论文标题
Dignet：通过图神经网络的通用交通情况学习可扩展的自动驾驶政策
DiGNet: Learning Scalable Self-Driving Policies for Generic Traffic Scenarios with Graph Neural Networks
论文作者
论文摘要
在新场景中，自动驾驶汽车（SDV）的传统决策和规划框架的规模较差，因此它们需要对规则和参数进行乏味的手工调整，以在所有可预见的情况下保持可接受的性能。最近，基于深度学习的自动驾驶方法已显示出令人鼓舞的结果，具有更好的概括能力，但手工工程的努力较少。但是，在有限的驾驶场景中，对以前的大多数基于学习的方法进行了培训和评估，该方案具有分散的任务，例如遵循车道，自动制动和有条件驾驶。在本文中，我们提出了一个基于图形的深网，以实现可扩展的自动驾驶，以处理大规模的交通情况。具体而言，在高保真驾驶模拟器中进行了超过7,000公里的评估，我们的方法可以遵守交通规则，并在各种各样的城市，农村和高速公路环境中安全地驾驶车辆，包括未受保护的左转弯，狭窄的道路，noar绕道路，环形交叉路口，行人和行人富含富裕的交叉点。演示视频可在https://caipeide.github.io/dignet/上找到。
Traditional decision and planning frameworks for self-driving vehicles (SDVs) scale poorly in new scenarios, thus they require tedious hand-tuning of rules and parameters to maintain acceptable performance in all foreseeable cases. Recently, self-driving methods based on deep learning have shown promising results with better generalization capability but less hand engineering effort. However, most of the previous learning-based methods are trained and evaluated in limited driving scenarios with scattered tasks, such as lane-following, autonomous braking, and conditional driving. In this paper, we propose a graph-based deep network to achieve scalable self-driving that can handle massive traffic scenarios. Specifically, more than 7,000 km of evaluation is conducted in a high-fidelity driving simulator, in which our method can obey the traffic rules and safely navigate the vehicle in a large variety of urban, rural, and highway environments, including unprotected left turns, narrow roads, roundabouts, and pedestrian-rich intersections. Demonstration videos are available at https://caipeide.github.io/dignet/.
