Vision-based future vehicle localization provides intuitive trajectory prediction, serving as a critical foundation for Advanced Driving Assistance Systems (ADAS) to formulate collision avoidance decisions. Among existing approaches, ego-view trajectory prediction has proven effective for driver monitoring and intervention in vision-based localization. This method aligns closely with human perceptual processing, making it essential for the Driver-in-the-Loop (DIL) development stage in modern ADAS. However, most existing ego-view trajectory prediction approaches rely on two-dimensional image-based predictions, creating a gap with human three-dimensional perception. This disparity negatively impacts the accuracy and timeliness of driver decision-making and intervention. In this paper, we propose MFV3DL (Monocular Vision Method for Future Vehicle 3D Localization), a dual-stream framework integrating 2D image trajectory prediction and depth prediction to achieve future vehicle 3D localization. To enhance accuracy, we leverage Multi-Object Tracking and Segmentation (MOTS) results and depth estimation as inputs for the dual-stream architecture. Additionally, we introduce a Related Information Fusion (RIF) unit to enable cross-modal interaction between the two streams. For depth stream predictions, we propose a ConvLSTM-based depth prediction method. Experimental results on the KITTI dataset demonstrate that MFV3DL outperforms state-of-the-art methods. In diverse driving scenarios, MFV3DL achieves superior 3D visualization results compared to 2D trajectory-based predictions. Baseline comparisons and ablation studies further validate that the proposed ConvLSTM-based depth prediction enhances the dual-stream architecture and RIF unit for 3D localization tasks.


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    Title :

    MFV3DL: Monocular Vision Method for Future Vehicle 3D Localization


    Contributors:
    Zhang, Wen (author) / Sun, Zhexuan (author) / Lv, Shengrong (author) / Mei, Konghao (author) / Chen, Guangkun (author) / Yang, Zhenya (author)


    Publication date :

    2025-07-01


    Size :

    3159773 byte




    Type of media :

    Article (Journal)


    Type of material :

    Electronic Resource


    Language :

    English




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