Driver audacity evaluation based on environment-specific embedded cyber-physical systems using low-level computation platform.

Abstract

Reckless driving of public transport in third world countries like Bangladesh has long been an unsolved issue. One of the reasons being the lack of constant monitoring of the driver’s driving style. Present solution to this is to use a constant monitoring device onboard the vehicle to assess the driver’s performance. They use onboard neural networks to analyze various driving data pattern collected from the suite of sensors on board the car. However the use of onboard neural network increases the computational complexity and demands for expensive processor. Moreover the suite of sensors used such as GPS, Steering angle sensor, IMU adds to the cost. This makes them a not so viable option for mass use in the roads of a underdeveloped country like Bangladesh. Here in this paper we propose a plug and play device that can analyze the driving behavior in real-time using an onboard low level processor and 2 low cost sensors. This process eliminates the need of onboard neural network. A test bench is setup for simulating the various driving patterns and the collected data is compared with the published studies to validate the method. Then the methodology is explained in details. Finally the whole system is tested with the collected data and result is analyzed.