Predictive Model-Based and Control-Aware Communication Strategies for Cooperative Adaptive Cruise Control

Abstract

Utilizing Vehicle-to-everything (V2X) communication technologies, vehicle platooning systems are expected to realize a new paradigm of cooperative driving with higher levels of traffic safety and efficiency. Connected and Autonomous Vehicles (CAVs) need to have proper awareness of the traffic context. The cooperative platoon's performance will be influenced by the communication strategy. In particular, time-triggered or event-triggered are of interest here. The expenses related to communication will increase significantly as the number of connected entities increases. Periodic communication can be relaxed to more flexible aperiodic or event-triggered implementations while maintaining desired levels of performance. This paper proposes a predictive model-based and control-aware communication solution for vehicle platoons. The method uses a fully distributed Event-Triggered Communication (ETC) strategy combined with Model-Based Communication (MBC) and aims to minimize communication resource usage while preserving desired closed-loop performance characteristics. In our method, each vehicle runs a remote vehicle state estimator based on the most recently communicated model and the event-driven communication scheme only updates the model when the performance metric error exceeds a certain threshold. Our approach achieves a significant reduction in the average communication rate (82%) while only slightly reducing control performance (e.g., less than 1% speed deviation).

Augmented driver behavior models for high‐fidelity simulation study of crash detection algorithms

“…Various methods have been proposed to minimize the tracking error. A few notable ones include simple models such as first and second order kinematic models (constant speed and constant acceleration), to more complex ones that are based on Kalman Filter and Gaussian Process [33][34][35]. Nevertheless, it has been shown that in a longitudinal car-following regime, a second order kinematic model can perform with high accuracy [36].…”

Comfort and Safety in Conditional Automated Driving in Dependence on Personal Driving Behavior

“…Higher rates of AVs in the future and their ability to communicate with both each other and the environment will influence the distancing behavior between vehicles as well. With the goal of traffic flow optimization for increasing fuel efficiency, platooning will enable vehicles to also maintain closer distances between each other while still ensuring safety [50]- [52].…”