2020/11/09 by Josiah Coad, Coad, Josiah, Zhiqian Qiao +3 · 5 citations
Computer Science · Engineering · Psychology · #Artificial Intelligence (cs.AI) #Artificial intelligence #Autonomous Vehicle Technology and Safety #Computer science #Control (management) #Engineering #FOS: Computer and information sciences #Imitation #Psychology #Reinforcement #Reinforcement Learning in Robotics #Reinforcement learning #Robotics (cs.RO) #Self driving #State (computer science) #Task (project management) #Traffic control and management #Trajectory #Transport engineering #cs.AI #cs.RO
paper · pdf · doi:10.48550/arxiv.2011.04702
published in arXiv (Cornell University) (Cornell University) · 7 pages, 5 figures
arxiv created 2020/11/09 · openalex publication_date 2020/11/09 · arxiv updated 2020/11/11 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
Self-driving vehicles must be able to act intelligently in diverse and difficult environments, marked by high-dimensional state spaces, a myriad of optimization objectives and complex behaviors. Traditionally, classical optimization and search techniques have been applied to the problem of self-driving; but they do not fully address operations in environments with high-dimensional states and complex behaviors. Recently, imitation learning has been proposed for the task of self-driving; but it is labor-intensive to obtain enough training data. Reinforcement learning has been proposed as a way to directly control the car, but this has safety and comfort concerns. We propose using model-free reinforcement learning for the trajectory planning stage of self-driving and show that this approach allows us to operate the car in a more safe, general and comfortable manner, required for the task of self driving.