Perception, Distance Estimation, and Tracking Integration From BEV Representations

Engineering notes

Experimental results on the KITTI dataset demonstrate the effectiveness of the proposed method, achieving a Multiple Object Tracking Accuracy (MOTA) of 83.9% and Multiple Object Tracking Precision (MOTP) of 84.2%, while significantly reducing false positives and false negatives compared to baseline approaches.

Chinese explanation / 中文解读

中文解读待补充：本站会优先为端到端自动驾驶、BEV感知、3D目标检测、轨迹预测、路径规划、LiDAR感知等高价值论文补充中文说明。

Original abstract

Strong perception, precise distance estimate, and dependable object tracking are necessary for autonomous vehicle systems to function. To improve autonomous driving capabilities, we describe a unique deep learning-based fusion architecture in this paper that combines LiDAR and camera data using Birds-Eye-View (BEV) representations. The system design addresses three main problems: object detection, object distance estimate and multi-object tracking. We employ a sensor fusion technique for perception to enhance 3D object detection in a variety of environmental settings. A triangulation-based approach is used for distance estimation, and BEV transformations are used to calculate object distances precisely. To achieve improved tracking reliability even in the presence of occlusions and changing environments, we finally utilized a multi-object tracking (MOT) approach that combines 3D bounding boxes and BEV maps. Experimental results on the KITTI dataset demonstrate the effectiveness of the proposed method, achieving a Multiple Object Tracking Accuracy (MOTA) of 83.9% and Multiple Object Tracking Precision (MOTP) of 84.2%, while significantly reducing false positives and false negatives compared to baseline approaches. These findings underline the potential of our approach to advance the safety and efficiency of autonomous driving systems.

Links and sources

• Official / arXiv page
• PDF from original source

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