Safety and efficiency-oriented adaptive strategy controls for connected and automated vehicles in unstable communication environment

Connected and automated vehicle (CAV) has demonstrated significant potential in enhancing traffic safety, efficiency, and environmental sustainability in the premise of high-quality communication environment. However, attributed to unexpected disturbances in real life, vehicular communication often suffers from unstable information transmission that lowers the performance of CAV platooning. Despite this drawback, existing research lacks sufficient attention on CAV platooning in the presence of unstable communication environment (UCE) and rarely addresses the response strategies. To fill these research gaps, this study integrates dynamic communication topologies into multi-class controls of automated and/or connected vehicles in response to various data abnormalities, based on simulation data generated from the modified intelligent driver model (IDM) and the communication disturbance framework designed to emulate UCE. The impact of UCE on CAV platooning performance is evaluated in terms of traffic safety and efficiency. The study introduces five adaptive dynamic strategy controls (ADSCs) and three hybrid switching ADSCs to enhance platoon performance under varying UCE conditions, along with recommendations on applicable ADSCs for maintaining the platoon performance under different UCE scenarios. Also, our sensitivity analysis of attenuation model parameters identifies optimal configurations for the development of ADSCs. Results show that continuous UCE (CUCE) poses greater safety challenges compared to intermittent UCE (IUCE), with combined IUCE being the most detrimental. Scenarios involving aggressive acceleration behavior significantly increase the collision risk and speed oscillation within the platoon. The proposed hybrid switching ADSCs significantly improve CAV platoon performance, offering resilient and adaptive vehicle control strategies to smooth traffic flow under disturbances.