Int2Planner: An Intention-based Multi-modal Motion Planner for Integrated Prediction and Planning

TL;DR

Int2Planner is a novel intention-based multi-modal motion planning framework for autonomous driving that integrates prediction and planning using route intentions, achieving state-of-the-art results and real-world deployment.

Contribution

It introduces route intention points for multi-modal planning, effectively integrating prediction and planning in autonomous driving.

Findings

Achieves state-of-the-art performance on nuPlan benchmark

Successfully deployed in real-world urban driving scenarios

Effectively models interactions with traffic environment

Abstract

Motion planning is a critical module in autonomous driving, with the primary challenge of uncertainty caused by interactions with other participants. As most previous methods treat prediction and planning as separate tasks, it is difficult to model these interactions. Furthermore, since the route path navigates ego vehicles to a predefined destination, it provides relatively stable intentions for ego vehicles and helps constrain uncertainty. On this basis, we construct Int2Planner, an \textbf{Int}ention-based \textbf{Int}egrated motion \textbf{Planner} achieves multi-modal planning and prediction. Instead of static intention points, Int2Planner utilizes route intention points for ego vehicles and generates corresponding planning trajectories for each intention point to facilitate multi-modal planning. The experiments on the private dataset and the public nuPlan benchmark show the effectiveness of route intention points, and Int2Planner achieves state-of-the-art performance. We also deploy it in real-world vehicles and have conducted autonomous driving for hundreds of kilometers in urban areas. It further verifies that Int2Planner can continuously interact with the traffic environment. Code will be avaliable at https://github.com/cxlz/Int2Planner.