Browsing by Author "Aapaoja, Aki"

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    3D Object Detection Algorithm Based on the Reconstruction of Sparse Point Clouds in the Viewing Frustum
    (Hindawi Publishing Corporation, 2022-10-15) Xu, Xing; Wu, Xiang; Zhao, Yun; Lü, Xiaoshu; Aapaoja, Aki; Department of Civil Engineering; Structures – Structural Engineering, Mechanics and Computation; Zhejiang University of Science and Technology; Solita Oy
    In response to the problem that the detection precision of the current 3D object detection algorithm is low when the object is severely occluded, this study proposes an object detection algorithm based on the reconstruction of sparse point clouds in the viewing frustum. The algorithm obtains more local feature information of the sparse point clouds in the viewing frustum through dimensional expansion, performs the fusion of local and global feature information of the point cloud data to obtain point cloud data with more complete semantic information, and then applies the obtained data to the 3D object detection task. The experimental results show that the precision of object detection in both 3D view and BEV (Bird's Eye View) can be improved effectively through the algorithm, especially object detection of moderate and hard levels when the object is severely occluded. In the 3D view, the average precision of the 3D detection of cars, pedestrians, and cyclists at a moderate level can be increased by 7.1p.p., 16.39p.p., and 5.42p.p., respectively; in BEV, the average precision of the 3D detection of car, pedestrians, and cyclists at hard level can be increased by 6.51p.p., 16.57p.p., and 7.18p.p., respectively, thus indicating the effectiveness of the algorithm.
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    Urban short-term traffic speed prediction with complicated information fusion on accidents
    (Elsevier Ltd, 2023-08-15) Xu, Xing; Hu, Xianqi; Zhao, Yun; Lü, Xiaoshu; Aapaoja, Aki; Department of Civil Engineering; Performance in Building Design and Construction; Zhejiang University of Science and Technology; Solita Plc.
    Optimizing the traffic flow prediction system is crucial in developing intelligent transportation since it increases the road network's capacity. The system's overall prediction accuracy will be increased by taking into account the relationship between the temporal and spatial properties of the road network and different external elements affecting the traffic situation. The traffic state, which is still a largely unexplored area, is impacted by the complicated interaction between accident information and the spatiotemporal properties of the route. This paper proposes an Accident Information Graph Fusion Attention Convolutional Network(AI-GFACN). Firstly, a highly correlated global road network is created using a global spatial feature point-edge swapping method, a D–D algorithm fusing Dijkstra, and Depth-First Search, which resolves the issue where the spatial features of accident sections are challenging to capture the diffusion effects caused by spatial features of nearby and further sections. Following the data's incorporation, it is suggested to combine the Spatio-temporal features of accident information and embed them in the road network. In addition, an attention mechanism is introduced, effectively addressing the difficulty in capturing the Spatio-temporal features of accident information within the road network. By integrating and categorizing the regionally distributed and temporally sustained congestion effects of various categories of accidents concerning previous research on accident information, this paper enhances the semantic expressiveness of accident information within the road network. Ablation experiments confirm the effectiveness and robustness of the proposed method, and it is applied to the dataset of Hangzhou West Lake District (including accident information), which increases short-term traffic speed prediction accuracy by 0.2% overall.