论文标题
端到端自动驾驶的轨迹引导的控制预测：一个简单而强大的基线
Trajectory-guided Control Prediction for End-to-end Autonomous Driving: A Simple yet Strong Baseline
论文作者
论文摘要
当前的端到端自主驾驶方法要么基于计划的轨迹运行控制器，要么直接执行控制预测，这已经跨越了两条单独研究的研究线。本文看到了它们彼此的潜在相互利益，主动探讨了这两个发达世界的结合。具体而言，我们的集成方法分别有两个用于轨迹计划和直接控制的分支。轨迹分支可以预测未来的轨迹，而控制分支则涉及一种新型的多步预测方案，以便可以将当前行动与未来状态之间的关系进行推理。连接两个分支，以便控制分支在每个时间步骤中从轨迹分支接收相应的指导。然后将来自两个分支的输出融合以实现互补的优势。我们的结果在闭环城市驾驶环境中进行了评估，并使用CARLA模拟器充满挑战的情况。即使有了单眼相机的输入，提议的方法在官方的Carla排行榜上排名第一，以大幅度的利润率优于其他具有多个传感器或融合机制的复杂候选者。源代码可在https://github.com/openperceptionx/tcp上公开获得
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 is publicly available at https://github.com/OpenPerceptionX/TCP
