Deep-learning-based techniques have been widely adopted for autonomous
driving software stacks for mass production in recent years, focusing primarily
on perception modules, with some work extending this method to prediction
modules. However, the downstream planning and control modules are still
designed with hefty handcrafted rules, dominated by optimization-based methods
such as quadratic programming or model predictive control. This results in a
performance bottleneck for autonomous driving systems in that corner cases
simply cannot be solved by enumerating hand-crafted rules. We present a
deep-learning-based approach that brings prediction, decision, and planning
modules together with the attempt to overcome the rule-based methods'
deficiency in real-world applications of autonomous driving, especially for
urban scenes. The DNN model we proposed is solely trained with 10 hours of
human driver data, and it supports all mass-production ADAS features available
on the market to date. This method is deployed onto a Jiyue test car with no
modification to its factory-ready sensor set and compute platform. the
feasibility, usability, and commercial potential are demonstrated in this
article.

提出一种基于深度学习的方法，将预测、决策和规划模块融合起来，以克服自动驾驶系统中基于规则的方法在真实世界应用中的不足，特别是在城市场景中。所提出的 DNN 模型仅经过 10 小时的人工驾驶数据训练，并且支持市场上所有的批量生产 ADAS 功能。在此论文中，展示了该方法在不对车辆的传感器设置和计算平台进行任何修改的情况下，通过部署到集约的测试车辆上，展示了其可行性、可用性和商业潜力。