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.

翻译：近年来，基于深度学习的技术已被广泛应用于量产自动驾驶软件栈，主要聚焦于感知模块，部分研究将此方法扩展至预测模块。然而，下游的规划与控制模块仍依赖大量手工规则设计，以优化方法（如二次规划或模型预测控制）为主导。这导致自动驾驶系统在极端场景下出现性能瓶颈，因为此类问题无法通过枚举手工规则解决。我们提出一种基于深度学习的方法，将预测、决策和规划模块整合，旨在克服基于规则的方法在自动驾驶实际应用（尤其是城市场景）中的不足。我们提出的深度神经网络模型仅使用10小时的人类驾驶员数据进行训练，并支持当前市场上所有量产高级驾驶辅助系统功能。该方法已部署至极越测试车，未对出厂传感器组与计算平台进行任何修改。本文验证了该方法的可行性、可用性及商业潜力。