The performance of the current collision avoidance systems in Autonomous Vehicles (AV) and Advanced Driver Assistance Systems (ADAS) can be drastically affected by low light and adverse weather conditions. Collisions with large animals such as deer in low light cause significant cost and damage every year. In this paper, we propose the first AI-based method for future trajectory prediction of large animals and mitigating the risk of collision with them in low light. In order to minimize false collision warnings, in our multi-step framework, first, the large animal is accurately detected and a preliminary risk level is predicted for it and low-risk animals are discarded. In the next stage a multi-stream CONV-LSTM-based encoder-decoder framework is designed to predict the future trajectory of the potentially high-risk animals. The proposed model uses camera motion prediction as well as the local and global context of the scene to generate accurate predictions. Furthermore, this paper introduces a new dataset of FIR videos for large animal detection and risk estimation in real nighttime driving scenarios. Our experiments show promising results of the proposed framework in adverse conditions. Our code is available online1.


    Zugriff

    Zugriff prüfen

    Verfügbarkeit in meiner Bibliothek prüfen

    Bestellung bei Subito €


    Exportieren, teilen und zitieren



    Titel :

    Deer in the headlights: FIR-based Future Trajectory Prediction in Nighttime Autonomous Driving


    Beteiligte:


    Erscheinungsdatum :

    04.06.2023


    Format / Umfang :

    3076092 byte





    Medientyp :

    Aufsatz (Konferenz)


    Format :

    Elektronische Ressource


    Sprache :

    Englisch




    Underride Accidents: Headlights, Glare, and Nighttime Visibility

    Brown, D. R. / Bookwalter, J. C. / Guenther, D. A. | British Library Conference Proceedings | 1985


    Eye movement patterns in nighttime driving simulation: conventional and swivelling headlights

    Panerai,F. / Toffin,D. / Paille,D. et al. | Kraftfahrwesen | 2007


    Vehicle Classification in Nighttime Using Headlights Trajectories Matching

    Vu, Tuan-Anh / Pham, Long Hoang / Huynh, Tu Kha et al. | Springer Verlag | 2018


    An algorithm for headlights region detection in nighttime vehicles

    Shan, Xue / Hong, Zhu / Shunyuan, Yu et al. | IEEE | 2016