With the advancement in technology, autonomous vehicles will have a great impact on the near future and share the road space with human beings. For ensuring safer navigation, a robust collision mitigation system becomes mandatory which in general is deployed with multi-modal approaches. In this paper, a novel heuristic unimodal approach based on the vision system is presented to estimate the collision priority of vehicles on road. The priorities are estimated from the perspective of an ego vehicle that may be equipped with a vision based driver-assist system or a fully autonomous vehicle. Crowd-sourced videos from YouTube involving vehicular collisions captured by the dashboard camera of vehicles are considered. To detect the moving vehicles in the video, a deep learning-based pre-trained object detection model and a tracking algorithm are used. From the bounding box output, an estimate of collision priority of detected vehicles concerning the ego vehicle is obtained using an empirical heuristic-based approach. A simpler collision warning and navigation suggestion is also incorporated as a credible advanced driver assistance system element. The proposed qualitative approach performs well for input videos and a more robust estimate can be achieved by combining with other quantitative semantics of traffic parameters.


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    Title :

    Estimation of Collision Priority on Traffic Videos using Deep Learning


    Contributors:


    Publication date :

    2020-12-20


    Size :

    428860 byte




    Type of media :

    Conference paper


    Type of material :

    Electronic Resource


    Language :

    English



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