A method for driving event detection using SAX with resource usage exploration on smartphone platform

Pimwadee Chaovalit,Chalermpol Saiprasert,Thunyasit Pholprasit

doi:10.1186/1687-1499-2014-135

Abstract

Driver 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 commercially available in the market. However, the majority of drivers on the road today do not have access to such systems. This paper proposes a novel methodology of driving event detection using a time series approximation algorithm known as symbolic aggregate approximation (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. In addition, a resource usage exploration on a smartphone platform is conducted in order to demonstrate the flexibility of our proposed algorithm to match different smartphone specifications. 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 100%. In terms of resource usage exploration, it has been found that there is a strong linear relationship between the parameter settings for data compression and the runtime of the algorithm. This is beneficial when a trade-off is required between the accuracy of the algorithm and the resource usage on the smartphone.

Highlights

Traveling from one place to another in a fastest possible time seems a necessity in our modern society today especially in big cities around the world
Our work proposed in this paper differs from the work in the literature as a novel algorithm based on the use of time series approximation technique called the symbolic aggregate approximation (SAX)
This paper proposes a novel method of applying a time series approximation technique called the SAX on driving event detection problems

Summary

Introduction

Traveling from one place to another in a fastest possible time seems a necessity in our modern society today especially in big cities around the world. Drivers tend to be more aggressive and careless whether to change lanes quickly to avoid traffic or to overtake cars in front to beat the red lights. This leads to an increased risk of road traffic accidents. Studies have shown that when a driver is monitored and driving events are recorded, the chances of aggressive and dangerous driving behavior are reduced [1]. A number of commercial products available in the market using in-vehicle data recorders equipped with a wide variety of sensory devices such as Global Positioning System (GPS) receiver and often a video camera are used to monitor drivers [4]. Taxi operators and fleet management systems are some examples that deploy driver monitoring systems where every driver can be traced to ensure that they follow designated routes and do not violate the speed limit [5,6]

Methods

Results

Conclusion