Road-induced vibrations dissipate energy through the bicycle structure and rider’s body beyond what classical rolling resistance predicts. To measure this effect a laboratory apparatus is used that reproduces realistic road surface characteristics. This thesis presents the design, construction, and validation of such an apparatus and characterises the powerloss on a 3D-printed klinker road surface across six speeds (5–30 km/h) and five tire pressures (3.5–5.5 bar).
The setup measures the total resistive force acting on an athlete and racing bicycle using two loadcells: a main loadcell and an interface loadcell that captures the longitudinal force exerted by a two-bar linkage stabilization mechanism, isolated from the vertical load by a parallelogram flexure. Multiplying this force by the belt speed gives the powerloss. The force is measured with an expanded uncertainty of ±0.41 N at the 95% confidence level, corresponding to a powerloss uncertainty of at most 3.4 W at 30 km/h.
Powerloss increases approximately linearly with speed for all pressures, ranging from about 30 W at 5 km/h to 234 W at 30 km/h. Much of this loss is not classical rolling resistance: at 30 km/h the classical term accounts for only about 82 W, so roughly 64% of the total powerloss is attributed to vibrational losses. Tire pressure has a smaller effect: depending on the speed setpoint, an optimal tire pressure can be identified, although at lower speeds the error bars overlap and no clear optimum can be determined. The near-linear speed dependence shows that although the vibrational losses are large, their speed-growing quadratic component remains weak on the relatively smooth klinker surface (BRI ≈ 40). A stiffer underlayer and a rougher surface are recommended before the apparatus can be used to quantify vibration transmission from the road surface through the bicycle to the rider’s body. ... Read more

The development of automated vehicles is progressing rapidly. This comes with numerous benefits, including increased safety, accessibility, and productivity, as well as reduced emissions and traffic. However, many challenges remain. One of the drawbacks is increased susceptibility to motion sickness due to the lack of control and the inability to anticipate motion as a passenger.

Motion sickness is a complex phenomenon generally believed to be influenced by sensory conflict; however, it is increasingly recognized as involving postural stability and head-neck stabilization. Despite extensive research on motion sickness, many studies examine motion perception and postural stability in isolation, without considering their interaction. Furthermore, while it is known that sensory reliance and motor strategies are individually dependent, this is rarely incorporated in existing models.

In this thesis, these gaps will be addressed by employing an experimental approach to investigate the relationships among individual differences in rotational motion perception, postural stability, and sensitivity to motion sickness. This is achieved by performing a structured experiment in which participants are exposed to dynamic motion while their postural sway, motion perception, dynamic head stabilization, and susceptibility to motion sickness are examined.

A significant positive correlation was found between the velocity storage time constant and motion sickness severity (ρ = 0.528, p = 0.012), suggesting that the EVAR paradigm may serve as a prescreening method for motion sickness susceptibility. No significant correlation was found between motion sickness severity and postural sway or dynamic stabilization, suggesting that these measures may not be appropriate to screen individuals for motion sickness susceptibility. ... Read more

With the advancements in automated driving, there is an increasing focus on passenger comfort and safety, fueled by the desire to establish a unique user experience identity among automotive companies. This study investigates the potential of the Galvanic Skin Response (GSR) as a physiological marker for assessing user experience. For this purpose, a test study of 32 participants was performed by collecting GSR measurements and self-reported comfort and safety scores, using a Wizard-ofOz setup on a closed test-track over repeated laps, alternating between distinct driving styles. Statistical analysis revealed the phasic maximum amplitude and peak count as the features most strongly correlating with both objective driving style and perceived comfort and safety ratings. The GSR measurements were also given as input for a predictive model for classifying the driving style, yielding an accuracy of 88.61%. General performance of the same model for perceived comfort and safety prediction on a five-point Likert scale was, however, notably lower, whereas participant-specific model calibration yielded substantially higher performance. This indicates that while the GSR consistently reflects physiological responses linked to perceived comfort and safety, the signal exhibits a strong inter-subject variability, highlighting the necessity of personalized calibration for accurate passenger-experience assessment. ... Read more

