AI-ACCIDENT DETECTION SYSTEM FOR FAST RESCUE ON FREEWAYS USING ON-BOARD CAR MOTION SENSORS FUSION

Abstract

Continuous tracking and detecting position systems are significant services that can using in many applications such as detecting accident cars on Freeways. This paper introduces Artificial intelligent (AI) method to quick discover the accident and determine the position of car on Freeways that will reduce the rate of fatalities. Most tracking and detecting position systems are based on Global Navigation Satellite System (GNSS). But the GNSS signal in certain areas such as buildings and tunnel is unavailable which make a big problem for these systems. To solve this problem, most traditional methods are based on integrated GNSS with other navigation sensors such as accelerometers, gyroscopes, odometers and so on. But these integrated methods still high cost and need to long time processing. Today, most modern Vehicles are equipping with several On-board Car Motion Sensors (OVMS) such as Acceleration Sensors (AS), Wheel Velocity Sensor (WVS), Gravity Sensor (GS), Yaw Rate Sensor (YRS) and additional to GNSS that can improve the safety and also use to determine and detect the position of accident vehicle. This paper develops an approach AI-accident detection system based on integrating GNSS with On-board Vehicle Motion Sensors (OVMS) using Extended Kalman Filter (EKF) algorithm. During GPS outages, the three Acceleration Sensors (3-ASs) are processing to determine velocities and then position of vehicle on three x, y and z axes. While the YRS and GS sensors are using to determine velocities angles  on three x, y and z axes, respectively. In same time the three WVS sensors are using to correct velocities errors of three acceleration sensors (3-ASs) until GNSS signal is return again. The proposed AI-accident detection system is tested in different scenarios during GPS outage signal. The results of proposed method can actually detect accident and determine the position of vehicle with high efficiency.