Vehicle Maneuvering-style Recognition in identifying the Culprit for a Road Accident

One of main reasons of road-side accidents (RSA) is the reckless by the driver. Reckless

drivers induce danger on the road and their surroundings, which could result in deadly accidents

both on road and off the road. High acceleration, frequent lane changes, lane changing in high

speed, turning at high speed, and braking late or suddenly are some of the activities by drivers that

cause these deadly accidents. In this paper, we have proposed and developed a driving style

recognition system, which would alert the driver to drive safely. It would also help in identifying

the driver at mistake during a road-side accident. In this paper, we have gathered data from an

accelerometer and a gyroscope to recognize the vehicle maneuvering style of the driver. We have

applied and compared the results of two well-known classifiers, i.e. Support Vector Machine

(SVM) and K-Nearest Neighbor (KNN) to identify the driving activity. We have also explored

different features extraction techniques to identify the best solution. After, the driving activity is

recognized, it is further classified to detect the driving style, as reckless or adequate. Later on, the

system can generate alarm to the driver through an actuator and use a weight-based algorithm to

identify the driver at fault, based on the driving style, in case of a RSA.

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