This project aims to provide insights and a better understanding of power losses in cycling, with a particular focus on professional road cycling and the vibration losses experienced during rough road stages such as Paris–Roubaix. While aerodynamic drag and pure rolling resistance are well-documented, vibrational losses, caused by the excitation of the bicycle-rider system over uneven roads, are often overlooked or inadequately modeled. This thesis investigates vibration-induced losses up to speeds of 30 km/h using a controlled coast-down experiment over a simulated cobblestone surface, isolating their contribution from aerodynamic and classical rolling resistance forces.
The results show that vibration losses can reach up to 350 W and account for as much as 60% of total power losses under certain conditions. A clear quadratic trend with respect to speed was identified, challenging the conventional assumption that such losses can be included in the speed-independent rolling resistance term. Posture and tire pressure were also found to significantly influence these losses: riding out of the saddle consistently reduced vibration losses, while higher tire pressures led to greater total power loss due to increased vibrational dissipation. In contrast, rider behaviour, defined by varying levels of muscle contraction, did not yield consistent trends under the current experimental results.
To describe and better understand these dynamics, a simple empirical model was proposed, incorporating a quadratic dependence on speed, a linear dependence on pressure and a posture-dependent scaling factor. While intentionally limited in complexity, the model fits the observed data well and captures key interactions between the main influencing variables. These findings support the need to model vibration losses as a separate, speed-dependent component of total resistance. They also offer a useful starting point for future research focused on refining resistance models and enhancing energy efficiency in cycling. ... Read more

With the increasing integration of Automated Vehicles (AVs) into our daily traffic, validating their performance poses a significant challenge. Virtual testing, where simulated AVs operate in a simulated environment, has become a widely adopted approach for efficient and cost-effective validation. However, the lack of realistic human behaviour models hinders the realism of these simulations, particularly in simulating reciprocal driver interactions. In this research, I introduce a discretionary lane change model based on the Communication-Enabled Interaction (CEI) framework, which simulates reciprocal driver interactions through implicit communication and belief modelling. These reciprocal interactions involve mutual behaviours, where individual drivers contribute through high-level decisions and low-level control actions. By employing the CEI framework, decision-making and control actions are integrated into a unified model. The proposed model is validated against naturalistic driving data in discretionary lane change scenarios to assess its validity. Results demonstrate that the model successfully reproduces qualitative and quantitative characteristics of human driving behaviour, reflecting both individual behaviours and the collective contributions of multiple drivers. Moreover, it reflects how varied tactical decisions yield distinct, human-like operational execution characteristics. Thereby improving the realism of interactive traffic simulations and posing a step towards improving virtual testing environments for AVs. ... Read more

This study investigates how deviations in avatar motion influence user motion in virtual reality (VR), specifically focusing on upper body and trunk motion in a virtual environment (VE). Previous research showed that user motion can be altered via the avatar follower effect, in which avatar deviations are followed by users. This is the first work exploring this effect for a deviation including the head. The primary objective was to explore whether these deviations could subconsciously guide user motions, potentially contributing to real-time motion sickness reduction in automated vehicles.
The experiment involved participants performing seated lateral leaning tasks where they were instructed to touch virtual goals with their heads. During some trials, the avatar unexpectedly deviated from the user's intended motion.
The results revealed that contrary to expectations, the avatar follower effect did not occur. Instead, an opposing effect was observed where participants' motions contradicted the avatar's deviations, particularly when the avatar stopped prior to the instructed goal. This effect was not influenced by the user's perspective (first or third person) or the scoring mechanism used in the game. However, individual personality traits, such as a tendency for autonomy or a focus on rewards, did affect the strength of the opposing effect.
These findings suggest that using avatar deviations to guide upper body and head motion in VR may not be effective, thus unsuited for applications such as motion sickness prevention in automated vehicles. ... Read more

Model matching control allows for a virtual change in the vehicle's physical parameters, removing the need to alter the system physically. These vehicles are used as a versatile test bed for safe and repeatable experiments, and allow for safer vehicle operation by virtually keeping the driving behaviour constant in the face of physical changes such as wet roads. Although already applied on planes and cars, it has not been applied to bicycles. The proposed control method accomplishes model matching control by equating the state derivative of the physical system to that of the the virtual system. Theoretical analysis shows model matching control is applicable to bicycle dynamics, even if only the steer torque is available for control. Via experiments, I identify the bicycle's weave mode eigenvalue at different speeds, and the Bode gain between steer torque to lean rate and to fork angle for frequencies between one and three Hz. The experimentally identified values follow the theoretically predicted values closely but do not overlap perfectly. The effect of artifacts during the experiment and unmodelled dynamics are analysed with the use of simulations and are able to explain these perceived differences. From the results, this research concludes that model matching control is applicable to bicycles and is able to virtually change the bicycle's physical parameters. Future work will look into making the controller more robust to modelling errors. ... Read more

Bicycles have been studied extensively over the past 200 years, with mathematical models providing valuable insights into various aspects of bicycle dynamics and rider control. However, the lack of a common framework for creating and sharing bicycle-rider models hinders the development of advanced models, research reproducibility, and dissemination. This thesis addresses this gap by introducing BRiM: an open-source modular and extensible framework for creating Bicycle-Rider Models.

