MVAdapt conditions end-to-end autonomous driving policies on explicit vehicle physics to achieve better zero-shot transfer and few-shot calibration across different vehicles in CARLA simulation.
How simulation helps autonomous driving: A survey of sim2real, digital twins, and parallel intelligence,
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A DRL controller for ASV floating waste capture, trained in simulation with a perception abstraction module, achieves centimeter-level accuracy in real-world field experiments across 14 disturbance regimes.
citing papers explorer
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MVAdapt: Zero-Shot Multi-Vehicle Adaptation for End-to-End Autonomous Driving
MVAdapt conditions end-to-end autonomous driving policies on explicit vehicle physics to achieve better zero-shot transfer and few-shot calibration across different vehicles in CARLA simulation.
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Sim-to-Real Transfer and Robustness Evaluation of Reinforcement Learning Control with Integrated Perception on an ASV for Floating Waste Capture
A DRL controller for ASV floating waste capture, trained in simulation with a perception abstraction module, achieves centimeter-level accuracy in real-world field experiments across 14 disturbance regimes.