Current end-to-end autonomous driving methods either run a controller based on a planned trajectory or perform control prediction directly, which have spanned two separately studied lines of research. Seeing their potential mutual benefits to each other, this paper takes the initiative to explore the combination of these two well-developed worlds. Specifically, our integrated approach has two branches for trajectory planning and direct control, respectively. The trajectory branch predicts the future trajectory, while the control branch involves a novel multi-step prediction scheme such that the relationship between current actions and future states can be reasoned. The two branches are connected so that the control branch receives corresponding guidance from the trajectory branch at each time step. The outputs from two branches are then fused to achieve complementary advantages. Our results are evaluated in the closed-loop urban driving setting with challenging scenarios using the CARLA simulator. Even with a monocular camera input, the proposed approach ranks $first$ on the official CARLA Leaderboard, outperforming other complex candidates with multiple sensors or fusion mechanisms by a large margin. The source code and data will be made publicly available at https://github.com/OpenPerceptionX/TCP.
翻译:目前端到端的自主驾驶方法或是根据计划轨迹运行一个控制器,或直接进行控制预测,这些预测跨越了两个分别研究的研究线。看这两条研究线的潜在互利关系,本文件采取主动探索这两个发达世界的组合。具体地说,我们的综合方法有两分支分别用于轨迹规划和直接控制。轨迹分支预测未来轨迹,而控制分支则采用新的多步预测办法,这样可以说明当前行动与未来国家之间的关系。两个分支相互连接,以便控制分支每一步都从轨迹分支获得相应的指导。两个分支的产出随后结合起来,以取得互补优势。我们的结果在闭路城市驾驶环境中用CARLA模拟器来评估具有挑战性的情况。即使使用单向摄像器输入,拟议的方法在正式的CARLA领头板上也排名第1美元,比其他有多种传感器或聚合机制的功能复杂候选人高出大幅度。源码和数据将在https://github.com/ OmpenPercepionX/CP上公开提供。