Development of navigation system for autonomous vehicle

Autonomous vehicle is an auto piloted vehicle which includes automatic steering, driving and communication with the environment. A novel algorithm with two level cascaded Kalman filter and PID controller is proposed for steer control. Filtering and calculation of the lateral error using Kalman filter is the first level. PID control algorithm uses the filtered value and calculates the required steer to reduce the lateral error towards zero. The experimental results show that the combination of Kalman Filter with PID for lateral control reduces trajectory error to a minimum over the entire course of the track. The closed loop algorithm for dc motor helps in modifying the speed depending on the nature of the track. The obstacle sensor measures the distance between preceding moving vehicle and helps to maintain the safe distance. The wireless RF module enables the vehicle to communicate with the neighboring vehicle as well as with infrastructure, which helps the auto-piloted vehicle to get the traffic situations and environmental conditions for better path planning and platooning. Combination of the proposed steer control, speed control, obstacle detection and communication algorithms helps the auto-piloted vehicles to navigate the track with better tracking accuracy and completes the test bed track in optimum speed with shortest time.