Deep Reinforcement Learning in Autonomous Car Path Planning and Control: A Survey

As autonomous driving technology rapidly advances, its potential to relieve drivers, enhance traffic efficiency, reduce energy consumption, and improve road safety is increasingly being recognized[1]. At present, advancements in autonomous vehicle control technologies are chiefly derived from the integration of Advanced Driver Assistance Systems (ADAS), including Adaptive Cruise Control (ACC), Lane Keeping Assistance Systems, and Lane Departure Warning technologies, which have been implemented in a variety of commercial electric vehicles. Projects such as Google's Waymo and Baidu's Apollo have advanced towards commercial operations, achieving autonomous driving capabilities and launching unmanned vehicle rental services in designated areas. The control framework of autonomous vehicles fundamentally encompasses three tiers: perception, planning, and control, with Figure 1 [2] depicting the comprehensive architecture of autonomous driving systems. The perception layer is tasked with the accurate perception and processing of measurement data to produce dependable state estimates essential for precise localization and environmental recognition.