让自动驾驶模型零样本适配不同车辆，提升泛化能力
MVAdapt: Zero-Shot Multi-Vehicle Adaptation for End-to-End Autonomous Driving
- 用物理属性条件化场景特征，实现跨车型自适应
- 在CARLA基准上对未见车辆实现强零样本迁移
- 支持少量数据微调，适配极端物理差异车辆
端到端自动驾驶模型通常在固定主车下训练和评估，尽管其驾驶策略隐式依赖于车辆动力学。当部署到尺寸、质量或传动系统不同的车辆时，性能可能大幅下降；我们称此为车辆域差距。为此，提出MVAdapt，一种面向多车辆的物理条件化自适应框架。该框架结合冻结的TransFuser++场景编码器、轻量级物理编码器及交叉注意力模块，在路径点解码前将场景特征基于车辆属性进行条件化。在CARLA Leaderboard 1.0基准中，MVAdapt在分布内与未见车辆上均优于基线方法。进一步展示两种互补行为：对多数未见车辆实现强零样本迁移，以及对严重物理异常车辆进行数据高效的少样本校准。结果表明，显式地将端到端驾驶策略基于车辆物理建模，是提升可迁移性的有效路径。所有代码已公开于https://github.com/hae-sung-oh/MVAdapt。
原文摘要 · Abstract (English)
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
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