Soft Degradation of CAVs Based on Historical Dynamic Information

Abstract

In recent years, many researchers have paid great attention to the transportation convenience and advantages brought by the future extensive use of connected and automated vehicles (CAVs). However, CAVs will degrade to lower-rank automated vehicles (AVs) when vehicle-to-vehicle (V2V) communication links are not available, which will cause a mess of traffic and even increase the risk of collision. How to avoid hard degradation of CAVs or to maintain the cooperative status based on the AVs information will be a highly concerning problem worth studying. This paper proposes a soft degradation strategy, in which the degraded CAVs will keep cooperative control only based on historical information of AVs. Specifically, the strategy uses historical information detected by onboard sensors to infer the acceleration of the preceding vehicle. Theoretical analysis shows that the proposed soft degradation strategy can significantly improve traffic flow stability caused by the degradation of CAVs. The direct numerical results are in good agreement with those of theoretical analysis. Compared with the existing strategies, our strategy can better improve traffic stability, safety, and fuel economy when CAVs degrade to AVs. These findings can give insights for traffic managers and vehicle designers to solve the degradation of CAVs.