The modular setup of BRiM relies on a systematic approach to define a model and form the analytical equations of motion. For the involved analytical computations BRiM utilizes SymPy, a Computer Algebra System. The systematic approach consists of four stages. The first stage defines the objects in the system, such as symbols and bodies. Secondly, the kinematic relationships between the objects, such as angular velocities between reference frames, are established. The third and the fourth stages, which are order-independent, specify the loads and constraints acting upon the system. The decoupling BRiM required to achieve modularity is enabled through this systematic approach, because computations within a stage are mostly order-independent.

The core of BRiM employs the systematic approach within a unified framework for modeling mechanical systems in general. It describes a model using a tree representation, in which a model is defined as an aggregation of smaller submodels. The relationships between submodels are established by parent models, using interchangeable connections to accommodate complex relations, such as tyre models between the ground and a wheel. This application of submodels enables swapping and adding submodels, making the overarching model both modular and extensible. Actuation within BRiM can either be specified by attaching prespecified groups of loads to models and connections, or by utilizing the interface provided by the mechanics module in SymPy, which offers the flexibility to even manipulate equations in detail.

BRiM applies this generalized framework to create modular bicycle-rider models. Both a stationary bicycle and a modular bicycle based on Moore's convention of the Carvallo-Whipple bicycle have been constructed. These bicycle models are extensible to bicycle-rider models by including an upper and/or lower body. Within the rider models each joint can be actuated by a linear torsional spring-damper. BRiM integrates parametrization of models, which provides mappings between symbolic quantities used in equations and experimentally determined values, using the existing open-source BicycleParameters library. Additionally, SymMePlot, a visualization package for symbolically defined mechanical systems, has been developed and integrated within BRiM to visualize the created bicycle-rider models.

The effectiveness of BRiM is demonstrated through optimization and simulation tasks. Firstly, a real-time forward simulation of a torque-driven upper body bicycle-rider is performed. Secondly, an optimization problem is solved, involving the tracking of a rolling disc along a sinusoidal trajectory while minimizing the control torques. These demonstrations highlight the seamless integration of BRiM with other scientific tools and BRiM's potential for practical applications.

In conclusion, BRiM fills the gap in bicycle dynamics research by providing a modular and extensible framework for creating and sharing bicycle-rider models. Its systematic approach, unified framework, and integration capabilities enable efficient model development, research reproducibility, and further advancement in bicycle research.
... Read more

Commercially available Lane Keeping Assist systems fail to consider the driver's intentions since they mainly focus on minimising path tracking errors, resulting in conflicts between humans and automation. This often leads to users being unsatisfactory and turning off the assist, as a result diminishing the advantages such as reduced workload and increased road safety. Considering a driver model in the assist helps increase user acceptance. Therefore, we propose a torque-based hybrid controller for a human-centric haptic shared Lane Keeping Assist, pairing a data-driven driver model with a model-based controller to foster the collaboration between the driver and assist. First, the driver's non-linear steering wheel torque behaviour is modelled and predicted using a Bidirectional Long Short-Term Memory network with an accuracy ≥72.4% and a smoothness ≥0.85Nm/s over a 0.4s prediction horizon. Second, a Model Predictive Controller with a linear bicycle and steering model is developed, where it utilises the driver model's predictions as a time-varying reference. We developed three human-centric controllers for comparison and used a state-of-the-art commercial solution as the baseline controller. The experiments were performed in Toyota Motor Europe's fixed-base driving simulator, where 15 participants tested and evaluated the four controllers. The results show a 113.1% increase in collaborative ratio while maintaining a similar path tracking performance compared to the baseline. ... Read more

