Impact of Communication Loss on MPC based Cooperative Adaptive Cruise Control and Platooning

Abstract

Cooperative driving, enabled by communication between automated vehicle systems, is expected to significantly contribute to transportation safety and efficiency. Cooperative Adaptive Cruise Control (CACC) and platooning are two of the main cooperative driving applications that are currently under study. These applications offer significant improvements over current advanced driver assistant systems such as adaptive cruise control (ACC). The primary motivation of CACC and Platooning is to reduce traffic congestion and improve traffic flow, traffic throughput, and highway capacity. These applications need an efficient controller to consider the computational cost and ensure driving comfort and high responsiveness. The advantage of Model Predictive Control is that we can realize high control performance since all constrain for these applications can be explicitly dealt with through solving an optimization problem. These applications highly depend on information update and Communication reliability for their safety and stability purposes. In this paper, we propose a Model Predictive Control (MPC) based approach for CACC and platooning, and examine the impact of communication loss on the performance and robustness of the control scheme. The results show an improvement in response time and string stability, demonstrating the potential of cooperation to attenuate disturbances and improve traffic flow.

Context‐aware target classification with hybrid Gaussian process prediction for cooperative vehicle safety systems

“…In CVS systems, Remote Vehicles (RV) frequently broadcast their state information to the neighboring vehicles over a wireless channel in the form of an information message, for example, Basic Safety Messages (BSM) [4,5] using Vehicle-to-Everything (V2X) communication. V2X communication allows the information from RVs to be available at the Host Vehicle (HV) over the wireless network extending the situational awareness of CAVs and alleviating the limitations of the sensor-based systems that rely on line-ofsight sensing [6][7][8]. At the HV, the information messages are processed to monitor the neighboring road participants and create a real-time map of all objects in its vicinity.…”

Robustness and Adaptability of Reinforcement Learning based Cooperative Autonomous Driving in Mixed-autonomy Traffic

“…Connected & autonomous vehicles (CAVs) via vehicle-tovehicle (V2V) communication allow vehicles to directly communicate with their neighbors, creating an extended perception that enables explicit coordination among vehicles to overcome the limitations of an isolated agent [5]- [11]. While planning in a fully AV scenario is relatively easy to achieve, coordination in the presence of HVs is a significantly more challenging task, as the AVs not only need to react to road objects but also need to consider the behaviors of HVs [3], [4], [12].…”