SSLA BASED TRAFFIC SIGN AND LANE DETECTION FOR AUTONOMOUSCARS

Abstract

The Self-Driving Cars are also known as Autonomous Vehicles. This Car has the ability to sense around the environment. These sensed parameters are processed and according to it the different actuators in the car will work without any human involvement. An Autonomous car work like a normal car but without any human driver. Autonomous cars rely on sensors, actuators, machine learning algorithms and Software to perform all the Automated Functions. The Software part is very important for Autonomous vehicles. The Software architecture acts as a bridge between Hardware Components and Application. The Standardized Software for Automotive cars is AUTOSAR. The AUTOSAR is a Standardized Architecture between Application Software and Hardware. This Standardized Architecture provide all Communication Interfaces, Device Drivers, Basic Software and Run-Time Environment. There are two important modules in Self-Driving Cars. They are Lane Detection and Traffic Signal detection which works automatically without any Human Intervention. A Machine Learning Algorithm is proposed in this paper. This Algorithm is mainly used to train the shape models and helps to detect the shape for Traffic Sign detection and Lane Detection. These both tasks are programmed using python with Open cv2 library file, numpy library file and Hough Detection technique is used to detect the appropriate circles of the traffic signals.By using all these tools, all the shape models are trained using Supervised training Algorithm and the detection is performed in such a way to help Autonomous cars to detect the lane and traffic Sign.