Deep Reinforcement Learning for Autonomous Driving: Challenges and Solutions

This work develops a deep reinforcement learning (DRL) training paradigm that involves rapidly and iteratively updating the autonomous agent in response to only tens of human operator demonstrations. These cars require sophisticated real-time decision-making in a chaotic and dynamic environment with many uncertainties, as well as variable traffic conditions and diverse driving scenarios. Notwithstanding the high cost, current vehicle systems partially implementing these methods are still quite limited because they do not scale well to handle all of real-world driving's variability and unpredictability. We focus on this study with the help of DRL, exploiting how well it can be used for autonomous vehicle to learn the optimal driving strategy by interacting continuously. In this paper, we conduct a comprehensive study of the architecture and algorithms for DRL with emphasis on their capability to deal with highdimensional sensory inputs as well as in generating safety-critical optimal control policies that are robust (safety), efficient (rewards) and adaptive. Additionally, the paper discusses challenges on employing DRL in ADs where sample efficiency matters a lot to estimate behaviors other than simple optimal choices and so do generalization across varied scenarios, explainability of learned policies as well as guaranteeing robustness under many safety-critical situations. We also suggest possible solutions and research directions for tackling these issues, stressing the necessity of combining DRL with other techniques like transfer learning, modelbased reinforcement learning (MBRL) or simulation based-testing. Developing a deeper understanding of DRL and solving these particular problems will allow for more intelligent decision-making in autonomous cars, which is essential to make the future vision of fully selfdriving transportation scalable from both safety and reliability standpoints.