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One of the main challenges in autonomous racing is to design algorithms formotion planning at high speed, and across complex racing courses. End-to-endtrajectory synthesis has been previously proposed where the trajectory for theego vehicle is computed based on camera images from the racecar. This is donein a supervised learning setting using behavioral cloning techniques. In thispaper, we address the limitations of behavioral cloning methods for trajectorysynthesis by introducing Differential Bayesian Filtering (DBF), which usesprobabilistic B\'ezier curves as a basis for inferring optimal autonomousracing trajectories based on Bayesian inference. We introduce a trajectorysampling mechanism and combine it with a filtering process which is able topush the car to its physical driving limits. The performance of DBF isevaluated on the DeepRacing Formula One simulation environment and comparedwith several other trajectory synthesis approaches as well as human drivingperformance. DBF achieves the fastest lap time, and the fastest speed, bypushing the racecar closer to its limits of control while always remaininginside track bounds.

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