Driver interaction with vulnerable road users: Modelling driver behaviour in crossing scenarios

Every year, more than 5000 pedestrians and 2000 cyclists die on European roads. These vulnerable road users (VRUs) are especially at risk when interacting with cars. Intelligent safety systems (ISSs), designed to mitigate or avoid crashes between cars and VRUs, first entered the market a few years ago, and still need to be improved to be effective. Understanding how drivers interact with VRUs is crucial to improving the development and the evaluation of ISSs. Today, however, there is a lack of knowledge about driver behaviour in interactions with VRUs. To address this deficiency and contribute to realising the full potential of ISSs, this thesis has multiple objectives: 1) to investigate and describe the driver response process when a VRU crosses the driver path, 2) to devise models that can predict the driver response process, 3) to inform Euro NCAP with new knowledge about driver interactions with crossing VRUs that may guide the development of their test scenarios, and 4) to develop a framework for ISS evaluation through counterfactual simulation and analyse the impact of the chosen driver model on the simulation outcome. The thesis results show that the moment when a VRU becomes visible to the driver has the largest influence on the driver’s braking response process in driver-VRU interactions. Data gathered in driving simulators and on a test track were used to devise different predictive models: one model for the pedestrian crossing scenario, and three for the cyclist crossing scenario. The model for the pedestrian crossing scenario can estimate the moments at which key components of the driver response process (e.g. gas pedal fully released and brake onset) happen. For the cyclist crossing scenario, the first model predicts the brake onset time and the second predicts the experienced discomfort score given the cyclist appearance time. The third predicts the continuous deflection signal of the brake pedal based on the interaction of two visually-derived cues (looming and projected post-encroachment time). These models could be used to improve the design and evaluation of ISSs. From the models, appropriate warning or intervention times that are not a nuisance to the drivers could be adopted by the ISSs, therefore maximizing driver acceptance. Additionally, the models could be used in counterfactual simulations to evaluate ISS safety benefits. In fact, it was shown that driver models are a critical part of these simulations, further demonstrating the need for the development of more realistic driver models. The knowledge provided by this thesis may also guide Euro NCAP towards an improved ISS test protocol by providing information about scenarios that have not yet been evaluated

Computational interaction models for automated vehicles and cyclists

Cyclists’ safety is crucial for a sustainable transport system. Cyclists are considered vulnerableroad users because they are not protected by a physical compartment around them. In recentyears, passenger car occupants’ share of fatalities has been decreasing, but that of cyclists hasactually increased. Most of the conflicts between cyclists and motorized vehicles occur atcrossings where they cross each other’s path. Automated vehicles (AVs) are being developedto increase traffic safety and reduce human errors in driving tasks, including when theyencounter cyclists at intersections. AVs use behavioral models to predict other road user’sbehaviors and then plan their path accordingly. Thus, there is a need to investigate how cyclistsinteract and communicate with motorized vehicles at conflicting scenarios like unsignalizedintersections. This understanding will be used to develop accurate computational models ofcyclists’ behavior when they interact with motorized vehicles in conflict scenarios.The overall goal of this thesis is to investigate how cyclists communicate and interact withmotorized vehicles in the specific conflict scenario of an unsignalized intersection. In the firstof two studies, naturalistic data was used to model the cyclists’ decision whether to yield to apassenger car at an unsignalized intersection. Interaction events were extracted from thetrajectory dataset, and cyclists’ behavioral cues were added from the sensory data. Bothcyclists’ kinematics and visual cues were found to be significant in predicting who crossed theintersection first. The second study used a cycling simulator to acquire in-depth knowledgeabout cyclists’ behavioral patterns as they interacted with an approaching vehicle at theunsignalized intersection. Two independent variables were manipulated across the trials:difference in time to arrival at the intersection (DTA) and visibility condition (field of viewdistance). Results from the mixed effect logistic model showed that only DTA affected thecyclist’s decision to cross before the vehicle. However, increasing the visibility at theintersection reduced the severity of the cyclists’ braking profiles. Both studies contributed tothe development of computational models of cyclist behavior that may be used to support safeautomated driving.Future work aims to find differences in cyclists’ interactions with different vehicle types, suchas passenger cars, taxis, and trucks. In addition, the interaction process may also be evaluatedfrom the driver’s perspective by using a driving simulator instead of a riding simulator. Thissetup would allow us to investigate how drivers respond to cyclists at the same intersection.The resulting data will contribute to the development of accurate predictive models for AVs

Driver performance under simulated and actual driving conditions: validity and orthogonality

