PDF(Pubmed)
Abstract：
Autonomous driving has the potential to revolutionize transportation, but developing safe and reliable systems remains a significant challenge. Reinforcement learning (RL) has emerged as a promising approach for learning optimal control policies in complex driving environments. However, existing RL-based methods often suffer from low sample efficiency and lack explicit safety constraints, leading to unsafe behaviors. In this paper, we propose a novel framework for safe reinforcement learning in autonomous driving that addresses these limitations. Our approach incorporates a latent dynamic model that learns the underlying dynamics of the environment from bird\'s-eye view images, enabling efficient learning and reducing the risk of safety violations by generating synthetic data. Furthermore, we introduce state-wise safety constraints through a barrier function, ensuring safety at each state by encoding constraints directly into the learning process. Experimental results in the CARLA simulator demonstrate that our framework significantly outperforms baseline methods in terms of both driving performance and safety. Our work advances the development of safe and efficient autonomous driving systems by leveraging the power of reinforcement learning with explicit safety considerations.
摘要：
自动驾驶有可能彻底改变交通，但是开发安全可靠的系统仍然是一个重大挑战。强化学习（RL）已成为在复杂驾驶环境中学习最佳控制策略的一种有前途的方法。然而,现有的基于RL的方法往往样本效率低，缺乏明确的安全约束，导致不安全的行为。在本文中,我们提出了一个新的框架，用于自动驾驶中的安全强化学习，以解决这些限制。我们的方法结合了一个潜在的动态模型，从鸟瞰图像中学习环境的潜在动态，通过生成综合数据，实现高效学习并降低安全违规风险。此外，我们通过障碍函数引入状态安全约束，通过将约束直接编码到学习过程中来确保每个状态的安全性。CARLA模拟器中的实验结果表明，我们的框架在驾驶性能和安全性方面均优于基线方法。我们的工作通过利用强化学习的力量和明确的安全考虑来推进安全高效的自动驾驶系统的开发。
