An intelligent driving system should be capable of dynamically formulating appropriate driving strategies based on the current environment and vehicle status, while ensuring the security and reliability of the system. However, existing methods based on reinforcement learning and imitation learning suffer from low safety, poor generalization, and inefficient sampling. Additionally, they cannot accurately predict future driving trajectories, and the accurate prediction of future driving trajectories is a precondition for making optimal decisions. To solve these problems, in this paper, we introduce a Safe and Generalized end-to-end Autonomous Driving System (SGADS) for complex and various scenarios. Our SGADS incorporates variational inference with normalizing flows, enabling the intelligent vehicle to accurately predict future driving trajectories. Moreover, we propose the formulation of robust safety constraints. Furthermore, we combine reinforcement learning with demonstrations to augment search process of the agent. The experimental results demonstrate that our SGADS can significantly improve safety performance, exhibit strong generalization, and enhance the training efficiency of intelligent vehicles in complex urban scenarios compared to existing methods.

翻译：智能驾驶系统应能基于当前环境与车辆状态动态制定合适的驾驶策略，同时确保系统的安全性与可靠性。然而，现有基于强化学习与模仿学习的方法存在安全性低、泛化能力差、采样效率低下等问题。此外，这些方法无法准确预测未来驾驶轨迹，而未来驾驶轨迹的精确预测是最优决策的前提条件。为解决上述问题，本文提出了一种面向复杂多变场景的安全且泛化的端到端自动驾驶系统（SGADS）。该系统通过结合归一化流的变分推理技术，使智能车辆能够精确预测未来驾驶轨迹。同时，我们提出了鲁棒安全约束的构建方法，并进一步将强化学习与示范相结合以增强智能体的搜索过程。实验结果表明，与现有方法相比，SGADS可显著提升复杂城市场景中智能车辆的安全性能，展现出强大的泛化能力，并有效提高训练效率。