Traditional modular self-driving frameworks scale poorly in new scenarios, which usually require tedious hand-tuning of rules and parameters to maintain acceptable performance in all foreseeable occasions. Therefore, robust and safe self-driving using traditional frameworks is still challenging, especially in complex and dynamic environments. Recently, deep-learning based self-driving methods 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. The results also show that our method achieves better performance over the baselines in terms of success rate. This work is accompanied with some demonstration videos which are available at https://sites.google.com/view/dignet-self-driving/video-clips/
翻译:在新的情景中,传统的模块式自行驾驶框架规模较差,在新的情景中,通常需要为在所有可预见的场合保持可接受的业绩而对规则和参数进行烦琐的手动调整,因此,使用传统框架进行稳健和安全的自行驾驶仍然具有挑战性,特别是在复杂和动态的环境中。最近,基于深层学习的自行驾驶方法显示出了令人乐观的成果,提高了普及能力,但减少了手工工程工作。然而,以往大多数基于学习的方法在有限的驱动情景中经过训练和评价,任务分散,例如绕道、自动制动和有条件驾驶。在本文件中,我们提议建立一个基于图表的深度网络,以实现可缩放的自我驾驶,从而能够处理大规模交通情况。具体地说,7 000多公里的评价是在高不动性驱动模拟器下进行的,在这个模拟器中,我们的方法可以遵守交通规则,安全地在城市、农村和公路环境,包括不受保护的左转、狭小道路、环绕路和行人与富人路交叉路。结果还表明,我们的方法在成功率基线上取得了更好的业绩。这项工作附有一些演示/图式/图式/图上可以使用。