Velocity Field: An Informative Traveling Cost Representation for Trajectory Planning

TL;DR

This paper introduces Velocity Field, a local map representation that provides heading and velocity priors for autonomous vehicle trajectory planning, improving reliability and efficiency in complex urban environments.

Contribution

The paper presents Velocity Field, a novel local map representation that learns heading and velocity priors from demonstrations to enhance trajectory planning for AVs.

Findings

Velocity Field outperforms rasterized cost maps in reliability.

It offers greater computational efficiency.

It simplifies trajectory planning in complex urban scenarios.

Abstract

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.