TL;DR
This study assesses how connected autonomous vehicles can enhance traffic flow in realistic mixed-traffic environments, considering imperfect communication and large-scale networks, revealing significant efficiency gains at high penetration rates under reliable communication.
Contribution
It investigates the impact of unreliable communication on CAV performance in mixed traffic within large-scale networks, a realistic scenario previously underexplored.
Findings
CAVs improve traffic efficiency significantly at high penetration rates.
Communication reliability critically affects CAV benefits, with high packet loss reducing efficiency gains.
Traffic congestion increases notably with communication failures, especially at higher penetration rates.
Abstract
Connected autonomous vehicles (CAVs) can supplement the information from their own sensors with information from surrounding CAVs for decision making and control. This has the potential to improve traffic efficiency. CAVs face additional challenges in their driving, however, when they interact with human-driven vehicles (HDVs) in mixed-traffic environments due to the uncertainty in human's driving behavior e.g. larger reaction times, perception errors, etc. While a lot of research has investigated the impact of CAVs on traffic safety and efficiency at different penetration rates, all have assumed either perfect communication or very simple scenarios with imperfect communication. In practice, the presence of communication delays and packet losses means that CAVs might receive only partial information from surrounding vehicles, and this can have detrimental effects on their performance.…
Peer Reviews
No public reviews on file for this paper yet. If you reviewed it on a platform where reviews are public (OpenReview, ICLR, NeurIPS, ICML), you can paste yours below so the community can read it here.
Code & Models
Videos
No videos yet. Explain this paper in a talk, walkthrough, or lecture? Add one.
