Deep Sensor Fusion Framework for Autonomous Vehicle Perception

Engineering notes

Experimental evaluations on benchmark datasets, including KITTI, nuScenes, and ApolloScape, demonstrate significant improvements in accuracy, recall, intersection-over-union, and multi-object tracking performance.

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 perception in autonomous vehicles is critical for ensuring safety and reliability in complex driving environments. Conventional single-sensor or single-model approaches often fail in the presence of occlusions, low visibility, or dense traffic. This work introduces an advanced multi-sensor deep learning framework that integrates YOLOv8 for object detection, Cascade R-CNN for precise localization, DeepLabv3+ for semantic segmentation, BEVFusion for multi-modal data fusion, and DeepSORT for temporal tracking. By combining camera, LiDAR, and radar inputs, the framework establishes a robust pipeline capable of addressing adverse conditions. Experimental evaluations on benchmark datasets, including KITTI, nuScenes, and ApolloScape, demonstrate significant improvements in accuracy, recall, intersection-over-union, and multi-object tracking performance. The proposed architecture represents a reliable step toward safe and scalable autonomous driving systems.

Links and sources

• Official / arXiv page

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