For the immediate future, Autonomous Vehicles (AV) will have to share the road with human-driven vehicles. However, decision errors by human drivers during maneuvers result in crashes. AV therefore have to predict human-initiated safe and unsafe maneuvers to react appropriately for the safety and comfort of its passengers. We propose ManeuverNet, a deep neural network model that uses sensor data to predict if any vehicle within its field-of-view would perform safe or unsafe lane maneuvers. The maneuver could either be into its lane, or into any other vehicle's lane in the next 0.5 to 5 seconds. The model predicts five classes of maneuvers by taking fewer features than the published state-of-the-art. It distinguishes safe lane changes from unsafe cut-in maneuvers. It was pre-trained using a stacked auto-encoder network to improve the model's performance on small datasets. The model has been tested on the NGSIM and CitySim datasets. The model predicted with 93% accuracy even with just two seconds of observation. Advance braking using the model's predictions resulted in safer headways during at least 91% of the cut-in events and passenger comfort was retained during at least 77% of such events. In addition, the model provides superior accuracy in high-risk scenarios like high speed and low headway.


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    Titel :

    ManeuverNet - A Deep Learning Model For Vehicle Maneuver Prediction


    Beteiligte:


    Erscheinungsdatum :

    24.06.2024


    Format / Umfang :

    1251749 byte





    Medientyp :

    Aufsatz (Konferenz)


    Format :

    Elektronische Ressource


    Sprache :

    Englisch



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