Optimizing Vehicle Approach Strategies for Connected Signalized Intersections

摘要

Approaching a traffic light regulated intersection or road segment represents a challenge in the context of fully or partially automated vehicles. The current state of an upcoming traffic light needs to be detected reliably and the resulting vehicle approach behavior can therefore only be based on the current state of the traffic light. While camera-based detection systems for traffic lights only allow for capturing the current state of the traffic light, its remaining duration is usually unknown, just as it is for manually operated vehicles. Vehicle-to-everything (V2X) communications enables vehicles to also receive information about the remaining duration of the current state directly from the infrastructure and to therefore optimize the approach behavior to arrive within the current or upcoming green windows. While the majority of related contributions aim at computing a green light optimal advisory speed or propose a computationally expensive optimization technique, this contribution introduces a lightweight and scalable methodology enabling the generation of an optimized approach behavior for connected (partially) automated vehicles. The introduced concept of a convex solution space targets a low computational overhead and still allows for adapting the vehicle speed to surrounding traffic participants.