Autonomous driving paper index
Autonomous Vehicles using Nuscenes Dataset: Sensor Fusion, Object Recognition, And Adaptive HMI Optimization
One-line summary
This paper presents an integrated autonomous vehicle framework combining Bird’s-Eye View (BEV) trimodal sensor fusion, Bi-LSTM temporal tracking, and adaptive Human Machine Interface (HMI) optimization using the nuScenes dataset.
Engineering notes
Key topics: autonomous driving, autonomous vehicle, bev, object detection, lidar, sensor fusion, nuscenes, 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
This paper presents an integrated autonomous vehicle framework combining Bird’s-Eye View (BEV) trimodal sensor fusion, Bi-LSTM temporal tracking, and adaptive Human Machine Interface (HMI) optimization using the nuScenes dataset. The proposed architecture fuses LiDAR, radar, and camera modalities using a channel-wise attention mechanism to improve perception robustness under complex urban driving conditions. Experimental evaluation demonstrates strong improvements in object detection accuracy, trajectory consistency, and driver workload reduction compared with single-modality baselines. The adaptive HMI subsystem dynamically adjusts visual complexity based on real-time scene analysis, improving situational awareness and reducing cognitive overload. The proposed framework achieved NDS 0.623, mAP 0.512, and AMOTA 0.541 while reducing driver cognitive workload by 28.2%.
Links and sources
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