This thesis describes the concept of a reconfigurable wheelbase for a car as a way to improve agility at higher velocities. Altering the longitudinal position of each wheel with respect to the centre of gravity, leads to a change in lateral wheel forces. This affects the car's yaw rate, therefore making it possible to influence its agility. Available literature has shown that this subject is still quite weakly investigated, which makes it an interesting topic of research. To investigate this influence of actively reconfiguring the wheelbase, simulations were done using IPG Carmaker together with MATLAB/Simulink, during which a simplified vehicle model of a Toyota Camry was used. A proportional derivative controller combined with control allocation was designed to regulate the reconfigurable wheelbase system. It features a reference generator that returns a desired yaw rate and activation logic which ensures that the controller is only activated in a velocity range of 60 km/h to 110 km/h and when a steering input is given. Furthermore, a benchmark study is designed, using a fuzzy logic controller that is based on a simplified version of one presented by Soltani et al. (2017). This controller uses the same reference generator and activation logic for consistency. The rules and membership functions of the fuzzy logic block are adopted from the mentioned paper. The performance of both these controllers and a baseline vehicle without any control, are evaluated for a step steer manoeuvre, an increasing circle manoeuvre and a double lane change, at different velocities and/or steering amplitudes. Based on the results of these tests, it is shown that both controllers are able to influence the lateral dynamics positively during most of these manoeuvres and are therefore able to improve the vehicle's agility in the velocity range in which the controller is active. ... Read more

Up to now, not much is known about how humans control bicycles, especially when subject to large perturbations. In order to learn more about the extent to which these perturbations can be handled, a new experimental setup is required, which can deliver large perturbations to cyclists and bring them to fall. This thesis describes the detailed design and evaluation of such a setup. Several requirements are formulated regarding different experiment aspects that the setup must adhere to. The proposed design consists of a treadmill on which a subject rides a bicycle. Ropes are guided from the ends of the bicycle’s handlebar toward the front and back of the setup, where they are attached to four motor units of a robotic rope-pulling system. Based on a feed-forward conversion added with PI force feedback control, a shared controller commands the motors to maintain a tracking force or perturb the cyclist by applying a net torque on the handlebar for a short time. An active safety harness is the main feature that prevents the subject from harm. Meanwhile, motion capture recordings of strategically placed passive markers and data from inertial measurement units on the bicycle are collected. With appropriate processing, the angles and angular velocities that describe the dynamics and control of the bicycle-rider system can be obtained from these measurements. The force data is also collected, by the controller. Interesting results that can be obtained with this setup include the probabilities to fall after perturbations of variable forces and the data required to evaluate the equations of motions of the bicycle-rider system during and shortly after a perturbation. This can be used to provide a baseline against which future bicycles can be compared and it is useful for validating rider models. The experiment shows consistent performance and generates high quality measurements. Neither the 12 pilot participants nor the 26 subjects who participated in follow-up experiments encountered any safety issues. The experiment could even be improved by resolving issues regarding the motor behaviour and CPU overloads. The workload of the experiment operators could be decreased if intervention of the safety harness was trained on cycling and coupled to deactivation of the treadmill. A next step would be to automate the data processing by developing a classifier that distinguishes recoveries from falls. ... Read more

