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arXiv · 2211.12111

Evaluation of MPC-based Imitation Learning for Human-like Autonomous Driving

Flavia Sofia Acerbo·Jan Swevers·Tinne Tuytelaars·Tong Duy Son
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Abstract

This work evaluates and analyzes the combination of imitation learning (IL) and differentiable model predictive control (MPC) for the application of human-like autonomous driving. We combine MPC with a hierarchical learning-based policy, and measure its performance in open-loop and closed-loop with metrics related to safety, comfort and similarity to human driving characteristics. We also demonstrate the value of augmenting open-loop behavioral cloning with closed-loop training for a more robust learning, approximating the policy gradient through time with the state space model used by the MPC. We perform experimental evaluations on a lane keeping control system, learned from demonstrations collected on a fixed-base driving simulator, and show that our imitative policies approach the human driving style preferences.

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Flavia Sofia Acerbo, Jan Swevers, Tinne Tuytelaars, Tong Duy Son. 2022-11-22. Evaluation of MPC-based Imitation Learning for Human-like Autonomous Driving. https://arxiv.org/abs/2211.12111

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