Cooperative bypassing algorithm for connected and autonomous vehicles in mixed traffic

Abstract

Connected and autonomous vehicles (CAVs) have the capability to acquire real-time information from each other while human-driven vehicles (HVs) are standalone in the vehicle roadway navigation system. The information asymmetry poses significant challenges in managing and controlling vehicles in mixed traffic. To address such challenges, this study first customises the intelligent driver model to describe the car-following behaviour of CAVs. Furthermore, to raise the traffic efficiency of the mixed traffic flow, a new lane-changing decision support algorithm is proposed, which incorporates the interaction between CAVs and HVs by controlling the speed and locating the lane of CAVs. In addition to the common indexes of outflow and travel time to be used to measure the travel efficiency, new parameters such as platoon intensity and single rate are defined to evaluate the CAV platooning capability. Through simulation tests, the proposed controlling framework is implemented for a two-lane (per direction) freeway segment under different CAV penetration rates and input volumes. The simulation results indicate that, at all the CAV penetration levels, the proposed controlling algorithm provides significant performance improvements to the whole mixed traffic flow in terms of outflow, travel time, and the number of CAV platoons.