Autonomous vehicles have the potential to revolutionize transportation, but they must be able to navigate safely in traffic before they can be deployed on public roads. The goal of this project is to train autonomous vehicles to make decisions to navigate in uncertain environments using deep reinforcement learning techniques using the CARLA simulator. The simulator provides a realistic and urban environment for training and testing self-driving models. Deep Q-Networks (DQN) are used to predict driving actions. The study involves the integration of collision sensors, segmentation, and depth camera for better object detection and distance estimation. The model is tested on 4 different trajectories in presence of different types of 4-wheeled vehicles and pedestrians. The segmentation and depth cameras were utilized to ensure accurate localization of objects and distance measurement. Our proposed method successfully navigated the self-driving vehicle to its final destination with a high success rate without colliding with other vehicles, pedestrians, or going on the sidewalk. To ensure the optimal performance of our reinforcement learning (RL) models in navigating complex traffic scenarios, we implemented a pre-processing step to reduce the state space. This involved processing the images and sensor output before feeding them into the model. Despite significantly decreasing the state space, our approach yielded robust models that successfully navigated through traffic with high levels of safety and accuracy.

It goes without saying that an autonomous vehicle (AV) must be able to track the movement of pedestrians, animals, bicycles accurately, and other vehicles around it to safely and effectively get from point A to B. The systems responsible for doing this depend on being fed data, among other things, from which it is "trained" and learns to spot and react to these obstacles and hazards. A technique developed by Carnegie Mellon University (CMU) researchers called "scene flow" may be able to deliver improved results by training systems on larger datasets. Generally speaking, the more data that is available for training tracking systems, the better the results will be. And, according to the CMU researchers, they have found a way to unlock a "mountain" of autonomous driving data for exactly that purpose. Most AVs navigate based on sensor data from light detection and radar (LiDAR) systems that scan the environment to generate three-dimensional information of the world surrounding the vehicle.