Highlights Primary focus of this study was to develop an in-vehicle fog detection method. Videos from the SHRP2 Naturalistic Driving Study dataset were used. Deep learning using TensorFlow Machine Learning library were utilized. The proposed deep learning models provided impressive weather detection accuracy. The proposed method can be used to develop connected weather based VSL algorithm.

    Abstract Providing drivers with real-time weather information and driving assistance during adverse weather, including fog, is crucial for safe driving. The primary focus of this study was to develop an affordable in-vehicle fog detection method, which will provide accurate trajectory-level weather information in real-time. The study used the SHRP2 Naturalistic Driving Study (NDS) video data and utilized several promising Deep Learning techniques, including Deep Neural Network (DNN), Recurrent Neural Network (RNN), Long Short-Term Memory (LSTM), and Convolutional Neural Network (CNN). Python programming on the TensorFlow Machine Learning library has been used for training the Deep Learning models. The analysis was done on a dataset consisted of three weather conditions, including clear, distant fog and near fog. During the training process, two optimizers, including Adam and Gradient Descent, have been used. While the overall prediction accuracy of the DNN, RNN, LSTM, and CNN using the Gradient Descent optimizer were found to be around 85 %, 77 %, 84 %, and 97 %, respectively; much improved overall prediction accuracy of 88 %, 91 %, 93 %, and 98 % for the DNN, RNN, LSTM, and CNN, respectively, were observed considering the Adam optimizer. The proposed fog detection method requires only a single video camera to detect weather conditions, and therefore, can be an inexpensive option to be fitted in maintenance vehicles to collect trajectory-level weather information in real-time for expanding as well as updating weather-based Variable Speed Limit (VSL) systems and Advanced Traveler Information Systems (ATIS).


    Access

    Check access

    Check availability in my library

    Order at Subito €


    Export, share and cite



    Title :

    Trajectory-level fog detection based on in-vehicle video camera with TensorFlow deep learning utilizing SHRP2 naturalistic driving data


    Contributors:


    Publication date :

    2020-03-22




    Type of media :

    Article (Journal)


    Type of material :

    Electronic Resource


    Language :

    English






    Multitasking additional-to-driving: Prevalence, structure, and associated risk in SHRP2 naturalistic driving data

    Bálint, András / Flannagan, Carol A.C. / Leslie, Andrew et al. | Elsevier | 2020


    Typical Acceleration Profiles for Left-Turn Maneuvers Based on SHRP2 Naturalistic Driving Data

    McAllister, Arianna J. / Wilkinson, Craig / Siegmund, Gunter P. et al. | SAE Technical Papers | 2021


    Investigation of Design Speed Characteristics on Freeway Ramps using SHRP2 Naturalistic Driving Data

    Brewer, Marcus A. / Stibbe, Jayson | Transportation Research Record | 2019