Self-Driving Car Using Neural Networks and Computer Vision

In recent years, lidar has been one of the most important sensors in autonomous vehicles; nevertheless, its high price makes scale production impractical. As a result, we will describe the operation of a self-driving automobile prototype that makes use of a more cost-effective cameras and computer vision, the major purpose of our prototype is to provide a means to travel across our virtual environment in a manner that is not only secure but also expedient, effective, and comfortable. We have been able to recognize lanes, traffic signals, obstructions, and also have utilised the notion of stereo vision in order to calculate depth. Additionally, trajectory planning and steering control have been incorporated into the system. The proposed work indicate that camera-based autonomous vehicles are feasible, and as a result, our research has the potential to serve as a foundation for all future real-world implementations. The proposed Self Driving Car using Neural Networks and Computer Vision detects lane lines on streets and highways, traffic light detection and front collision avoidance in various climate conditions. Lane detection technique, to a noteworthy degree, improves the security in the independent vehicles. A self-propelled or a self-sufficient vehicle is an independent machine that detects the natural conditions and settles on an appropriate human-like decision. The purpose of this project is to find a feasible solution to detect lanes, traffic light detection and front collision avoidance on a low voltage computer that can be easily powered in a regular auto vehicle.