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Researchers at Carnegie Mellon University, Stanford University, and NVIDIA propose MPA to make end-to-end autonomous driving models safer in the real world End-to-end driving models look good in offline tests, but in closed-loop driving small errors stack up and cause failures. MPA (Model-Based Policy Adaptation) fixes this during deployment. MPA generates many counterfactual trajectories in a geometry-consistent simulator to expose the agent to situations beyond its training data. It trains a diffusion-based policy adapter to suggest candidate trajectories, and a multi-step Q model to pick the best one by expected utility. At run time, the system proposes several options and chooses the safest, highest-value action. On nuScenes with a photoreal closed-loop simulator, MPA improves robustness and safety across in-domain, out-of-domain, and safety-critical scenarios. Project: https://lnkd.in/ec8cuQC6 Paper: https://lnkd.in/ewui7u7c Authors: Haohong Lin, Yunzhi Zhang, Wenhao Ding, Jiajun Wu, Ding Zhao video credit: https://lnkd.in/ec8cuQC6 ‐-------------‐------------------------------------- Udacity’ fully online Master’s in AI*: https://lnkd.in/eTMFKN72 55% off -> code “BLACKFRIDAY55” * sponsored post that includes an affiliated link

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