Highlights We demonstrate the applicability of transformer encoder models with unsupervised pre-training to real-world interaction data for traffic conflict identification. The method unifies both proximity-based and evasive action-based surrogate safety measures and utilizes the entire time series of the interaction data. The method eliminates the use of thresholds and interprets the similarities of clusters of traffic conflicts and non-conflicts. We conduct eight case studies using real-world data that validate the usefulness of the proposed method and found the universal properties of traffic conflict from the identified results.

    Abstract Traffic conflict can be identified by the presence of evasive actions or the amount of temporal (spatial) proximity measures like time-to-collision (TTC). However, it is not enough to use only one kind of measures in some scenarios and it is hard to set a threshold for those measures. This paper proposed a method to identify traffic conflict by learning the representation of TTC and driver maneuver profiles with deep unsupervised learning and clustering the representations into traffic conflict and non-conflict clusters. We first trained a transformer encoder to encode sequences of surrogate safety measures into some latent space with unsupervised pre-training. Second, we identified informative clusters in the latent space by calculating the statistic summaries and visualizing trajectory pairs of each cluster. Some clusters are interpreted as traffic conflict clusters because they have small TTC, large deceleration rate and intertwining trajectories and they can be further interpreted as rear-end or angle conflicts. Moreover, the identified traffic conflicts contain critical conditions from the two vehicles in an interaction and one vehicle perceives them as abnormal and takes evasive action to avoid crashes.


    Access

    Check access

    Check availability in my library

    Order at Subito €


    Export, share and cite



    Title :

    Learning the representation of surrogate safety measures to identify traffic conflict


    Contributors:
    Lu, Jiajian (author) / Grembek, Offer (author) / Hansen, Mark (author)


    Publication date :

    2022-06-07




    Type of media :

    Article (Journal)


    Type of material :

    Electronic Resource


    Language :

    English




    Surrogate Safety Measures from Traffic Simulation Models

    Gettman, Douglas | Online Contents | 2003


    Surrogate Safety Measures from Traffic Simulation Models

    Gettman, Douglas / Head, Larry | Transportation Research Record | 2003




    A systematic mapping review of surrogate safety assessment using traffic conflict techniques

    Arun, Ashutosh / Haque, Md Mazharul / Bhaskar, Ashish et al. | Elsevier | 2021