An Efficient Safety-Oriented Car-Following Model for Connected Automated Vehicles Considering Discrete Signals

Abstract

With the rapid development of Connected and Automated Vehicle (CAV) technology, limited self-driving vehicles have been commercially available in certain leading intelligent transportation system countries. When formulating the car-following model for CAVs, safety is usually the basic constraint. Safety-oriented car-following models seek to specify a safe following distance that can guarantee safety if the preceding vehicle were to brake hard suddenly. The discrete signals of CAVs bring a series of phenomena, including discrete decision-making, phase difference, and discretely distributed communication delay. The influences of these phenomena on the car-following safety of CAVs are rarely considered in the literature. This paper proposes an efficient safety-oriented car-following model for CAVs considering the impact of discrete signals. The safety constraints during both normal driving and a sudden hard brake are incorporated into one integrated model to eliminate possible collisions during the whole driving process. The mechanical delay information of the preceding vehicle is used to improve car-following efficiency. Four modules are designed to enhance driving comfort and string stability in case of heavy packet losses. Simulations of a platoon with diversified vehicle types demonstrate the safety, efficiency, and string stability of the proposed model. Tests with different packet loss rates imply that the model could guarantee safety and driving comfort in even poor communication environments.