Using BFD1000 and Raspberry pi for Autonomous Vehicle

The paper focuses to build an autonomous car prototype monocular vision with the help of Raspberry Pi as a processing chip. The model will perform three basic tasks that include self-driving on track, detecting street sign and will help in obstacle detection and collision avoidance. The necessary data from the real world to the car is provided by camera. It uses a BFD1000 tracking sensor module that provides information to the system through raspberry pi to our system which is connected to computer with the same network. The collected data is then trained and analyzed and relevant information and details is alerted back to car for appropriate actions to be taken. The risk of human errors is thus avoided safely and intelligently as the car can reach the given destination. Algorithms like lane detection, obstacle detection and traffic sign detection are used to provide the important types of control to the car. Lane detection and obstacle detection is implemented using BFD1000 module ie., a tracking sensor for better performance and accuracy as compared to OpenCV while cascade object detection method is used for street sign detection.