC. Besides, this study identifies a set of factors that might be related to the decision-making process behind FLC: an average driver executes an FLC when the preceding and following vehicles in the target lane are slower, and when the follower in the target lane is closer than those observed during the onset of a CLC. Our findings suggest that FLC is motivated by an increased necessity to change lane such as during a mandatory lane change.
Journal article
(2020)
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Haneen Farah, Shubham Bhusari, Paul van Gent, Freddy Mullakkal-Babu, Peter Morsink, Riender Happee, Bart van Arem
Lower levels of automation are designed to work in specific conditions referred to as the Operational Design Domain (ODD). Beyond these conditions, the human driver is expected to take control. A mismatch between a driver's understanding and expectations of the automated vehicle capabilities and its actual capabilities as prescribed in the Original Equipment Manufacturers (OEMs) manual, could affect their safety and trust in automation. The main aim of this study is to develop a method for assessing the ODD of lane keeping system equipped vehicles. The analysis method is composed of an objective driving risk measure based on the Probabilistic Driving Risk Field (PDRF), and a subjective risk measure based on driver behavior, trust and situation awareness. We demonstrate the method applicability using the Automated Lane Keeping system of the Tesla Model S. A field test was conducted with 19 participants on public roads in the Netherlands including situations within and outside the defined ODD by the OEM. Across all test situations, a mismatch was observed between the ODD specified by the OEM and by the driver. Situations outside the ODD (i.e. no-lane markings and on/off-ramp) were often regarded as within the ODD by the participants. Situations inside the ODD (i.e. tunnel and curve) were mostly correctly classified by the participants. This analysis method has the potential to aid OEMs and road operators in defining more clearly the ODD while taking into account the driver's safety and awareness of the system capabilities.
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Lower levels of automation are designed to work in specific conditions referred to as the Operational Design Domain (ODD). Beyond these conditions, the human driver is expected to take control. A mismatch between a driver's understanding and expectations of the automated vehicle capabilities and its actual capabilities as prescribed in the Original Equipment Manufacturers (OEMs) manual, could affect their safety and trust in automation. The main aim of this study is to develop a method for assessing the ODD of lane keeping system equipped vehicles. The analysis method is composed of an objective driving risk measure based on the Probabilistic Driving Risk Field (PDRF), and a subjective risk measure based on driver behavior, trust and situation awareness. We demonstrate the method applicability using the Automated Lane Keeping system of the Tesla Model S. A field test was conducted with 19 participants on public roads in the Netherlands including situations within and outside the defined ODD by the OEM. Across all test situations, a mismatch was observed between the ODD specified by the OEM and by the driver. Situations outside the ODD (i.e. no-lane markings and on/off-ramp) were often regarded as within the ODD by the participants. Situations inside the ODD (i.e. tunnel and curve) were mostly correctly classified by the participants. This analysis method has the potential to aid OEMs and road operators in defining more clearly the ODD while taking into account the driver's safety and awareness of the system capabilities.
The past three decades have witnessed the emergence of several automotive applications that take over the task of vehicle driving on a sustained basis. The most advanced class of such applications is known as Automated Driving Systems (ADSs). ADS can autonomously operate the vehicle on road stretches that fall under its operational design domain. Industry and governments expect that such systems will be technologically feasible shortly and the traffic will be mixed with system-driven and human-driven vehicles. Even though ADSequipped vehicles will have an impact on traffic safety, there is no clarity on if they would enhance or detriment traffic safety and at what conditions and magnitude. A human and an ADS apply fundamentally different processes to acquire information, make decisions, and operate the vehicle. Therefore, our current insights on the relationship between driving behaviour and safety may not be sufficient to predict the possible impacts of ADS systems. Hence there is an urgent need to study the impacts of ADS functionalities and design factors on traffic safety.
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The past three decades have witnessed the emergence of several automotive applications that take over the task of vehicle driving on a sustained basis. The most advanced class of such applications is known as Automated Driving Systems (ADSs). ADS can autonomously operate the vehicle on road stretches that fall under its operational design domain. Industry and governments expect that such systems will be technologically feasible shortly and the traffic will be mixed with system-driven and human-driven vehicles. Even though ADSequipped vehicles will have an impact on traffic safety, there is no clarity on if they would enhance or detriment traffic safety and at what conditions and magnitude. A human and an ADS apply fundamentally different processes to acquire information, make decisions, and operate the vehicle. Therefore, our current insights on the relationship between driving behaviour and safety may not be sufficient to predict the possible impacts of ADS systems. Hence there is an urgent need to study the impacts of ADS functionalities and design factors on traffic safety.
