Traffic flow prediction methods commonly rely on historical traffic data, such as traffic volume and speed, but may not be suitable for high-capacity expressways or during peak traffic hours. Furthermore, downstream flow can have significant impacts on traffic flow. To address these challenges, our study proposes a novel traffic flow prediction model, V-STF, which integrates visual methods to quantify macroscopic traffic flow indicators, as well as density features in temporal and flow feedback in spatio features. The contribution of our proposed model lies in its ability to improve prediction accuracy during non-periodic peak hours, by taking into account the impact of congested road conditions on traffic flow. Our experiments using the STREETS dataset demonstrate that V-STF outperforms state-of-the-art methods, especially in predicting sudden changes in traffic flow, resulting in more accurate predictions.

Detection methods based on 2-D images tend to extract the color, texture, shape and other appearance features of objects. However, in complex scenes, the detection results using these methods are often influenced by shadows, occlusion and resolution. In this paper, a disparity-proposal-based detection method that rapidly extracts candidate frames of the detection objects on the basis of stereo disparity and ensures the robustness of the candidate frames under different perturbations is proposed. Furthermore, depth information is used to construct multi-scale pooling layers, allowing objects of different sizes to activate different layers at different levels. The detection model incorporates 2-D image features and 3-D geometric features and overcomes the limitations of the 2-D detection methods (absence of depth information) by using disparity features. Based on the experimental results, this method effectively achieves on-road object detection in complex scenes.

For further enhancing the completeness and conciseness of the existing quantum-inspired feature fusion methods, this paper applies the quantum-related theories of Wootters metric and Fisher linear discriminant to dimension reduction and feature fusion. From the perspective of quantum metric spaces, i.e. phase space and probability space, this paper proposes two different feature fusion methods which take the Wootters statistical distance as the key factor to detect and fuse the duplicate feature data, and are different to the already developed quantum-inspired feature fusion methods. The experimental results reflect the superiority of the proposed feature fusion methods based on the Wootters metric for their better performances on relative completeness and conciseness.