Genetic Programming for Channel Selection from Multi-stream SensorData with Application on Learning Risky Driving Behaviours

Publication Year: 2014 Publication Type : ConferenceProceeding


Unsafe driving behaviours can put the driver himself and other people participating in the trac at risk. Smart-phones with builtin inertial sensors o er a convenient way to passively monitor the driving patterns, from which potentially risky events can be detected. However, it is not trivial to decide which sensor data channel is relevant for the task without domain knowledge, given the growing number of sensors readily available in the phone. Using too many channels can be computationally expensive. Conversely, using too few channels may not provide sucient information to infer meaningful patterns. We demonstrate Genetic Programming (GP) technique's capability in choosing relevant data channels directly from raw sensor data. We examine three risky driving events, namely harsh acceleration, sudden braking and swerving in the experiment. GP performance on detecting these unsafe driving behaviours is consistently high on di erent channel combinations that it decides to use.


    author = {Anh Hoang Dau and Andy Song and Feng Xie and Flora Dilys Salim and Vic Ciesielski},
    bibsource = {dblp computer science bibliography,},
    biburl = {},
    booktitle = {Simulated Evolution and Learning - 10th International Conference, SEAL 2014, Dunedin, New Zealand, December 15-18, 2014. Proceedings},
    doi = {10.1007/978-3-319-13563-2_46},
    editor = {Grant Dick and Will N. Browne and Peter A. Whigham and Mengjie Zhang and Lam Thu Bui and Hisao Ishibuchi and Yaochu Jin and Xiaodong Li and Yuhui Shi and Pramod Singh and Kay Chen Tan and Ke Tang},
    pages = {542--553},
    publisher = {Springer},
    series = {Lecture Notes in Computer Science},
    timestamp = {Mon, 16 Sep 2019 01:00:00 +0200},
    title = {Genetic Programming for Channel Selection from Multi-stream Sensor Data with Application on Learning Risky Driving Behaviours},
    url = {},
    volume = {8886},
    year = {2014}


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