Autonomous driving approaches today are mainly based on perception-planning-action modular pipelines and the End2End paradigm respectively. The End2End paradigm is a strategy that directly maps raw sensor data to vehicle control actions. This strategy is very promising and appealing because complex module design and cumbersome data labeling are avoided. Since this approach lacks a degree of interpretability, safety and practicability. we propose an autonomous driving approach based on trajectory learning using deep neural networks in this paper. In comparison to End2End algorithm, it is found that the trajectory learning algorithm performs better in autonomous driving. As for trajectory learning algorithm, the CNN_Raw-RNN network structure is established, which is verified to be more effective than the original CNN_LSTM network structure. Besides, we propose an autonomous driving architecture of a pilot and copilot combination. The pilot is responsible for trajectory prediction via imitation learning with labeled driving trajectories, while the copilot is a safety module that is employed to verify the effectiveness of the vehicle trajectory by the results of the semantic segmentation auxiliary task. The proposed autonomous driving architecture is verified with a real car on urban roads without manual intervention within 40 km.

今天的自动驾驶方法主要分别基于感知-规划-动作模块化管道和 End2End 范式。End2End 范式是一种将原始传感器数据直接映射到车辆控制动作的策略。这种策略非常有前途和吸引力，因为避免了复杂的模块设计和繁琐的数据标记。由于这种方法缺乏一定程度的可解释性、安全性和实用性。我们在本文中提出了一种基于使用深度神经网络的轨迹学习的自动驾驶方法。与End2End算法相比，发现轨迹学习算法在自动驾驶中表现更好。对于轨迹学习算法，建立了CNN_Raw-RNN网络结构，经验证比原来的CNN_LSTM网络结构更有效。除了，我们提出了飞行员和副驾驶组合的自动驾驶架构。飞行员负责通过带有标记的驾驶轨迹的模仿学习进行轨迹预测，而副驾驶是一个安全模块，用于通过语义分割辅助任务的结果来验证车辆轨迹的有效性。所提出的自动驾驶架构在 40 公里范围内通过城市道路上的真实汽车进行验证，无需人工干预。