Browsing by Author "Yin, Chenhui"

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    Dataset "Results of centreline extraction based on maximal disks"
    (Cranfield University, 2025-03-03) Cecotti, Marco; Yin, Chenhui
    Maps of road layouts play an essential role in autonomous driving, and it is often advantageous to represent them in a compact form, using a sparse set of surveyed points of the lane boundaries. While lane centrelines are valuable references in the prediction and planning of trajectories, most centreline extraction methods only achieve satisfactory accuracy with high computational cost and limited performance in sparsely described scenarios. This paper explores the problem of centreline extraction based on a sparse set of border points, evaluating the performance of different approaches on both a self-created and a public dataset, and proposing a novel method to extract the lane centreline by searching and linking the internal maximal circles along the lane. Compared with other centreline extraction methods producing similar numbers of centre points, the proposed approach is significantly more accurate: in our experiments, based on a self-created dataset of road layouts, it achieves a max deviation below 0.15 m and an overall RMSE less than 0.01 m, against the respective values of 1.7 m and 0.35 m for a popular approach based on Voronoi tessellation, and 1 m and 0.25 m for an alternative approach based on distance transform.
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    ItemOpen Access
    Deep‐learning‐based vehicle trajectory prediction: a review
    (Institution of Engineering and Technology (IET), 2025-01-01) Yin, Chenhui; Cecotti, Marco; Auger, Daniel J.; Fotouhi, Abbas; Jiang, Haobin
    Vehicle trajectory prediction enables autonomous vehicles to better reason about fast‐changing driving scenarios and thus perform well‐informed decision‐making tasks. Among different prediction approaches, deep learning‐based (DL‐based) methodologies stand out because of their capabilities to efficiently summarise historical data, infer nonlinear behavioural patterns from human driving data, and perform long‐horizon prediction. This work reviews the DL‐based methods that have shown promising results, organising them in terms of usage of the input data, separating the encodings of the target vehicle's historical data, surrounding vehicle's historical data, and road layout data. In particular, this paper explores the relationships between the scope of the prediction components and the input data formats, as well as the connections with other elements in the same prediction framework, including vehicle interaction and road scene mining. This information is crucial to understand complex architectural decisions and to provide guidance for the design of improved solutions. This work also compares the performance of the most successful prediction models, establishing that appropriate encodings of vehicle interactions and road scenes improve trajectory prediction accuracy, with the best performance achieved by attention mechanism and Transformer‐based models. Finally, this work discusses future research directions, including considerations for real‐time applications.
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    ItemOpen Access
    Lane centerline extraction based on surveyed boundaries: an efficient approach using maximal disks
    (MDPI, 2025-04-18) Yin, Chenhui; Cecotti, Marco; Auger, Daniel J.; Fotouhi, Abbas; Jiang, Haobin
    Maps of road layouts play an essential role in autonomous driving, and it is often advantageous to represent them in a compact form, using a sparse set of surveyed points of the lane boundaries. While lane centerlines are valuable references in the prediction and planning of trajectories, most centerline extraction methods only achieve satisfactory accuracy with high computational cost and limited performance in sparsely described scenarios. This paper explores the problem of centerline extraction based on a sparse set of border points, evaluating the performance of different approaches on both a self-created and a public dataset, and proposing a novel method to extract the lane centerline by searching and linking the internal maximal circles along the lane. Compared with other centerline extraction methods producing similar numbers of center points, the proposed approach is significantly more accurate: in our experiments, based on a self-created dataset of road layouts, it achieves a max deviation below 0.15 m and an overall RMSE less than 0.01 m, against the respective values of 1.7 m and 0.35 m for a popular approach based on Voronoi tessellation, and 1 m and 0.25 m for an alternative approach based on distance transform.
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    ItemOpen Access
    Results of Centreline Extraction Based on Maximal Disks
    (Cranfield University, 2023-06-07 11:28) Yin, Chenhui; Cecotti, Marco; Auger, Daniel; Fotouhi, Abbas
    Results of centreline extraction based on maximal disks in a chosen lanelet2 map.