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广泛应用于量产自动驾驶软件栈，主要集中在感知模块，部分研究将该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人工规则设计，主要采用基于优化的方法，如二次规划或模型预测控制。这导致自动驾驶系统存在性能瓶颈：极端场景无法通过枚举人工规则来解决。本文提出一种基于深度学习的方法，将预测、决策与规划模块整合，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DNN模型仅使用10小时人类驾驶数据进行训练，并支持目前市场上所有量产的ADAS功能。该方法已部署于极越测试车辆，未对其原厂传感器配置与计算平台进行任何改动。本文论证了该方法的可行性、可用性与商业潜力。