Deep Learning Techniques for Obstacle Detection and Avoidance in Driverless Cars

With the advent of Internet of Things (IoT), The realization of smart city seems to be very imminent. One of the key parts of a cyber physical system of urban life is transportation. This mission-critical application has attracted many researchers in both academia and industry to investigate driverless cars. In the domain of autonomous vehicles, intelligent video analytics is very critical. By the advent of deep learning many neural networks based learning approaches are under consideration. This work tries to implement obstacle detection and avoidance in a self-driven car. One of advanced neural network called Convolutional Neural Network (CNN) is exploited for real time video/image analysis using an IOT device. This project makes use of a raspberry pi which is responsible for controlling the car and performing inference using CNN, based on its current input. The model trained has achieved an accuracy of 88.6% and are in good consent with expected performance.