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A Closed-Loop Evaluation of Capability Loss and Recovery in Compressed Driving Policies

arXiv · AI, language, vision and robotics · article · Sep 1, 2026 · UTC

Many automobile and mobility companies deploy learned driving policies on embedded computers with limited memory and power. Pruning, knowledge distillation, and quantization are the standard methods to reduce the size and the inference cost of these policies. However, these methods are commonly assessed by aggregate numerical scores, and such scores may not reflect the ability of the policy to drive safely when interacting with other road users. In this study, we propose a stage-wise closed-loop evaluation approach to follow a driving policy through a compression pipeline. We formulate the dri

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