Abstract:
Intelligent transportation Systems are gaining significant manufacturer and customer attention in recent years. The prime purpose of the systems is to use on-board sensory data for safety and congestion related information gathering. On the average latest cars are equipped with forty to sixty sensors which are likely to increase above hundred in coming years. These sensors are normally very expensive in the market but are already installed in the vehicles. Different hand held gadgets are available in the market that can communicate with the cars on board processing unit and sensors to get sensory information via serial interface but these gadgets only get the current data from the sensors and to diagnose any faults stored in the system. The Vehicle Monitoring and Fault Prediction System get the data from the built-in sensors of the vehicle. The system uses those sensors to get the data from the vehicle’s Electronic Control Unit and sends them to database. The system running on centralized server normalizes the data and performs analysis on them. The android application on the other end provides the interface to the user. The user terminal contains information about current status, history of the errors and most importantly, the upcoming faults which are predicted by the sensor data analysis. All of this operation is done in real-time. As soon as the car is started, the Raspberry Pi, connected with the car starts getting the data from the Electronic Control Unit. The system is very cheap to implement as it contains no major installations and provides both real-time and past history of sensor data.