Although the capabilities of automated driving systems (ADS) are growing, the human operator remains in charge of driving the vehicle outside the operational design domain and after requests to intervene. The vehicle sensors required for ADS and new regulation for driver availability monitoring systems stimulate the development of systems that can monitor human driving ability. Such systems could allow for ADS to track possible driving skill degradation, monitor driver impairment, or adapt to the driver’s needs. However, knowledge is required about how to assess human driving ability effectively. This work proposes a novel method to capture driving skill and style based on curve driving, straight-road driving, and driving through road narrowings. In addition to conventional measures, the study introduces new measures, including the relationship between steering angle and eye movements. Employing a high-fidelity simulator, we compared the vehicle-control-related measures (i.e., driving skill measures), trajectory-planning and speed-related measures (i.e., driving style measures), and eye movements of sixteen inexperienced and thirteen experienced drivers. It was examined which of these measures are valid, i.e., allow a statistical discrimination between experienced and inexperienced drivers. The study was complemented with two expert drivers serving as a benchmark. The results showed that the experienced drivers adopted a more abrupt braking style when approaching curves and a higher speed through the different track sections than the inexperienced drivers. No statistically significant differences were observed between the skill measures of the experienced and inexperienced drivers. An analysis of lead times obtained through a cross-correlation between horizontal gaze angle and steering angle showed that eye movements generally preceded steering movements. However, differences in lead times between experienced and inexperienced drivers were not statistically significant, which may have been caused by eye-tracking measurement inaccuracies, the layout of the driving task and strong intra-individual variability in looking behavior. The eye-tracking results of the road-narrowings showed that the experienced and inexperienced drivers reduced their vertical gaze dispersion, while only the experienced drivers statistically significantly reduced their horizontal gaze dispersion compared to the straights. In other words, the experienced drivers showed increased horizontal gaze tunneling from the straights to the narrowings, while the inexperienced drivers only showed vertical gaze tunneling. The results of the expert drivers showed consistent higher speed, lower control activity, and more of a racing line through the curves than the experienced and inexperienced drivers. Furthermore, the results showed that the expert drivers adopted a more variable horizontal gaze strategy between different curves. Overall, these results indicate that driving style and eye-movement measures, but not driving skill measures, allow differentiating between experienced and inexperienced drivers using a driving simulator. These findings may be explained by the fact that driving is a self-paced task, i.e., more competent drivers increase their own task demands by driving faster. Future research could examine how strongly scores the present driving and eye-movement measures correlate with drivers’ take-over quality in automated driving scenarios. ... Read more

Naturalistic driving research with a focus on trucks has been gaining momentum in the past decade. With the advancement in sensor technology and access to big data, it becomes possible to understand driver behaviour at a more fundamental level. This can assist in mitigating the impact trucks have on the environment while enhancing safety. Several studies have worked towards examining the predictability of driving behaviour through driver profiling (i.e. scoring a driver's behaviour or classifying drivers by assigning them different categories such as aggressive/non-aggressive). However, little research still focuses on the importance and impact of individual features used to develop these models. In the current study, an analysis of driving data from 1,727 trucks recorded over one year as part of a Dutch Field Operational Test (FOT) has been performed. This FOT, to date, has not been investigated in the published academic literature. Recent studies have analysed historical location data to assess risk associated with specific routes and environments. This is being used to provide notifications to drivers around work zones to mitigate the impact of accidents.

The current thesis extends the geo-specific analysis of (truck) driving data by analysing stability in truck driving behaviour with a focus on time and location (urban areas and motorways). Here correlation analysis has been used to explore stability. Correlational analysis elucidates that metrics such as the number of headway warnings, braking events and lane departure warnings are stable over space and time. A discussion reflects on the role of vehicle characteristics (i.e. mass and engine power) towards stability.

It is concluded that in the case of spatial stability: Mean point speed has higher stability on motorways than in urban areas. It has been determined that trucks with higher mass and lower engine power tend to have lower mean speed than the norm. Contrary to mean point speed, headway warnings show higher stability in urban areas than on motorways. Braking events and lane departure warnings exhibit high stability. Secondly, a strong correlation between (t and t+1) hours over the entire day is observed for temporal stability.

This research is a precursor to building generalised models for profiling drivers and assessing various driving patterns. An in-depth understanding of different driving patterns can help driver coaching companies better understand metrics when time and location are factored in before providing targeted feedback. Apart from this can also facilitate fleet management. The code for analysing this dataset is accessible online and may stimulate future researchers to explore this dataset further. ... Read more

