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.

智能驾驶系统应能根据当前环境和车辆状况动态制定适当的驾驶策略，确保系统的安全可靠性。本文介绍了一种适用于复杂和各种场景的安全通用端到端自动驾驶系统（SGADS），该系统结合了变分推断与归一化流，使智能车辆能够准确预测未来的驾驶轨迹，并提出了鲁棒安全约束的制定方法。此外，我们将强化学习与示范结合，以增强智能体的搜索过程。实验结果表明，与现有方法相比，我们的 SGADS 能够显著提高安全性能，在复杂城市场景中展现出强大的泛化能力，并提高智能车辆的训练效率。

具备强化学习和演示的安全且通用的端到端自动驾驶系统

Safe and Generalized end-to-end Autonomous Driving System with  Reinforcement Learning and Demonstrations

Multi-sensor fusion-based road segmentation plays an important role in the
intelligent driving system since it provides a drivable area. The existing
mainstream fusion method is mainly to feature fusion in the image space domain
which causes the perspective compression of the road and damages the
performance of the distant road. Considering the bird's eye views(BEV) of the
LiDAR remains the space structure in horizontal plane, this paper proposes a
bidirectional fusion network(BiFNet) to fuse the image and BEV of the point
cloud. The network consists of two modules: 1) Dense space transformation
module, which solves the mutual conversion between camera image space and BEV
space. 2) Context-based feature fusion module, which fuses the different
sensors information based on the scenes from corresponding features.This method
has achieved competitive results on KITTI dataset.

该研究提出了一种基于双向融合网络的多传感器融合道路分割方法，该方法利用摄像机成像空间和点云鸟瞰图空间之间的密集空间变换模块以及基于场景的特征融合模块，有效地解决了道路远景损失等问题，并在 KITTI 数据集上取得了有竞争力的结果。