Driver mental workload is an important factor in the operational safety of automated driving. In this study, workload was evaluated subjectively (NASA R-TLX) and objectively (auditory detection-response task) on Dutch public highways (∼150 km) comparing manual and supervised automated driving in a Tesla Model S with moderators automation experience and traffic complexity. Participants (N = 16) were either automation-inexperienced drivers or automation-experienced Tesla owners. Complexity ranged from an engaging environment with a road geometry stimulating continuous traffic interaction, and a monotonic environment with lower traffic density and a simple road geometry. Perceived and objective workload increased with traffic complexity. When using the automation, automation-experienced drivers perceived a lower workload, while automation-inexperienced drivers perceived their workload to be similar to manual driving. However, the detection-response task indicated an increase in cognitive load with automation, in particular in complex traffic. This indicates that drivers under-estimate the actual task load of attentive monitoring. The findings also highlight the relevance of using system-experienced participants and the importance of incorporating both objective and subjective measures when examining workload.
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Driver mental workload is an important factor in the operational safety of automated driving. In this study, workload was evaluated subjectively (NASA R-TLX) and objectively (auditory detection-response task) on Dutch public highways (∼150 km) comparing manual and supervised automated driving in a Tesla Model S with moderators automation experience and traffic complexity. Participants (N = 16) were either automation-inexperienced drivers or automation-experienced Tesla owners. Complexity ranged from an engaging environment with a road geometry stimulating continuous traffic interaction, and a monotonic environment with lower traffic density and a simple road geometry. Perceived and objective workload increased with traffic complexity. When using the automation, automation-experienced drivers perceived a lower workload, while automation-inexperienced drivers perceived their workload to be similar to manual driving. However, the detection-response task indicated an increase in cognitive load with automation, in particular in complex traffic. This indicates that drivers under-estimate the actual task load of attentive monitoring. The findings also highlight the relevance of using system-experienced participants and the importance of incorporating both objective and subjective measures when examining workload.
Conference paper
(2017)
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Freddy Mullakkal Babu, Meng Wang, Haneen Farah, Bart van Arem, Riender Happee
Safety measurement and analysis have been a challenging and well-researched topic in transportation. Conventionally, surrogate safety measures have been used as safety indicators in simulation models for safety assessment, in control formulations for driver assistance systems, and in data analysis of naturalistic driving studies. However, surrogate indicators only give partial insights into traffic safety i.e., they only indicate a predetermined set of possible pre-crash situations for an interacting vehicle pair. Recently, a safety indicator called the driving safety field based on field theory has been proposed for two-dimensional vehicle interactions. However, the objectivity of its functional form and validity are yet to be tested. This paper provides a qualitative and quantitative comparison of different safety indicators as a risk measure to demarcate their mathematical properties and evaluate their usefulness in quantifying trajectory risk. We compare five relevant safety indicators: inverse time to collision, post-encroachment time, potential indicator of collision with urgent decceleration, warning index and safety field strength. Their formulations are mathematically analyzed to yield qualitative insights and their values over simulated vehicle trajectories are evaluated to yield quantitative insights. Our results acknowledge the limitations and demarcate the functional utilities of the selected safety indicators.
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Safety measurement and analysis have been a challenging and well-researched topic in transportation. Conventionally, surrogate safety measures have been used as safety indicators in simulation models for safety assessment, in control formulations for driver assistance systems, and in data analysis of naturalistic driving studies. However, surrogate indicators only give partial insights into traffic safety i.e., they only indicate a predetermined set of possible pre-crash situations for an interacting vehicle pair. Recently, a safety indicator called the driving safety field based on field theory has been proposed for two-dimensional vehicle interactions. However, the objectivity of its functional form and validity are yet to be tested. This paper provides a qualitative and quantitative comparison of different safety indicators as a risk measure to demarcate their mathematical properties and evaluate their usefulness in quantifying trajectory risk. We compare five relevant safety indicators: inverse time to collision, post-encroachment time, potential indicator of collision with urgent decceleration, warning index and safety field strength. Their formulations are mathematically analyzed to yield qualitative insights and their values over simulated vehicle trajectories are evaluated to yield quantitative insights. Our results acknowledge the limitations and demarcate the functional utilities of the selected safety indicators.
Journal article
(2017)
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Freddy Antony Mullakkal-Babu, Meng Wang, Haneen Farah, Bart van Arem, Riender Happee
Safety measurement and its analysis have been challenging and well-researched topics in transportation. Conventionally, surrogate safety measures have been used as safety indicators in simulation models for safety assessment, in control formulations for driver assistance systems, and in data analysis of naturalistic driving studies. However, surrogate indicators give partial insights on traffic safety; that is, these indicators only indicate a predetermined set of possible precrash situations for an interacting vehicle pair. Recently, a safety indicator called the “driving safety field,” based on field theory, was proposed for two-dimensional vehicle interactions. However, the objectivity of its functional form and its validity have yet to be tested. A qualitative and quantitative comparison of different safety indicators was provided as a risk measure to demarcate their mathematical properties and evaluate their usefulness in quantifying trajectory risk. Five relevant safety indicators were compared: inverse time to collision, postencroachment time, potential indicator of collision with urgent decceleration, warning index, and safety field force. Their formulations were mathematically analyzed to yield qualitative insights and their values over simulated vehicle trajectories were evaluated to yield quantitative insights. The results acknowledge the limitations and demarcate the functional utilities of the selected safety indicators.