This study contrasted the performance of drivers under actual and simulated driving conditions, in order to assess the validity of the simulators and test the hypothesis that driving is composed of largely orthogonal sub-tasks. Thirty experienced drivers completed an on-road driving test and drove two different simulators, each simulator drive comprising seven difficulty-moderated driving scenarios. Between-simulator contrasts revealed largely absolute validity, the anticipated effects of increased difficulty within driving scenarios, but weak relationships between performance of different driving scenarios. On-road driving was reliably assessed by a nationally-recognised expert driving assessor, as reflected by standard statistical measures of reliability and consistency. However, on-road driving revealed relatively little cross-category correlation of on-road driving errors, or between on-road and simulator driving. Thus, despite the compelling evidence of absolute and relative validity within and between simulators, there is little evidence of criterion validity (i.e. relationship to on road driving, as assessed by the expert assessor). Moreover, the study provides strong evidence for orthogonality in the driving task- driving comprises large numbers of relatively separate tasks

Studying Pedestrian’s Unmarked Midblock Crossing Behavior on a Multilane Road When Interacting With Autonomous Vehicles Using Virtual Reality

This dissertation focuses on the challenge of pedestrian interaction with autonomous vehicles (AVs) at unmarked midblock locations where the right-of-way is unspecified. A virtual reality (VR) simulation was developed to replicate an urban unmarked midblock environment where pedestrians cross a four-lane arterial roadway and interact with AVs. One research goal is to investigate the impact of roadway centerline features (undivided, two-way left-turn lane, and median) and AV operational schemes portrayed through on-vehicle signals (no signal, yellow negotiating indication, and yellow/blue negotiating/no-yield indications) on pedestrian crossing behavior. Results demonstrate that both roadway centerline design features and AV operations and signaling show significant impacts on pedestrians\u27 unmarked midblock crossing behavior, including the waiting time at the curb, waiting time in the middle of the road, and the total crossing time. Whereas, only the roadway centerline design features significantly impact the walking time, and only the AV operations and signaling significantly impact the accepted gap. Participants in the undivided centerline scene spent longer time waiting at the curb and walking on the road. Also, pedestrians are more likely to display risky behavior and cross in front of AVs indicating blue signals with non-yielding behavior in the presence of a median centerline scene. The inclusion of a yellow signal, which indicates the detection of pedestrians and signifies that the AVs will negotiate with them, resulted in a significant reduction in pedestrian waiting time both at the curb and in the middle of the road, when compared to AVs without a signal. Interaction effects between roadway centerline design features and AV operations and signaling are significant only for waiting time in the middle of the road. It is also found that older pedestrians tend to wait longer at the curb and are less likely to cross in front of AVs showing a blue signal with non-yielding behavior. Another research goal is to investigate how this VR experience change pedestrians’ perception of AVs. Results demonstrated that both pedestrians’ overall attitude toward AVs and trust in the effectiveness of AV systems significantly improved after the VR experience. It is also found that the more pedestrians trust the yellow signals, the more likely they are to improve their perception of AVs. Further, pedestrians who exhibit more aggressive crossing behavior are less likely to change their perception towards AVs as compared to those pedestrians who display rule-conforming crossing behaviors. Also, if the experiment made pedestrians feel motion sick, they were less likely to experience increased trust in the AV system\u27s effectiveness

Evaluating a VR System for Collecting Safety-Critical Vehicle-Pedestrian Interactions

Autonomous vehicles (AVs) require comprehensive and reliable pedestrian trajectory data to ensure safe operation. However, obtaining data of safety-critical scenarios such as jaywalking and near-collisions, or uncommon agents such as children, disabled pedestrians, and vulnerable road users poses logistical and ethical challenges. This paper evaluates a Virtual Reality (VR) system designed to collect pedestrian trajectory and body pose data in a controlled, low-risk environment. We substantiate the usefulness of such a system through semi-structured interviews with professionals in the AV field, and validate the effectiveness of the system through two empirical studies: a first-person user evaluation involving 62 participants, and a third-person evaluative survey involving 290 respondents. Our findings demonstrate that the VR-based data collection system elicits realistic responses for capturing pedestrian data in safety-critical or uncommon vehicle-pedestrian interaction scenarios.Comment: In submission to CHI 202

