Intelligent Cooperative Collision Avoidance at Overtaking and Lane Changing Maneuver in 6G-V2X Communications
Sahaya Beni Prathiba, Gunasekaran Raja, Neeraj Kumar
Abstract
The rapid growth in Autonomous Vehicle (AV) technology endeavors increased attention towards road safety in recent days. Particularly, a higher number of road accidents occurs when the AV tries to overtake or change the lane. To cut down the number of accidents and improve traffic reliability, the AV should be capable of making intelligent decisions and communicating those to other AVs. Therefore, in this paper, a Cooperative Collision avoidance scheme for AVs at Overtaking and Lane Changing maneuver (CCAV-OLC) is proposed. The Inverse Reinforcement Learning (IRL) in the CCAV-OLC scheme, processes on the given number of expert demonstrations for automatically acquiring the reward function, and thereby imitating actual human driving strategy and decisions. However, the adaptability of IRL to a high-dimensional AV environment restricts the performance of the CCAV-OLC scheme. To overcome this, the IRL in CCAV-OLC leverages the Gaussian Process (GP) regression model (IRL-GP), which enables data-efficient Bayesian prediction even when the number of demonstrations is very low. After taking intelligent decisions in overtaking and lane changing maneuvers, the AVs cooperatively communicate and exchange the decisions with each other by 6th Generation Vehicle-to-Everything (6G-V2X) communications, which further improves the accuracy and lessens the time taken for making optimal decisions. The experimental results show that the AVs clone the expert's optimal driving strategy and avoid the collisions to a greater extent.