This thesis presents a new MPC controller which integrates path tracking and stability control into one controller. Previously these tasks were done by separate controllers, where one controller handled the path tracking while another controller ensured the vehicle was kept in the stable operating region. A drawback of this method is that the controllers have opposing objectives. The path tracker could require a higher steering wheel angle to follow the path, while the Vehicle Stability Controller (VSC) might require a lower angle to keep the vehicle stable. By integrating these two controllers into one controller, the new controller is able to take both tasks into account and optimise the control output such that both objectives are satisfied. This is achieved by implementing two extra yaw rates into the MPC model. These are the expected yaw rates based on the steering wheel angle and lateral acceleration of the vehicle. By comparing these two yaw rates to the actual yaw rate, the stability of the vehicle can be determined. The MPC controller is then able to prioritise path tracking or vehicle stability. This is achieved by actively varying the weights in the cost function depending on the vehicle state. To compare the new MPC controller, 8 benchmark controllers have been created. These controllers can be divided into two groups of four controllers. The first group is able to use differential braking in the control output, while the second group can only output an equal brake torque for all wheels. The benchmark controllers use different methods for path tracking and stability control, to get an understanding of the performance benefits of each method.
These different methods include: adding an extra target yaw rate based on path curvature and speed for tracking, adding constraints to ensure vehicle stability and using a separate stability controller to stabilise the vehicle. All controllers are evaluated using the industry standard Moose test as well as a double lane change in simulations. These manoeuvres are used in industry to evaluate stability and can also be used to evaluate path tracking. Furthermore the robustness of the controllers was evaluated by changing various parameters. These variations include: changing vehicle speed, adding extra weight to the vehicle, lowering the road 𝜇 level and performing a lane change where each lane has a different 𝜇 level. The results were evaluated using objective Key Performance Indicators regarding tracking performance and vehicle stability. The results show that the new MPC controller with the combined path tracking and stability control improves performance in both objectives. The new controller improves path tracking by 8% compared to the pure path tracking controller. While the stability is improved by 11% compared to the controller with a separate VSC. Furthermore the new controller was able to keep the vehicle stable at higher speeds and was more robust to varying conditions. ... Read more

Higher levels of automation in driving may allow drivers to engage in other activities, but may also increase the likelihood of Motion Sickness (MS). The exact causes of MS are not well understood, and various susceptibility factors(e.g. age, gender, ethnicity) can cause large individual differences. To better understand and predict MS, it is ideally studied in a safe and controlled environment, such as a driving simulator. However, the validity of driving simulator studies on MS as a proxy for on-road studies with real vehicles has not been properly evaluated. We conducted an experiment where the temporal aspects and symptom profiles of MS in a real-road driving scenario are compared to Simulator Sickness (SS) in a reproduction of this scenario in a motion-base driving simulator. A cohort of 25 participants was exposed to both the car and the simulator conditions. The scenarios consisted of sections of provocative(slaloming, stop-and-go) and normal driving. Sickening stimuli of the simulator were similar to the car accelerations in design (r = 0.51) but different in outcome (r = 0.27) as a result of motion cueing. MIsery Scale (MISC) scores on a 30 s interval, post-experiment Motion Sickness Assessment Questionnaire (MSAQ) scores, Galvanic Skin Response(GSR) and Electrogastrography (EGG) data were collected. Results showed significant correlations between the car and simulator conditions for 3 out of 4 MSAQ symptom categories (0.48 < r < 0.73, p < 0.02) and a relation (r = 0.57,p = 0.004) for individual sensitivity to sickness. Sickness onset times did not differ between the car and the simulator[F(1,308) = 4.80, p = 0.029], after Bonferroni corrections had been applied. Both MS and SS increased and decreased as a result of the driving style, with the effect being larger in the car condition, than in the simulator (for MISC [F(1,248)= 19.15, p = 0.000] and for GSR [F(1,230) = 5.55, p = 0.019]). Results from all four measures indicate that the severity of sickness was higher in the car as compared to in the simulator. EEG responses did not fully show expected outcomes. However, the signal quality was limited and dedicated EGG equipment may yield different results. Because individual sensitivity and temporal aspects of SS and MS were similar between the car and simulator but different in magnitude, we conclude relative validity for the simulator. As the human vestibular system, a prominent contributor in causing sickness, is solely sensitive to accelerations, we attribute the difference in magnitude due to downscaling of the vehicle motion in the simulator. In order to obtain absolute validity, either extensive training or considerable technological advances may be necessary. ... Read more

The level of automation in vehicles is growing. But until all vehicles are completely automated, there will be a transition period where automated vehicles and human drivers coexist. Because these road users will coexist, it is necessary that automated vehicles understand human drivers and vice versa. This study aims to create a model that predicts human risk perception in different driving scenarios, to provide an understanding of the fundamental features of human threat perception while driving. The model created is a multi-criteria decision-making process that uses KITTI Vision Benchmark data as an input. This model is tested against the data gathered by an online survey, where 1918 participants answered the question: "How high is the risk on a scale from 0-10?" for 100 situations, chosen from the KITTI Vision Benchmark data. The survey response data is then compared to the model. Analysis of the survey data revealed that risk perception of driving situations is non-linear in the extremities of risk, showing that the input image are perceived as normally distributed instead of uniformly distributed. The comparison further shows that a model with features and weights solely based on literature is only slightly capable of predicting the risk of situations with a Pearson correlation coefficient with the survey responses of 0.28, whereas a model with feature weights optimised is moderately capable of predicting the risk of situations with a correlation coefficient of 0.57. However, multivariate regression is better capable of predicting risk with a correlation coefficient of 0.70, and shows that features and weights based on literature were not enough to establish an accurate model. The features that have the most impact on the result are the information about other road users' location and heading, the ego vehicle velocity, and the road type. Further research should focus on determining if speed is the cause of the perceived risk, or if it follows from other unknown predictors. Furthermore, an extension of the questions asked of the participants and the usage of videos instead of images can clarify the discrepancy between the literature-based model and the perceived risk of the participants. ... Read more

