发表机构
Federal University of Santa Catarina（圣卡塔琳娜联邦大学）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
本文提出Diffusion-2BC，结合扩散去噪与辅助回归损失，提升自动驾驶离线行为克隆的闭环可靠性，同时保持多模态预测，在Claw和CARLA任务中显著降低误差并延长行驶距离。
AI 中文摘要
行为克隆为自动驾驶策略学习提供了一条离线途径，但均方误差回归与演示数据（其中同一观测对应多个有效动作）的匹配效果不佳。扩散策略能够表示条件多模态动作分布，然而当视觉特征和控制从有限数据中学习时，其闭环性能可能不稳定。本文提出Diffusion-2BC，该方法在共享视觉编码器上结合了扩散去噪目标与辅助确定性行为克隆损失。辅助分支仅在训练期间使用；推理仍基于扩散。所提方法在受控的Claw环境和鸟瞰视角CARLA导航中进行了评估，包括路线条件驾驶、穿越多个交叉路口的无路线导航，以及从Town01到Town02的跨地图评估。在Claw任务中，与基于扩散的行为克隆基线相比，Diffusion-2BC将平均掩码距离误差降低了约10%，与标准确定性行为克隆相比降低了85%。在无路线CARLA中，根据评估协议，Diffusion-2BC在Town01和Town02中都比两个基线在终止前行驶了更远的距离。额外的定性滚动展示了不同的路线选择，显示了所提基于扩散的智能体的多模态行为。结果表明，辅助回归信号可以提高扩散行为克隆的闭环可靠性，同时在受控基准中保持多模态预测。
英文摘要
Behavior cloning provides an offline route to autonomous-driving policy learning, but mean-squared-error regression is poorly matched to demonstrations in which one observation admits several valid actions. Diffusion policies can represent conditional multimodal action distributions, yet their closed-loop performance may be unstable when visual features and control are learned from limited data. This paper presents Diffusion-2BC, which combines a diffusion denoising objective with an auxiliary deterministic behavior-cloning loss over a shared visual encoder. The auxiliary branch is used only during training; inference remains diffusion-based. The proposed method is evaluated in the controlled Claw environment and in bird's-eye-view CARLA navigation, including route-conditioned driving, route-free navigation through multiple intersections, and cross-map evaluation from Town01 to Town02. In the Claw task, Diffusion-2BC reduced the mean mask-distance error by approximately 10% relative to a diffusion-based behavior-cloning baseline and by 85% relative to standard deterministic behavior cloning. In route-free CARLA, Diffusion-2BC traveled substantially farther before termination under the evaluation protocol than both baselines in Town01 and Town02. Additional qualitative rollouts revealed distinct route choices, showing the multimodal behavior of the proposed diffusion-based agent. The results indicate that an auxiliary regression signal can improve the closed-loop reliability of diffusion behavior cloning while preserving multimodal prediction in the controlled benchmark.
