Robust traffic scene recognition via bidirectional cross-attention-based vision-map fusion

Engineering notes

Experiments on a self-built dataset covering five typical traffic scenes show that the proposed method achieves an overall recognition accuracy of 92.6%, outperforming the best single-input baseline Swin-Tiny by 3.3%.

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 traffic scene recognition serves as a critical foundation for decision-making and safe driving in autonomous driving and intelligent transportation systems. Existing methods mostly rely on single visual data vulnerable to environmental variations, or vision-LiDAR fusion schemes with insufficient capacity to represent road topology and traffic semantic information, limiting recognition accuracy and robustness. To address these limitations, this paper proposes an end-to-end traffic scene recognition network based on the fusion of monocular camera images and corresponding road map top-down view data. We design a learnable cross-view spatial alignment module to eliminate perspective discrepancy, and a bidirectional cross-attention fusion module to enable deep bidirectional interaction between visual semantic and map topology features. Experiments on a self-built dataset covering five typical traffic scenes show that the proposed method achieves an overall recognition accuracy of 92.6%, outperforming the best single-input baseline Swin-Tiny by 3.3%. Ablation studies further validate the effectiveness of each core module.

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