Abstract:
in the Autonomous Driving, traffic lights and signs are very important to provide
useful information to the autonomous driving such as direction and alerts, we must
build a system for autonomous driving that can detect these traffic signals and signs,
and perform action based on these detected traffic signals and signs to ensure our
autonomous car to be in lane with the traffics in the urban areas. The main objective
of this project is to develop image recognition algorithms to recognize Traffic Signs
and Signals and then perform action to control car in correspondence to the traffic
sign and signal. This report will describe convolutional neural network CNN based
object detection used for the recognition of Traffic Signals and signs, CNN can predict
different 2D poses of the signs i.e., triangular, square and circle. Our model can easily
detect the signal by differentiating the colors of signal in real time and then process
the information and tell the vehicle what to do.
The main goal and advantage of using this technique is that it provides better
recognition of Traffic Signals and Signs. Model of car is used to represent the
algorithm. The model can start running, slow, fast and stop by recognizing the Traffic
Signals and Signs. We use different hardware components to represent and test our
algorithm on model or vehicle.
