改进卡莱拉模拟器中目标框生成，减少遮挡导致的误检。
Improve bounding box in Carla Simulator
- 基于传感器坐标系优化物体边界框对齐方式。
- 新方法有效过滤掉遮挡引起的虚假检测框。
- 适合自动驾驶数据集构建与算法测试场景。
CARLA模拟器（Car Learning to Act）是自动驾驶（AD）领域中测试算法和生成数据集的可靠平台，支持对多种环境参数的控制，便于全面评估。开发用的边界框在深度学习中广泛应用，并在自动驾驶中起关键作用。当前在CARLA模拟器中的主流数据生成方法是通过边界框标注感兴趣物体（如车辆）。该流程涉及获取地图上所有物体的坐标，再将其与自车传感器坐标系对齐，并以自车视角生成相对边界框。然而，此方法在目标检测与边界框标注中存在挑战，如产生“幽灵框”（ghost boxes）。尽管该方法在直接视线内能有效检测车辆等物体，但可能因遮挡而误检被遮挡物体。为此，本文改进了原有方法，旨在过滤掉不需要的边界框。性能分析表明，改进后的方法实现了高精度。
原文摘要 · Abstract (English)
The CARLA simulator (Car Learning to Act) serves as a robust platform for testing algorithms and generating datasets in the field of Autonomous Driving (AD). It provides control over various environmental parameters, enabling thorough evaluation. Development bounding boxes are commonly utilized tools in deep learning and play a crucial role in AD applications. The predominant method for data generation in the CARLA Simulator involves identifying and delineating objects of interest, such as vehicles, using bounding boxes. The operation in CARLA entails capturing the coordinates of all objects on the map, which are subsequently aligned with the sensor's coordinate system at the ego vehicle and then enclosed within bounding boxes relative to the ego vehicle's perspective. However, this primary approach encounters challenges associated with object detection and bounding box annotation, such as ghost boxes. Although these procedures are generally effective at detecting vehicles and other objects within their direct line of sight, they may also produce false positives by identifying objects that are obscured by obstructions. We have enhanced the primary approach with the objective of filtering out unwanted boxes. Performance analysis indicates that the improved approach has achieved high accuracy.
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