AI driven Sustainability for Autonomous Driving

Self-driving technology is poised to revolutionize transportation infrastructure globally, offering a unique opportunity to enhance the quality of life. As urban areas face challenges such as rapid growth, avoidable collisions, vehicle emissions, and congestion from single-occupant commuters, autonomous vehicles promise to transform transportation systems by delivering significant environmental, social, and economic benefits. However, autonomous ground vehicles (AGVs) must overcome various challenges to navigate safely from origin to destination. In this lecture, we will explore these challenges across different self-driving models. We will delve into self-driving algorithms, the integration of supervised learning and reinforcement learning, fundamental driving functions, and collision avoidance using deep reinforcement learning. The talk will conclude with test results and an assessment of risk levels for self-driving technology.