This is the Way: Differential Bayesian Filtering for Agile Trajectory Synthesis

TL;DR

This paper introduces Differential Bayesian Filtering (DBF), a probabilistic approach for high-speed autonomous racing trajectory synthesis that outperforms existing methods and human drivers in simulation.

Contribution

The paper presents a novel Bayesian filtering method using probabilistic Bézier curves for autonomous racing trajectory planning, addressing behavioral cloning limitations.

Findings

DBF achieves the fastest lap times in simulation.

DBF pushes the vehicle closer to control limits safely.

Compared to other methods, DBF outperforms human drivers in the simulation.

Abstract

One of the main challenges in autonomous racing is to design algorithms for motion planning at high speed, and across complex racing courses. End-to-end trajectory synthesis has been previously proposed where the trajectory for the ego vehicle is computed based on camera images from the racecar. This is done in a supervised learning setting using behavioral cloning techniques. In this paper, we address the limitations of behavioral cloning methods for trajectory synthesis by introducing Differential Bayesian Filtering (DBF), which uses probabilistic B\'ezier curves as a basis for inferring optimal autonomous racing trajectories based on Bayesian inference. We introduce a trajectory sampling mechanism and combine it with a filtering process which is able to push the car to its physical driving limits. The performance of DBF is evaluated on the DeepRacing Formula One simulation environment and compared with several other trajectory synthesis approaches as well as human driving performance. DBF achieves the fastest lap time, and the fastest speed, by pushing the racecar closer to its limits of control while always remaining inside track bounds.