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.
@inproceedings{DBLP:conf/seal/DauSXSC14,
author = {Anh Hoang Dau and
Andy Song and
Feng Xie and
Flora Dilys Salim and
Vic Ciesielski},
bibsource = {dblp computer science bibliography, https://dblp.org},
biburl = {https://dblp.org/rec/conf/seal/DauSXSC14.bib},
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 = {https://doi.org/10.1007/978-3-319-13563-2_46},
volume = {8886},
year = {2014}
}
© 2021 Flora Salim - CRUISE Research Group.