Visual perception is one of the most important information sources during driving. However, current camera perception systems are limited to object detection and, hence, to directly visible objects. Because it is mandatory for vehicles to run headlights at night, their emitted light can be detected before a vehicle is directly visible. Humans use this phenomenon to providently detect vehicles at night. In this paper, we analyze the discrepancy between ordinary vehicle detection and provident detection by quantifying the time gap between the two. This is achieved by conducting a test group study where participants are recorded while driving at night. Additionally, the dataset recorded during the study is used to provide a training and test dataset for machine learning approaches. To make use of the dataset for training machine learning methods, we analyze and discuss several annotation techniques. In a proof-of-concept, we used this dataset with its annotations to train a neural network on the direct and provident vehicle detection task. The resulting model shows that neural networks can successfully learn how to detect light-features. With further research and improvements, we are confident that a model for provident vehicle detection can be industrialized for use in production vehicles so that this information can be used in various safety and planning functions, including automatically adapting the high beam before it blinds other road users.


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    Title :

    Provident Detection of Vehicles at Night


    Contributors:


    Publication date :

    2020-10-19


    Size :

    2231266 byte





    Type of media :

    Conference paper


    Type of material :

    Electronic Resource


    Language :

    English



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