GETTING ACTIVE WITH PASSIVE CROSSINGS: INVESTIGATING THE EFFICACY OF IN-VEHICLE AUDITORY ALERTS FOR RAIL ROAD CROSSINGS

Train-vehicle collisions at highway-rail grade crossings continue to be a major issue in the US and across the world. Installing additional hardware at individual crossings is expensive, time consuming, and potentially ineffective. To prevent recent trends in safety improvement from plateauing, experts are turning towards novel warning devices that can be applied to all crossings with minimal cost. In-vehicle auditory alerts (IVAAs) could potentially remedy many of the human factor issues related to crossing safety in a cost effective manner. This thesis presents a series of experiments designing and testing an IVAA system for grade level railroad (RR) crossings. Study 1 collected subjective data on a pool of potential in-vehicle auditory alerts from 31 undergraduate participants. The type of IVAAs was varied along a number of dimensions (pitch, repetition, wave shape, wording, voice, etc.). Results from study 1 were used to design a prototype IVAA crossing notification system. A pilot study was conducted to calibrate the simulated driving scenario featuring multiple RR crossings and a compliance behavior coding procedure. Compliance behavior was operationalized as an amount of visual scanning and pedal depression. Study 2 recruited 20 undergraduate participants to drive in a medium fidelity driving simulator featuring four types of RR crossings with and without IVAAs. Results suggest that IVAAs not only inform and remind drivers of how to comply at RR crossings, but also have a lasting effect on driver behavior after the IVAA is no longer presented. Compliance scores were highest among novel RR crossing visual warnings such as crossbucks featuring STOP or YIELD signs. Compliance was lowest for crossbucks alone and active gates in the off position. IVAAs had the largest impact on compliance scores at crossbucks and gates. The discussion includes implications for designing IVAA systems for RR crossings, and the potential implementation of prototype systems as a smartphone application

A System for the Generation of Synthetic Wide Area Aerial Surveillance Imagery

The development, benchmarking and validation of aerial Persistent Surveillance (PS) algorithms requires access to specialist Wide Area Aerial Surveillance (WAAS) datasets. Such datasets are difficult to obtain and are often extremely large both in spatial resolution and temporal duration. This paper outlines an approach to the simulation of complex urban environments and demonstrates the viability of using this approach for the generation of simulated sensor data, corresponding to the use of wide area imaging systems for surveillance and reconnaissance applications. This provides a cost-effective method to generate datasets for vehicle tracking algorithms and anomaly detection methods. The system fuses the Simulation of Urban Mobility (SUMO) traffic simulator with a MATLAB controller and an image generator to create scenes containing uninterrupted door-to-door journeys across large areas of the urban environment. This ‘pattern-of-life’ approach provides three-dimensional visual information with natural movement and traffic flows. This can then be used to provide simulated sensor measurements (e.g. visual band and infrared video imagery) and automatic access to ground-truth data for the evaluation of multi-target tracking systems

Driving examiners’ views on data-driven assessment of test candidates:An interview study

Vehicles are increasingly equipped with sensors that capture the state of the driver, the vehicle, and the environment. These developments are relevant to formal driver testing, but little is known about the extent to which driving examiners would support the use of sensor data in their job. This semi-structured interview study examined the opinions of 37 driving examiners about datadriven assessment of test candidates. The results showed that the examiners were supportive of using data to explain their pass/fail verdict to the candidate. According to the examiners, data in an easily accessible form such as graphs of eye movements, headway, speed, or braking behaviour, and colour-coded scores, supplemented with camera images, would allow them to eliminate doubt or help them convince disagreeing test-takers. The examiners were sceptical about higher levels of decision support, noting that forming an overall picture of the candidate’s abilities requires integrating multiple context-dependent sources of information. The interviews yielded other possible applications of data collection and sharing, such as selecting optimal routes, improving standardization, and training and pre-selecting candidates before they are allowed to take the driving test. Finally, the interviews focused on an increasingly viable form of data collection: simulator-based driver testing. This yielded a divided picture, with about half of the examiners being positive and half negative about using simulators in driver testing. In conclusion, this study has provided important insights regarding the use of data as an explanation aid for examiners. Future research should consider the views of test candidates and experimentally evaluate different forms of data-driven support in the driving test

Vehicle and Traffic Safety

The book is devoted to contemporary issues regarding the safety of motor vehicles and road traffic. It presents the achievements of scientists, specialists, and industry representatives in the following selected areas of road transport safety and automotive engineering: active and passive vehicle safety, vehicle dynamics and stability, testing of vehicles (and their assemblies), including electric cars as well as autonomous vehicles. Selected issues from the area of accident analysis and reconstruction are discussed. The impact on road safety of aspects such as traffic control systems, road infrastructure, and human factors is also considered