Computational interaction models for automated vehicles and cyclists

Licentiatavhandling, 2023

Cyclists’ safety is crucial for a sustainable transport system. Cyclists are considered vulnerable road users because they are not protected by a physical compartment around them. In recent years, passenger car occupants’ share of fatalities has been decreasing, but that of cyclists has actually increased. Most of the conflicts between cyclists and motorized vehicles occur at crossings where they cross each other’s path. Automated vehicles (AVs) are being developed to increase traffic safety and reduce human errors in driving tasks, including when they encounter cyclists at intersections. AVs use behavioral models to predict other road user’s behaviors and then plan their path accordingly. Thus, there is a need to investigate how cyclists interact and communicate with motorized vehicles at conflicting scenarios like unsignalized intersections. This understanding will be used to develop accurate computational models of cyclists’ behavior when they interact with motorized vehicles in conflict scenarios.
The overall goal of this thesis is to investigate how cyclists communicate and interact with motorized vehicles in the specific conflict scenario of an unsignalized intersection. In the first of two studies, naturalistic data was used to model the cyclists’ decision whether to yield to a passenger car at an unsignalized intersection. Interaction events were extracted from the trajectory dataset, and cyclists’ behavioral cues were added from the sensory data. Both cyclists’ kinematics and visual cues were found to be significant in predicting who crossed the intersection first. The second study used a cycling simulator to acquire in-depth knowledge about cyclists’ behavioral patterns as they interacted with an approaching vehicle at the unsignalized intersection. Two independent variables were manipulated across the trials: difference in time to arrival at the intersection (DTA) and visibility condition (field of view distance). Results from the mixed effect logistic model showed that only DTA affected the cyclist’s decision to cross before the vehicle. However, increasing the visibility at the intersection reduced the severity of the cyclists’ braking profiles. Both studies contributed to the development of computational models of cyclist behavior that may be used to support safe automated driving.
Future work aims to find differences in cyclists’ interactions with different vehicle types, such as passenger cars, taxis, and trucks. In addition, the interaction process may also be evaluated from the driver’s perspective by using a driving simulator instead of a riding simulator. This setup 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.