Quantify Transformer for End-to-End BEV Scene Understanding

Engineering notes

Key topics: autonomous driving system, autonomous driving, bev, end-to-end, trajectory prediction, lidar, nuscenes, perception, prediction. See the paper for implementation details and experimental results.

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

Accurate 3D perception is crucial for autonomous driving systems. In this paper, a novel lightweight bird’s-eye view (BEV) architecture is introduced, which fully utilizes the camera data and aims to improve the efficiency of perspective conversion and feature representation. The proposed method use a Transformer-like perspective transformation module to efficiently convert camera images into a unified BEV representation, which enables 3D perception including detection, semantic maps in dynamic scenes, and trajectory prediction of surrounding vehicles without relying on LiDAR or other sensors. Experimental results of our proposed model on the nuScenes dataset show improved accuracy and efficiency in integrated 3D perception tasks using camera-only inputs. The ablation study further emphasizes the scalability and adaptability of the framework for real-time 3D perception and environment understanding in complex driving situations.

Links and sources

• Official / arXiv page

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