Abstract: To addresses the issue of degraded vehicle recognition performance caused by changes in lighting,pose differences,and occlusions in complex traffic scenarios,this paper proposes a lightweight multi-modal fusion framework,called MM-ASTFL.Its core innovation lies in a triple attention mechanism.First,Adaptive Channel-Space Attention(ACSA),which dynamically weights MobileNetV2 features to significantly enhance the representation of key areas such as license plates and headlights.Second,Time-Aware Attention-Enhanced LSTM(TA-LSTM) uses multi-head self-attention to capture temporal key frames,accurately depicting driving behaviours such as lane changes and turns.Third,cross-modal cross-attention achieves bidirectional guidance and deep aggregation of visual and temporal information.Experiments on VeRi-776 demonstrate that MM-ASTFL achieves a vehicle re-identification mAP of 89.8% and Rank-1 of 92.6%,representing improvements of 1.7% and 1.6% over SOTA;driving behaviour classification F1-score reaches 92.4%,an increase of 2.2%;trajectory prediction ADE and FDE are reduced to 0.68 m and 1.25 m,respectively,with error reductions of 13.3% and 15.4%.Ablation experiments confirm that each module contributes significant gains,and visualisation analysis further validates its robustness under extreme conditions such as backlighting and occlusion,providing an efficient and reliable solution for intelligent transportation systems.

Key words: Multimodal feature fusion, Intelligent transportation systems, Vehicle re-identification, Driving behavior classification, Trajectory prediction, Adaptive attention mechanism, Cross-attention fusion