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/.

翻译：在新的情景下,自驾车辆的传统决定和规划框架规模在新的情景下差强人意,因此需要对规则和参数进行繁琐的手动调整,以便在所有可预见的情况下保持可接受的性能。最近,基于深层学习的自驾式方法显示了有希望的成果,提高了一般化能力,但减少了手工工程工作。然而,以前多数基于学习的方法在有限的驾驶情景下得到了培训和评价,任务分散,如行车道随行、自动制动和有条件驾驶等。本文建议建立一个基于图表的深层网络,以实现可变缩的自驾式,能够处理大规模交通情况。具体地说,7 000多公里的评估是在高纤维驾驶模拟器下进行的,在这个模拟器中,我们的方法可以遵守交通规则,在广泛的城市、农村和公路环境中安全驾驶车辆,包括无保护的左转、狭窄道路、环绕路和行人路富人路交叉路。示范录像可在https://caipeide.github.io/dignet/上查阅。