In recent years, system intelligence has revolutionized various domains, including the automotive industry, which has fully incorporated intelligence through the emergence of Advanced Driver Assistance Systems (ADAS). Within this transformative context, Autonomous Parking Systems (APS) have emerged as a foundational component, revolutionizing the way vehicles navigate and park with precision and efficiency. This paper presents a comprehensive approach to autonomous parallel parking, leveraging advancements in Artificial Intelligence (AI). Three state-of-the-practice approaches—Imitation Learning (IL), deep Reinforcement Learning (deep RL), and a hybrid deep RL-IL method—are employed and evaluated through extensive experiments in the CARLA Simulator using randomly generated parallel parking scenarios. Results demonstrate that the hybrid deep RL-IL approach achieves a remarkable success rate of 98% in parking attempts, surpassing the individual IL and deep RL methods. Furthermore, the proposed hybrid model exhibits superior maneuvering efficiency and higher overall reward accumulation. These findings underscore the advantages of combining deep RL and IL, representing a significant advancement in APS technology.
Read more: Here
