Longitudinal Car-Following Control of Connected Autonomous Vehicles in Realistic Scenarios: A Survey

By exploiting information from surrounding vehicles and road infrastructure, Connected Autonomous Vehicles (CAVs) are widely expected to enable improved traffic safety and efficiency, as well as fuel savings. In practice, however, the information exchange may suffer from communication delays and packet losses due to limited communication channel bandwidth, fading, and signal interference, and this might have severe negative effects on the longitudinal control performance of CAVs. Furthermore, it is expected that mixed traffic, i.e., vehicles with different automation levels and communication capabilities (Human-driven vehicles, Connected human-driven vehicles, Autonomous vehicles, CAVs), will coexist on roads during a long transition period. This creates additional control challenges for CAVs due to the uncertainty in human’s driving behaviour. While extensive surveys on longitudinal control strategies for CAVs and their impact on traffic performance have been reported in the literature in recent years, they have assumed CAVs’ operation in unrealistic scenarios such as perfect communication, pure CAVs traffic, identical vehicle dynamics, etc. In this paper, we review the existing work on CAVs in realistic scenarios with a focus on the control strategies to address the challenges in unreliable communication and mixed-traffic environments. Open research questions and potential future research directions to facilitate the deployment of CAVs are also highlighted.