IA Planner: Motion Planning Using Instantaneous Analysis for Autonomous Vehicle in the Dense Dynamic Scenarios on Highways

TL;DR

This paper introduces IA Planner, a novel motion planning method for autonomous vehicles that analyzes collision relationships instantaneously in dense highway scenarios, improving safety, efficiency, and comfort.

Contribution

It proposes an instantaneous collision analysis model that reduces computational complexity and enhances lane-changing trajectory planning in dense dynamic traffic.

Findings

Successfully plans safe lane-changing trajectories in dense traffic

Reduces computational complexity by projecting 3D constraints to 2D

Improves traffic efficiency and ride comfort

Abstract

In dense and dynamic scenarios, planning a safe and comfortable trajectory is full of challenges when traffic participants are driving at high speed. The classic graph search and sampling methods first perform path planning and then configure the corresponding speed, which lacks a strategy to deal with the high-speed obstacles. Decoupling optimization methods perform motion planning in the S-L and S-T domains respectively. These methods require a large free configuration space to plan the lane change trajectory. In dense dynamic scenes, it is easy to cause the failure of trajectory planning and be cut in by others, causing slow driving speed and bring safety hazards. We analyze the collision relationship in the spatio-temporal domain, and propose an instantaneous analysis model which only analyzes the collision relationship at the same time. In the model, the collision-free constraints in 3D spatio-temporal domain is projected to the 2D space domain to remove redundant constraints and reduce computational complexity. Experimental results show that our method can plan a safe and comfortable lane-changing trajectory in dense dynamic scenarios. At the same time, it improves traffic efficiency and increases ride comfort.