用速度场提供行驶方向与速度先验，简化复杂城市驾驶的路径规划。
Velocity Field: An Informative Traveling Cost Representation for Trajectory Planning
- 提出速度场表示法，为路径规划提供航向与速度先验
- 在真实数据集上表现优于学习型栅格代价图，更可靠高效
- 适合需要快速、稳定路径规划的自动驾驶系统
轨迹规划旨在生成未来一段时间内需遵循的一系列空间点。然而，由于驾驶环境复杂且不确定，自动驾驶车辆（AVs）难以全面设计规则以优化未来轨迹。为此，我们提出一种局部地图表示方法——速度场（Velocity Field），该方法为轨迹规划任务提供航向与速度先验，简化复杂城市驾驶中的规划过程。航向与速度先验可通过人类驾驶员示范数据，利用我们提出的损失函数进行学习。此外，我们开发了一种基于迭代采样的规划器，用于训练并比较不同局部地图表示之间的差异。我们在真实世界数据集上研究了多种局部地图表示形式对规划性能的影响。结果表明，相比学习型栅格代价图，本方法在可靠性与计算效率方面均表现更优。
原文摘要 · Abstract (English)
Trajectory planning involves generating a series of space points to be followed in the near future. However, due to the complex and uncertain nature of the driving environment, it is impractical for autonomous vehicles~(AVs) to exhaustively design planning rules for optimizing future trajectories. To address this issue, we propose a local map representation method called Velocity Field. This approach provides heading and velocity priors for trajectory planning tasks, simplifying the planning process in complex urban driving. The heading and velocity priors can be learned from demonstrations of human drivers using our proposed loss. Additionally, we developed an iterative sampling-based planner to train and compare the differences between local map representations. We investigated local map representation forms for planning performance on a real-world dataset. Compared to learned rasterized cost maps, our method demonstrated greater reliability and computational efficiency.
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