AI Recommendation Systems for Lane-Changing Using Adherence-Aware Reinforcement Learning

-- In this paper, we present an adherence-aware reinforcement learning (RL) approach aimed at seeking optimal lane-changing recommendations within a semi-autonomous driving environment to enhance a single vehicles travel efficiency. The problem is framed within a Markov decision process setting and is addressed through an adherence-aware deep Q network, which takes into account the partial compliance of human drivers with the recommended actions. This approach is evaluated within CARLAs driving environment under realistic scenarios. Autonomous driving is classified into five levels, ranging from Level 1 to Level 5 [1]. However, complete Level 5 automation has yet to be realized. Current research and development primarily focus on partially autonomous vehicles at Levels 2 to 4, where human drivers retain varying degrees of responsibility. In such a semi-autonomous driving environment, an artificial intelligence (AI) system usually provides recommendations on driving decisions.