Leveraging Swarm Intelligence to Drive Autonomously: A Particle Swarm Optimization based Approach to Motion Planning

TL;DR

This paper introduces a modular, vehicle-agnostic motion planning pipeline using particle swarm optimization (PSO), enabling fast, adaptable, and safe autonomous navigation demonstrated through real-world shuttle deployments.

Contribution

The paper presents a novel PSO-based motion planning approach that is modular, adaptable to different vehicles and scenarios, and capable of real-time operation for autonomous driving.

Findings

Successfully deployed in autonomous shuttles over 3,500 km

Achieved fast planning cycles through parallel computation

Demonstrated safety and robustness in real-world traffic

Abstract

Motion planning is an essential part of autonomous mobile platforms. A good pipeline should be modular enough to handle different vehicles, environments, and perception modules. The planning process has to cope with all the different modalities and has to have a modular and flexible design. But most importantly, it has to be safe and robust. In this paper, we want to present our motion planning pipeline with particle swarm optimization (PSO) at its core. This solution is independent of the vehicle type and has a clear and simple-to-implement interface for perception modules. Moreover, the approach stands out for being easily adaptable to new scenarios. Parallel calculation allows for fast planning cycles. Following the principles of PSO, the trajectory planer first generates a swarm of initial trajectories that are optimized afterward. We present the underlying control space and inner workings. Finally, the application to real-world automated driving is shown in the evaluation with a deeper look at the modeling of the cost function. The approach is used in our automated shuttles that have already driven more than 3.500 km safely and entirely autonomously in sub-urban everyday traffic.