A method for driving event detection using SAX on smartphone sensors

Drivers errors such as careless and aggressive driving behaviors are one of the key factors contributing to road traffic accidents. It is, therefore, essential that drivers are aware of their actions when they are in control of the wheel responsible for not only their own lives but also passengers and bystanders on the road. Driver monitoring and advanced driver assistance systems have already been utilized in fleet and logistic domain as well as built into high end vehicles. However, the majority of drivers on the road today do not have access to such systems. This paper proposes a novel methodology of driving events detection using a time series approximation algorithm known as SAX on data collected from smartphone sensors. The use of smartphone allows the system to be easily accessible, widely available and implemented at low cost. Preliminary results from our experiments revealed that the precision of the proposed detection algorithm of aggressive driving events is fairly good as the precision values range from 50% to 66.67%. Further improvements can be made as our future work on the detection rate of the proposed algorithm as the detection rates reported range from 25% to 37.5%.