用跨视图变换器预测鸟瞰图，提升自动驾驶行为克隆的闭环表现。
Closed-Loop Evaluation of Bird's-Eye-View Maps from Cross-View Transformers as Inputs to Behavior-Cloning Policies
- 用跨视图变换器生成六通道鸟瞰图作为策略输入。
- 加权损失让转弯和交叉口等关键区域预测更准，实现无违规完整行驶。
- 适合关注真实场景下视觉输入对驾驶性能影响的研究者。
在自动驾驶中，鸟瞰图（BEV）提供车辆周围环境的结构化俯视抽象，已成为行为克隆（BC）策略的关键输入模态。尽管仿真环境中可获得真值BEV地图，但真实部署需用摄像头预测的对应地图替代——这一替换引入感知误差，其对闭环驾驶性能的下游影响尚不明确。本文研究在CARLA模拟器中，将跨视图变换器（CVT）预测的BEV地图直接作为BC代理的输入。我们提出一种六通道BEV表示，涵盖道路表面、规划路径、车道线、车辆、行人和交通灯，并引入核密度估计（KDE）加权方案，重新平衡分割损失，使曲率与交叉口等少数驾驶行为得到更好优化。在两个CARLA城镇的闭环评估中，采用KDE加权的模型是唯一能全程无违规完成任务的预测型BEV代理，尽管其总体交并比（IoU）并非最高。这一差异表明，全局分割指标无法有效反映驾驶表现：导航成功的关键在于几何关键位置的预测精度，而路径通道成为在预测BEV输入下可靠导航的主要瓶颈。
原文摘要 · Abstract (English)
In autonomous driving, Bird's-Eye View (BEV) representations provide a structured, top-down abstraction of the vehicle's surroundings and have become a key input modality for Behavioral Cloning (BC) policies. While ground-truth BEV maps are readily available in simulation, real-world deployment requires replacing them with camera-predicted counterparts - a substitution that introduces perceptual errors whose downstream impact on closed-loop driving performance is not well understood. In this work, we investigate the use of Cross-View Transformer (CVT)-predicted BEV maps as direct policy inputs for a BC agent in the CARLA simulator. We propose a six-channel BEV representation covering road surface, planned route, lane boundaries, vehicles, pedestrians, and traffic lights, and introduce a Kernel Density Estimation (KDE) weighting scheme that rebalances the segmentation loss towards underrepresented driving maneuvers such as curves and intersections. Closed-loop evaluation across two CARLA towns shows that the KDE-weighted model is the only predicted-BEV agent to complete a full episode without infractions, despite not achieving the highest aggregate IoU. This discrepancy reveals that global segmentation metrics are poor proxies for driving performance: what determines navigation success is prediction quality at geometrically critical locations, and the route channel emerges as the primary bottleneck for reliable agent navigation under predicted BEV inputs.
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