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：智能驾驶系统应能根据当前环境与车辆状态动态制定合理驾驶策略，同时确保系统安全性与可靠性。然而，现有基于强化学习与模仿学习的方法存在安全性低、泛化性差、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，而准确预测未来行驶轨迹是进行最优决策的先决条件。为解决上述问题，本文针对复杂多变场景提出了一种安全泛化的端到端自动驾驶系统（SGADS）。该系统融合了变分推断与归一化流技术，使智能车辆能够精确预测未来行驶轨迹。同时，我们提出了鲁棒安全约束的构建方法。进一步地，我们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，本系统在复杂城市场景中能显著提升智能车辆的安全性能，展现出强泛化能力，并有效提高训练效率。