Paper Title
Self Driving Car Using Asynchronous Advantageous Actor Critic

Abstract
Self-driving cars, enabled by Reinforcement Learning (RL), represent a transformative paradigm in the field of autonomous transportation. RL techniques empower these vehicles to learn and make decisions from interactions with the environment, mimicking human driving skills. RL algorithms, coupled with sensor data, enable self-driving cars to navigate complex traffic scenarios, optimize routes, and make real-time decisions. These vehicles continually adapt to varying road conditions and traffic dynamics, enhancing safety, efficiency, and reducing human error. RL-driven self-driving cars are on the cusp of revolutionizing our transportation landscape by offering the promise of safer, more efficient, and environmentally friendly travel options while paving the way for an autonomous future. Self-driving cars have the potential to provide increased mobility options for individuals who are unable to drive due to age, disability, or other factors. Self-driving cars can be programmed to drive more efficiently, potentially leading to reduced fuel consumption and emissions. One of the primary motivations for self-driving cars is to improve road safety. Theyre are an advancement in the transportation technology leveraging the power of AI. The selfdriving car application begins with a comprehensive perception module, utilizing an array of sensors such as LiDAR, radar, and cameras to capture the vehicle’s surroundings. These sensors feed data to a central processing unit, where sophisticated computer vision algorithms interpret the environment, identifying obstacles, pedestrians, and road signs. Sensor fusion plays a crucial role in consolidating information from various sensors, ensuring a more accurate and robust perception of the vehicle’s surroundings. Keywords - Reinforcement Learning, Route Optimization, Sensor Data, Deep Learning