Vehicle lane change prediction with explainable soft mask attention mechanism and heterogeneous information encoder

Engineering notes

Experiments demonstrate: (1) The proposed SMILE-LC (Soft Mask Information with Lane-based Encoding for Lane Change) achieves optimal prediction performance across all perception ranges with strong efficiency and generalization. (3) The heterogeneous information encoder significantly improves lane change prediction performance, and the proposed lane-based encoding strategy outperforms other encoding architectures.

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

Lane-change intention prediction is critical for intelligent vehicles, and driver decisions depend on the perception and processing of driving context information. Despite advances in deep learning in this domain, further exploration of the driving context processing remains essential. This study proposes a soft mask attention mechanism to adaptively enhance or suppress input features from target and surrounding vehicles. After that, the masked features are extracted using a heterogeneous information encoder, thereby differentiating between target and surrounding vehicle information processing. The encoded features are then integrated using a multi-head attention mechanism, and the lane change probabilities are output through convolution operations and decoder layers. Experiments demonstrate: (1) The proposed SMILE-LC (Soft Mask Information with Lane-based Encoding for Lane Change) achieves optimal prediction performance across all perception ranges with strong efficiency and generalization. (2) The soft mask attention mechanism intuitively reveals the information processing patterns in prediction model. The target vehicle's lateral acceleration is the most critical feature, while the position information of surrounding vehicles is more important than their velocity and acceleration. (3) The heterogeneous information encoder significantly improves lane change prediction performance, and the proposed lane-based encoding strategy outperforms other encoding architectures. These results can significantly advance the development of Advanced Driver Assistance Systems (ADAS) and enhance lane-change safety.

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