A platform using flow matching for real-world image generation and an adversarial policy creates challenging corner cases to evaluate end-to-end autonomous driving models like UniAD and VAD, showing performance degradation.
arXiv preprint arXiv:2505.15880 (2025)
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SimScale synthesizes unseen driving states from real logs via neural rendering and reactive environments, generates pseudo-expert trajectories, and shows that co-training on real plus simulated data improves planning robustness and generalization on real benchmarks, with gains scaling by simulation
AFM is a novel gray-box adversarial attack using flow matching to create visually imperceptible perturbations that degrade performance of Vision-Language-Action and modular end-to-end autonomous driving models while showing strong cross-model transferability.
FocalAD adds an ego-local graph interactor and focal loss to prioritize decision-critical neighbors, yielding lower collision rates than prior methods on nuScenes, Bench2Drive, and especially the Adv-nuScenes robustness set.
A hybrid imitation-plus-reinforcement diffusion planner generates more diverse multi-mode trajectories for end-to-end autonomous driving, with a new diversity metric used for evaluation.
citing papers explorer
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Driving in Corner Case: A Real-World Adversarial Closed-Loop Evaluation Platform for End-to-End Autonomous Driving
A platform using flow matching for real-world image generation and an adversarial policy creates challenging corner cases to evaluate end-to-end autonomous driving models like UniAD and VAD, showing performance degradation.
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SimScale: Learning to Drive via Real-World Simulation at Scale
SimScale synthesizes unseen driving states from real logs via neural rendering and reactive environments, generates pseudo-expert trajectories, and shows that co-training on real plus simulated data improves planning robustness and generalization on real benchmarks, with gains scaling by simulation
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Adversarial Flow Matching for Imperceptible Attacks on End-to-End Autonomous Driving
AFM is a novel gray-box adversarial attack using flow matching to create visually imperceptible perturbations that degrade performance of Vision-Language-Action and modular end-to-end autonomous driving models while showing strong cross-model transferability.
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FocalAD: Local Motion Planning for End-to-End Autonomous Driving
FocalAD adds an ego-local graph interactor and focal loss to prioritize decision-critical neighbors, yielding lower collision rates than prior methods on nuScenes, Bench2Drive, and especially the Adv-nuScenes robustness set.
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DIVER: Reinforced Diffusion Breaks Imitation Bottlenecks in End-to-End Autonomous Driving
A hybrid imitation-plus-reinforcement diffusion planner generates more diverse multi-mode trajectories for end-to-end autonomous driving, with a new diversity metric used for evaluation.