A Novel Deep Learning Approach for Multi-Sensor Fusion in Autonomous Vehicle Perception

Engineering notes

Key topics: autonomous driving, autonomous vehicle, lidar, sensor fusion, multi-sensor fusion, radar, perception. 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

An effective autonomous driving perception system must function well in real-world settings. In order to enhance situational robustness and environmental perception, LiDAR, radar, and camera data must be combined. This study examines the ongoing challenges in multi-sensor fusion. First, create a deep learning-based fusion framework that can manage the diverse spaces, timings, and semantics of the multiple sensors in a systematic manner. In order to implement attention-based fusion, adaptively extract features, and dynamically estimate uncertainty in the perception pipeline for context-aware decision-making, a new structure has been developed. Perform multi-stage attention weighting and cross-modal integration after methodically encoding and aligning each sensor stream separately. Experiments using a large public dataset have demonstrated that the suggested approach is more suited for real-world autonomous driving scenarios. The new framework is still a real-time system with minimal latency and an inference speed of 27 frames per second; quantitatively, it has improved the mean Average Precision (mAP) by more than 6 percentage points. To make sure that multiple item tracking and detection remain accurate, robustness has been evaluated in inclement weather and sensor deterioration. In summary, this study has offered a comprehensive and workable solution to the perception issue in intelligent vehicles, and experimental results have demonstrated its effectiveness, flexibility, and potential use in urban traffic scenarios.

Links and sources

• Official / arXiv page

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