Poster: Delay-Resilient Cooperative Velocity Control for Mitigating Congestion Based on Machine Learning

Congestion on expressways occurs mainly due to concentration of traffic. Deceleration control, in which vehicles following the congested vehicle group intentionally slow down, is known to effectively work to mitigate this type of congestion. Previous studies on deceleration control such as variable speed limit (VSL) have been focused on environments with negligible system processing delay, that is wireless communication delay and server processing delay. This delay, however, could pose negative impact on performance in practical use. This paper proposes a novel method of deceleration control in which each vehicle independently determines its target speed by referring to both its current cruise information and surrounding traffic flow information provided by the server with the help of reinforced learning. Performance evaluation results show the proposed method outperforms existing approach in terms of average speed performance when the delay increases up to several seconds.