...
Safety measurement and its analysis have been challenging and well-researched topics in transportation. Conventionally, surrogate safety measures have been used as safety indicators in simulation models for safety assessment, in control formulations for driver assistance systems, and in data analysis of naturalistic driving studies. However, surrogate indicators give partial insights on traffic safety; that is, these indicators only indicate a predetermined set of possible precrash situations for an interacting vehicle pair. Recently, a safety indicator called the “driving safety field,” based on field theory, was proposed for two-dimensional vehicle interactions. However, the objectivity of its functional form and its validity have yet to be tested. A qualitative and quantitative comparison of different safety indicators was provided as a risk measure to demarcate their mathematical properties and evaluate their usefulness in quantifying trajectory risk. Five relevant safety indicators were compared: inverse time to collision, postencroachment time, potential indicator of collision with urgent decceleration, warning index, and safety field force. Their formulations were mathematically analyzed to yield qualitative insights and their values over simulated vehicle trajectories were evaluated to yield quantitative insights. The results acknowledge the limitations and demarcate the functional utilities of the selected safety indicators.
Driver mental underload is an important concern in the operational safety of automated driving. In this study, workload was evaluated subjectively (NASA RTLX) and objectively (auditory detection-response task) on Dutch public highways (~150km) in a Tesla Model S comparing manual and supervised automated driving with moderators automation experience and traffic complexity. Participants (N=16) were either automationinexperienced drivers or automation-experienced Tesla owners. Complexity ranged from an engaging environment with a road geometry stimulating continuous traffic interaction, and a monotonic environment with lower traffic density and a simple road geometry. Perceived and objective workload increased with traffic complexity. Automation use reduced perceived workload in both environments for automation-experienced drivers, but not for inexperienced drivers. However, the DRT did not reveal a reduced attentional demand with automation. This suggests that attentive monitoring requires a similar attentional demand as manual driving. The findings highlight the relevance of using system-experienced participants and the relevance of on-road testing for behavioral validity.
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Driver mental underload is an important concern in the operational safety of automated driving. In this study, workload was evaluated subjectively (NASA RTLX) and objectively (auditory detection-response task) on Dutch public highways (~150km) in a Tesla Model S comparing manual and supervised automated driving with moderators automation experience and traffic complexity. Participants (N=16) were either automationinexperienced drivers or automation-experienced Tesla owners. Complexity ranged from an engaging environment with a road geometry stimulating continuous traffic interaction, and a monotonic environment with lower traffic density and a simple road geometry. Perceived and objective workload increased with traffic complexity. Automation use reduced perceived workload in both environments for automation-experienced drivers, but not for inexperienced drivers. However, the DRT did not reveal a reduced attentional demand with automation. This suggests that attentive monitoring requires a similar attentional demand as manual driving. The findings highlight the relevance of using system-experienced participants and the relevance of on-road testing for behavioral validity.
Current Full Range Adaptive Cruise Control (FRACC) systems switch between separate adaptive cruise control and collision avoidance systems. This can lead to jerky responses and discomfort during the transition between the two control modes. We propose a Full Range Adaptive Cruise Control (FRACC) design integrating adaptive cruise control and collision avoidance into a single non-linear mathematical formulation. The proposed FRACC responds to a velocityerror using a sigmoidal function of forward spacing. Mathematical properties of the controller, in particular string stability, are examined. Simulation experiments demonstrate that the controller yields smooth and safe responses in typical highway scenarios, including hard-braking and cut-in scenarios. Results also show a clear advantage of the proposed controller in string stability performance with reference to a state-of-The-Art controller.
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Current Full Range Adaptive Cruise Control (FRACC) systems switch between separate adaptive cruise control and collision avoidance systems. This can lead to jerky responses and discomfort during the transition between the two control modes. We propose a Full Range Adaptive Cruise Control (FRACC) design integrating adaptive cruise control and collision avoidance into a single non-linear mathematical formulation. The proposed FRACC responds to a velocityerror using a sigmoidal function of forward spacing. Mathematical properties of the controller, in particular string stability, are examined. Simulation experiments demonstrate that the controller yields smooth and safe responses in typical highway scenarios, including hard-braking and cut-in scenarios. Results also show a clear advantage of the proposed controller in string stability performance with reference to a state-of-The-Art controller.