Self driving car using tensorflow

Abstract

Deep Learning has led us to newer possibilities in solving complex control and navigation related tasks. The paper presents Deep Learning with back propagation autonomous navigation and obstacle avoidance of self-driving cars, applied with Deep Q Network to a simulated car an urban environment. The approach uses two types of sensor data as input: camera sensor and laser sensor in front of the car. It also designs a cost-efcient high-speed car prototype capable of running the same algorithm in real-time. The design uses a camera and a Hokuyo Lidar sensor in the car front. It uses embedded GPU (Nvidia-TX2) or CPU for running deep-learning algorithms based on sensor inputs.