Analysis of Traffic Conflicts With Right Turning Vehicles at Unsignalized Intersections in Suburban Areas

Right-turn collisions at intersections are one of the most dominant crash types in suburban areas, especially at unsignalized intersections. There is, however, a lack of comprehensive research on the speed patterns of vehicles during right-turn manoeuvres and their impact on crashes. To provide an in-depth investigation of the factors determining the safety of right-turn manoeuvres, driving behaviour data were collected through an instrumented vehicle study. Using this data, binary logistic regression models were developed to identify the factors affecting the probability of Vehicle-Vehicle (V-V) and Vehicle-Pedestrian (V-P) conflicts at six suburban intersections in Babol, Iran, during right-turn stage manoeuvres. In total, 1,456 V-V and V-P conflicts were identified from the data analysis. The results from the logistic regression model showed that the vehicle speed, the distance between road users, as well as driver and pedestrian distractions were associated with higher risk for vehicle-to-vehicle or vehicle-to-pedestrian conflicts. To estimate the safe right-turn speeds to be selected by the drivers at different stages of the right turn, i.e., at the start, during, and end of the movement, linear regression models were developed. The results showed that participants adjust their driving behaviour the same way toward pedestrians as they do toward vehicles. The findings of this study can be leveraged for the development of a robust advanced driving assistance system, the use of which can further improve the safety performance of right-turn manoeuvres

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

Critical Scenario Identification for Testing of Autonomous Driving Systems

Background: Autonomous systems have received considerable attention from academia and are adopted by various industrial domains, such as automotive, avionics, etc. As many of them are considered safety-critical, testing is indispensable to verify their reliability and safety. However, there is no common standard for testing autonomous systems efficiently and effectively. Thus new approaches for testing such systems must be developed.Aim: The objective of this thesis is two-fold. First, we want to present an overview of software testing of autonomous systems, i.e., relevant concepts, challenges, and techniques available in academic research and industry practice. Second, we aim to establish a new approach for testing autonomous driving systems and demonstrate its effectiveness by using real autonomous driving systems from industry.Research Methodology: We conducted the research in three steps using the design science paradigm. First, we explored the existing literature and industry practices to understand the state of the art for testing of autonomous systems. Second, we focused on a particular sub-domain - autonomous driving - and proposed a systematic approach for critical test scenario identification. Lastly, we validated our approach and employed it for testing real autonomous driving systems by collaborating with Volvo Cars.Results: We present the results as four papers in this thesis. First, we conceptualized a definition of autonomous systems and classified challenges and approaches, techniques, and practices for testing autonomous systems in general. Second, we designed a systematic approach for critical test scenario identification. We employed the approach for testing two real autonomous driving systems from the industry and have effectively identified critical test scenarios. Lastly, we established a model for predicting the distribution of vehicle-pedestrian interactions for realistic test scenario generation for autonomous driving systems. Conclusion: Critical scenario identification is a favorable approach to generate test scenarios and facilitate the testing of autonomous driving systems in an efficient way. Future improvement of the approach includes (1) evaluating the effectiveness of the generated critical scenarios for testing; (2) extending the sub-components in this approach; (3) combining different testing approaches, and (4) exploring the application of the approach to test different autonomous systems

Analysis of traffic conflicts with right-turning vehicles at unsignalized intersections in suburban areas

Right-turn collisions at intersections are one of the most dominant crash types in suburban areas, especially at unsignalized intersections. There is, however, a lack of comprehensive research on the speed patterns of vehicles during right-turn manoeuvres and their impact on crashes. To provide an in-depth investigation of the factors determining the safety of right-turn manoeuvres, driving behaviour data were collected through an instrumented vehicle study. Using this data, binary logistic regression models were developed to identify the factors affecting the probability of Vehicle-Vehicle (V-V) and Vehicle-Pedestrian (V-P) conflicts at six suburban intersections in Babol, Iran, during right-turn stage manoeuvres. In total, 1,456 V-V and V-P conflicts were identified from the data analysis. The results from the logistic regression model showed that the vehicle speed, the distance between road users, as well as driver and pedestrian distractions were associated with higher risk for vehicle-to-vehicle or vehicle-to-pedestrian conflicts. To estimate the safe right-turn speeds to be selected by the drivers at different stages of the right turn, i.e., at the start, during, and end of the movement, linear regression models were developed. The results showed that participants adjust their driving behaviour the same way toward pedestrians as they do toward vehicles. The findings of this study can be leveraged for the development of a robust advanced driving assistance system, the use of which can further improve the safety performance of right-turn manoeuvres

Filtration analysis of pedestrian-vehicle interactions for autonomous vehicle control

Abstract. Interacting with humans remains a challenge for autonomous vehicles (AVs). When a pedestrian wishes to cross the road in front of the vehicle at an unmarked crossing, the pedestrian and AV must compete for the space, which may be considered as a game-theoretic interaction in which one agent must yield to the other. To inform development of new real-time AV controllers in this setting, this study collects and analy- ses detailed, manually-annotated, temporal data from real-world human road crossings as they interact with manual drive vehicles. It studies the temporal orderings (filtrations) in which features are revealed to the ve- hicle and their informativeness over time. It presents a new framework suggesting how optimal stopping controllers may then use such data to enable an AV to decide when to act (by speeding up, slowing down, or otherwise signalling intent to the pedestrian) or alternatively, to continue at its current speed in order to gather additional information from new features, including signals from that pedestrian, before acting itself

Filtration analysis of pedestrian-vehicle interactions for autonomous vehicle control

Interacting with humans remains a challenge for autonomousvehicles (AVs). When a pedestrian wishes to cross the road in front of thevehicle at an unmarked crossing, the pedestrian and AV must competefor the space, which may be considered as a game-theoretic interaction inwhich one agent must yield to the other. To inform development of newreal-time AV controllers in this setting, this study collects and analy-ses detailed, manually-annotated, temporal data from real-world humanroad crossings as they interact with manual drive vehicles. It studies thetemporal orderings (filtrations) in which features are revealed to the ve-hicle and their informativeness over time. It presents a new frameworksuggesting how optimal stopping controllers may then use such data toenable an AV to decide when to act (by speeding up, slowing down, orotherwise signalling intent to the pedestrian) or alternatively, to continueat its current speed in order to gather additional information from newfeatures, including signals from that pedestrian, before acting itself