MVAdapt: Zero-Shot Multi-Vehicle Adaptation for End-to-End Autonomous Driving
For autonomous driving researchers, MVAdapt provides a method to improve model transferability across different vehicle types, addressing a practical deployment bottleneck.
MVAdapt addresses the vehicle-domain gap in end-to-end autonomous driving by conditioning policies on vehicle physics, achieving strong zero-shot transfer to unseen vehicles and data-efficient few-shot calibration for outliers in the CARLA Leaderboard 1.0 benchmark.
End-to-End (E2E) autonomous driving models are usually trained and evaluated with a fixed ego-vehicle, even though their driving policy is implicitly tied to vehicle dynamics. When such a model is deployed on a vehicle with different size, mass, or drivetrain characteristics, its performance can degrade substantially; we refer to this problem as the vehicle-domain gap. To address it, we propose MVAdapt, a physics-conditioned adaptation framework for multi-vehicle E2E driving. MVAdapt combines a frozen TransFuser++ scene encoder with a lightweight physics encoder and a cross-attention module that conditions scene features on vehicle properties before waypoint decoding. In the CARLA Leaderboard 1.0 benchmark, MVAdapt improves over naive transfer and multi-embodiment adaptation baselines on both in-distribution and unseen vehicles. We further show two complementary behaviors: strong zero-shot transfer on many unseen vehicles, and data-efficient few-shot calibration for severe physical outliers. These results suggest that explicitly conditioning E2E driving policies on vehicle physics is an effective step toward more transferable autonomous driving models. All codes are available at https://github.com/hae-sung-oh/MVAdapt