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Autonomous racing with scaled race cars has gained increasing attention as aneffective approach for developing perception, planning and control algorithmsfor safe autonomous driving at the limits of the vehicle's handling. To trainagile control policies for autonomous racing, learning-based approaches largelyutilize reinforcement learning, albeit with mixed results. In this study, webenchmark a variety of imitation learning policies for racing vehicles that areapplied directly or for bootstrapping reinforcement learning both in simulationand on scaled real-world environments. We show that interactive imitationlearning techniques outperform traditional imitation learning methods and cangreatly improve the performance of reinforcement learning policies bybootstrapping thanks to its better sample efficiency. Our benchmarks provide afoundation for future research on autonomous racing using Imitation Learningand Reinforcement Learning.

A Benchmark Comparison of Imitation Learning-based Control Policies for Autonomous Racing