Research Article
Automatic Classification of Traffic Accident Using Velocity and Acceleration Data of Drive Recorder
@INPROCEEDINGS{10.1007/978-3-319-90740-6_17, author={Moe Miyata and Kojiro Matsuo and Ren Omura}, title={Automatic Classification of Traffic Accident Using Velocity and Acceleration Data of Drive Recorder}, proceedings={Mobile Computing, Applications, and Services. 9th International Conference, MobiCASE 2018, Osaka, Japan, February 28 -- March 2, 2018, Proceedings}, proceedings_a={MOBICASE}, year={2018}, month={5}, keywords={Acceleration Classification Machine learning Drive recorder}, doi={10.1007/978-3-319-90740-6_17} }- Moe Miyata
Kojiro Matsuo
Ren Omura
Year: 2018
Automatic Classification of Traffic Accident Using Velocity and Acceleration Data of Drive Recorder
MOBICASE
Springer
DOI: 10.1007/978-3-319-90740-6_17
Abstract
In recent years, a drive recorder becomes common and is installed in a car to record sensor data, such as images, acceleration, and speed, about driving. The recorded data is useful to confirm and analyze a dangerous driving scene of a traffic accident and an incident. However, analyzing such data takes long time because it is done by a person who checks data one by one. Therefore, a method of automatic classification of drive recorder data is explored in this study. First, we labeled three types of incidents on the recorded data. Then, after extracting features from the acceleration and velocity, machine learning techniques are applied for the classification. Our preliminary evaluation showed that the classification result achieved about 0.55 of f-measure value.
