BranchDrive：用于动作条件驾驶预测的分支结构数据集
BranchDrive: A Branch-Structured Dataset for Action-Conditioned Driving Prediction
- University of Michigan-Dearborn（密歇根大学迪尔伯恩分校）
机构由 AI 辅助整理，请以论文原文为准。
AI总结：
BranchDrive是一个分支结构数据集，通过干预未来配对支持动作条件驾驶预测，其结构化模型显著优于基线，但保守执行问题仍未解决。
AI中文摘要：
大多数自动驾驶数据集仅记录行为策略执行的动作及其后续单一未来，为比较替代自我决策提供的监督有限。我们引入了BranchDrive，一个分支结构的CARLA数据集和基准，它将一个典型决策前历史与一个名义专家未来及十二个物理执行的干预未来配对，涵盖加速、制动以及左、右转向策略，每种策略有三个幅度。每个干预持续2.5秒，随后由专家恢复。经过控制合规性、模态完整性、重放保真度和动作泄漏审计后，冻结基准包含606个独立分支组和7,878条关联轨迹。我们评估了六个连续短时域结果和十步自我轨迹的预测，使用仅动作、仅历史、结构化、视觉、多模态和特权鸟瞰图模型。在保留测试集上，结构化历史与动作模型实现了宏归一化平均绝对误差0.5036和平均位移误差2.2042米，显著优于受限基线。在全信息离线评估中，其基于结果的选通器将平衡策略值从0.5364提高到0.5704，并将归一化遗憾从0.2674降低至0.1495（相对于冻结动作先验）。然而，验证校准的最小间隔防护拒绝了所有干预，表明保守执行仍未解决。因此，BranchDrive支持动作条件短时域预测和固定库离线决策评估，但未建立精确因果效应、二元安全预测或闭环安全改进。
英文摘要：
Most autonomous-driving datasets record only the action executed by a behavior policy and the single future that followed, providing limited supervision for comparing alternative ego decisions. We introduce BranchDrive, a branch-structured CARLA dataset and benchmark that pairs one canonical pre-decision history with one nominal expert future and twelve physically executed intervention futures spanning acceleration, braking, and left- and right-steering policies at three magnitudes. Each intervention lasts 2.5 s and is followed by expert recovery. Following control-compliance, modality-completeness, replay-fidelity, and action-leakage audits, the frozen benchmark contains 606 independent branch groups and 7,878 associated trajectories. We evaluate prediction of six continuous short-horizon outcomes and a ten-step ego trajectory using action-only, history-only, structured, visual, multimodal, and privileged bird's-eye-view models. On the held-out test split, the structured history-and-action model achieves a macro normalized mean absolute error of 0.5036 and an average displacement error of 2.2042 m, significantly outperforming both restricted baselines. In full-information offline evaluation, its outcome-derived selector increases balanced policy value from 0.5364 to 0.5704 and reduces normalized regret from 0.2674 to 0.1495 relative to the frozen action prior. However, a validation-calibrated minimum-separation guard rejects every intervention, showing that conservative execution remains unresolved. BranchDrive therefore supports action-conditioned short-horizon prediction and fixed-bank offline decision evaluation, but does not establish exact causal effects, binary safety prediction, or closed-loop safety improvement.