CO2 emission of vehicles and its influence on climate change is a widely discussed topic already for many years. New CO2 emission norms for vehicles have been defined based on the 2021 baseline: 15% reduction resulting in 0.09 kg/km for 2025, and 37.5% reduction resulting in 0.069 kg/km for 2030. Both norms are defined under the Worldwide harmonized Light-duty Test Procedure (WLTP) and apply to light-duty vehicles. In this study a new accurate measure is introduced to determine data related CO2 emissions of vehicle automation. The data related aspects in this study include: sensing components, the computing platform, disks inside the vehicle, wireless communication networks and data centers. These data related emissions are used to compare to the norms, which are based on the propulsion of the vehicle. A computational model is developed based on literature and a survey to obtain the instantaneous CO2 emission rates (kg CO2-e/h) of the data related aspects for varying scenarios. Scenario analysis is applied to define six different scenarios. The scenarios differ based on two varying sensing compositions and three varying energy grids. The two sensing compositions correspond to a Tesla Model S (SAE level 2) and Waymo's Chrysler Pacifica (SAE level 4). The different energy grids correspond to the 2019 Climate Act targets for 2030 and 2050 to reduce CO2 emission, and are referred to as fixed CO2 emission rates in kg CO2-e/Wh. Sensitivity analysis of the instantaneous CO2 emission rate is applied, to obtain the data related aspects that have the strongest influence on the resulting CO2 emission in each scenario. The instantaneous data related CO2 emission rates are translated to the driving cycle of the WLTP to obtain the spatial data related CO2 emission rates (kg CO2-e/km), which are used to compare to the defined propulsion-based CO2 norms of vehicles. From the sensitivity analysis, it is concluded that the energy intensity of wireless communication networks and the data transmission rate from vehicle to data center, are the two data related aspects that have the strongest influence on the instantaneous CO2 emission rate. From the spatial CO2 emission rate of each scenario, it is concluded that the energy grid also significantly affects whether the norms can be met. For high amounts of data transmission, the norms seem to be difficult to achieve in most scenarios and situations. Therefore, the energy consumption of wireless communication networks should be further optimized. Regarding the data transmission rate, further research should be obtained to gain more insights into the values of the data transmission rates dependent on the travel speed. Based on the assumptions made in this study, the CO2 emission from the data related aspects seem to exceed the propulsion-based CO2 norms of vehicles. Future work should investigate if data related aspects of vehicle automation need to be included in the CO2 norms of vehicles. ... Read more

