Abstract-I: Autonomous Navigating car : To make car move as fast as possible and with absolutely no collisions. And Controlling a car with high dimensional sensory inputs and Driving a car autonomously around a racetrack with obstacles like pedestrians and other vehicles and even traffic rules. We created a deep Q-network (DQN) agent to perform the task of autonomous car driving from raw sensory inputs. We evaluated our agent’s performance against several standard agents in a racing simulation environment (CARLA). Our results indicate that our DQN agent is capable of successfully controlling a car to navigate around a simulation environment(CARLA). Abstract-II: 3D Object Tracking in Autonomous Driving: we developed a stereo-camera based 3D multi Vehicle tracking System. The main objective of this system is to accurately predict location and orientation of vehicles from stereo camera data. It has three modules: a 2D object detection network, 3D position extraction and 3D object correlation. This system utilizes the Kalman filtering to improve the robustness of missed detections Abstract-III: trajectory planning: The planning module in an autonomous navigation can be considered as the system’s brain where the vehicle trajectory for the next seconds is planned. In this module, we make use of the perception data from the 3D object tracking to plan a secure and smooth trajectory for the vehicle. The trajectory planning objective is to provide the vehicle with a secure trajectory constrained by the vehicle dynamics limits, the navigation comfort and safety, and the traffic rules. Once the desired trajectory is generated, the next step is to control the vehicle in order to track this trajectory with the desired speed profile also defined by the planning module. The vehicle control is indeed the control of the vehicle actuators, such as the steering wheel, the accelerator and the braking force, in order to manage the vehicle motions. Abstract-IV: Distributed System Architecture and Development Process: For autonomous driving, complex autonomous driving algorithms, including perception, 3D object tracking, localization, planning, and control, are required with many heterogeneous sensors and computers. To manage the complexity of the driving algorithms and the heterogeneity of the system components, we applied distributed system architecture to the autonomous driving system, and a system platform for the distributed system of an autonomous car. The development process provides the guidelines to design and develop the distributed system of an autonomous vehicle. A time-triggered network protocol, FlexRay, to be applied as the main network of the software platform to improve the network bandwidth, fault tolerance, and system performance.