Licenciate seminar: Computational interaction models for automated vehicles and cyclists
oktober 13 @ 13:15 - 14:30
Ämne: Ali Mohammadi’s Licentiate Seminar
När: den 13 oktober 2023 13:15-14:30 (UTC+01:00) Amsterdam, Berlin, Bern, Rom, Stockholm, Wien.
Plats: Room Omega, Linholmen and Zoom
You are all welcome to Ali Mohammadi’s licentiate seminar. (In Sweden, PhD candidates also present a (licentiate) thesis mid-way to their final defense.)
The title of the seminar is: Computational interaction models for automated vehicles and cyclists (the abstract is below)
This research was performed within the SHAPE-IT and Hi-Drive projects, funded by the European Commission (Marie Skłodowska-Curie, g.a. 860410 and Horizon 2020 program, g.a. 101006664) at Chalmers in the Vehicle Safety Division at the Mechanics and Maritime Sciences department.
Discussion leader: Gabriel Rodrigues de Campos, Technical Expert Precautionary Safety, Zenseact
Supervision team: Marco Dozza and Giulio Bianchi-Piccinini, Chalmers
Examiner: Marco Dozza, Chalmers
The seminar will be at: Chalmers Lindholmen. Room Omega, house Jupiter.
To join on-line instead, please use the link below.
https://chalmers.zoom.us/j/66753122670
Password: 064293
International numbers available: https://chalmers.zoom.us/u/cyPWMhza3
Abstract
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.