The concept of motion planning for a path following task is highly relevant. Having a comfortable reference motion is an advantage for automated vehicles, since this leads to more higher comfort for the passengers, in turn leading to higher adoption of ADAS. Defining a speed profile can be done in three different ways. First is a heuristics based methods, such as seen in IPG CarMaker's driver model. Secondly, line-of-sight based methods use the knowledge that blind corners can contain potential moving obstacles, leading to slower speeds to compensate for the braking distance. Finally, optimization based methods, such as laptime optimization, can be used by defining dynamics, constraints and a cost function to find the optimal motion for the given scenario. Comfort is primarily a function of acceleration and jerk. The RMS of the acceleration should be as low as possible for more comfort, while jerk lower than a determined value can be ignored from a comfort point of view. Finally, the scope and the contributions to the state of the art have been brought forward. A curvilinear approach in combination with a distance domain approach is utilized, since this allows for only the curvature of the road to be used as a reference. To generate a speed profile longitudinal point mass dynamics are utilized with the jerk as an input to the optimization. The maximum speed is determined beforehand using the curvature of the road and assuming steady-state cornering. For each point along the road a speed is now determined that should not be exceeded. Generating a speed profile using a linear optimization can not be done accurately. However, an assumption can be made that the vehicle takes the same time for each distance step, leading to an approximate solution. The non-linear optimization is able to overcome the shortcomings of the linear optimization, by including the time as a state in the solution. By incorporating the lateral dynamics in the EOM the predetermined maximum speed is no longer needed. The lateral dynamics are simplified by assuming the vehicle is always aligned with the reference, therefore ignoring yaw dynamics. Leading to a complete description of the longitudinal and lateral motion that can be solved using a non-linear optimization method. ACADO is used for the non-linear optimization. A benchmark is performed against a state of the art literature example based on a non-linear optimization method that is able to minimize motion sickness based on vibrations experienced by the passengers. The speed profile generation based on linear optimization is not able to reproduce the resulting speed profile as seen in the example. For the non-linear optimization method the speed profile obtained for the time optimal cost function is very close, however for the comfortable motion, the speed through the corners was still too high to be considered comfortable. By minimizing the acceleration and jerk the motion planning system including the lateral motion is able to accurately reproduce similar speed profiles for the three different cost functions of the example. In Simcenter Prescan a complex 10 degrees of freedom vehicle model containing both sprung and unsprung mass has been presented. A driver model, based on two PD controllers for longitudinal control and the Prescan built-in path follower for lateral control, has been introduced. The motion planning containing both lateral and longitudinal motion has been used to define a comfortable reference speed and trajectory. The simulation results show that the reference motion is comparable to the resulting motion, both in the longitudinal and the lateral direction. Indicating that the motion planning system produces accurate results. On the other hand it is also observed that the longitudinal control of the vehicle needs a more complex control scheme. For the lateral control a predictive control scheme is needed to be able to follow the road with low lateral offset. ... Read more

Advanced Driver Assistance Systems (ADAS) are becoming more available and will become mandatory for all new vehicle models from 2022 onward. In order to achieve the highest safety benefits, it is important that these systems are available. Lane Keep Assist (LKA) is part of ADAS and assists the driver in the lateral control of the vehicle. Lane markings are used by both human drivers and machine vision to stay on the road, but factors contributing to lane marking detection in different driving conditions are mostly unknown. A field test was conducted on Dutch provincial roads to evaluate lane marking visibility properties in relation to the LKA detection performance of different sensor types. The LKA detection performance of the mono camera was found to be higher in most weather and illumination conditions than the detection performance of the mono camera with infrared. The mono camera with infrared had a higher detection performance during rain in nighttime conditions than during dry daytime conditions. The highest detection performance for the mono camera and the mono camera with infrared were 97% in dry nighttime conditions and 91,4% in sunset conditions, respectively. Binary logistic regression was used to determine the effect of lane marking properties on the lane detection performance. A profiled lane marking type was found to increase the detection likelihood by 6-8 times as opposed to a smooth lane marking type. Other visibility properties, such as retroreflectivity and contrast with the road surface, were not found to be a significant contributor to the detection performance. ... Read more

Bicycles connected to the internet present an opportunity for integrated accident detection and geolocation. Such a system can reduce the time it takes for help to arrive by automatically alerting predefined contacts with the location of the accident. I developed a systematic method for the practical implementation of bicycle accident detection in connected bicycles and present the performance of a prototype system. The method uses accelerometer and gyroscopic measurements as well as localization and velocity estimations. Supplementing existing research, a bicycle accident detection system is validated on normal cycling, edge cases, and three types of single bicycle accidents with constraints set by a bicycle manufacturer. Edge cases are movements of a bicycle that occur during regular usage, but can not be described by normal cycling. This method uses a data¬driven approach. For the prototype system, the input signals are collected during 71 different simulated accidents and 54 hours of normal cycling and edge cases. A three¬layer detection algorithm determines if an accident has occurred and sends the last known location to a set of predefined contacts. Multiple combinations of thresholds and classification algorithms are compared. This resulted in a prototype system with a K¬Nearest Neighbours classifier which detects 75% of accidents. Normal cycling and edge cases are correctly detected 99.997% of the time. From all warnings send, 85.7% are true accidents. The prototype system proves that the proposed method can be used to integrate reliable accident detection in connected bicycles. Bicycles with such a system automatically inform emergency contacts with a message containing the location of the accident, in a time where every second counts. ... Read more