Title: Improve bounding box in Carla Simulator Show Chinese title

Title: 改进Carla模拟器中的边界框

Abstract: 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. Show Chinese abstract

Abstract: CARLA模拟器（Car Learning to Act）作为一个强大的平台，用于测试算法和生成自动驾驶（AD）领域的数据集。它提供了对各种环境参数的控制，使评估更加全面。开发边界框是深度学习中常用的工具，在AD应用中起着关键作用。CARLA模拟器中的数据生成主要方法涉及识别和描绘感兴趣的物体，如车辆，使用边界框。CARLA中的操作包括捕获地图上所有物体的坐标，这些坐标随后与自车传感器的坐标系对齐，然后根据自车视角被封装在边界框内。然而，这种主要方法在对象检测和边界框标注方面遇到了诸如鬼影框等挑战。尽管这些过程通常在检测其直接视野内的车辆和其他物体方面是有效的，但它们也可能因识别被障碍物遮挡的物体而产生误报。我们改进了主要方法，以过滤掉不需要的边界框。性能分析表明，改进的方法已经实现了高精度。