A Real-time Spatio-Temporal Trajectory Planner for Autonomous Vehicles with Semantic Graph Optimization

TL;DR

This paper introduces a real-time trajectory planning method for autonomous vehicles that leverages semantic spatio-temporal graphs to efficiently navigate complex urban environments.

Contribution

It presents a novel graph optimization approach utilizing semantic maps to improve real-time trajectory planning in urban settings.

Findings

Effective handling of complex urban scenarios

Real-time trajectory generation demonstrated

Code will be released for benchmarking

Abstract

Planning a safe and feasible trajectory for autonomous vehicles in real-time by fully utilizing perceptual information in complex urban environments is challenging. In this paper, we propose a spatio-temporal trajectory planning method based on graph optimization. It efficiently extracts the multi-modal information of the perception module by constructing a semantic spatio-temporal map through separation processing of static and dynamic obstacles, and then quickly generates feasible trajectories via sparse graph optimization based on a semantic spatio-temporal hypergraph. Extensive experiments have proven that the proposed method can effectively handle complex urban public road scenarios and perform in real time. We will also release our codes to accommodate benchmarking for the research community