Algorithms and Systems for Autonomous Vehicles 7.5 credits

About the course

Autonomous vehicles can be about cars, trucks, drones, or different types of special vehicles, such as mobile robots. The development of autonomous vehicles can lead to changes in several industries in the not so distant future. An autonomous vehicle is equipped with built-in processors and sensors that can detect the environment, perform sensor fusion for decision making, and have continuous control and steering. The course provides an in-depth introduction to autonomous vehicles where both Artificial Intelligence (AI) algorithms and their system aspects are studied.

The course consists of both theoretical and experimental elements, and is closely related to current research and development. The topics covered include: key concepts of the perception-planning-control pipeline for autonomous driving (AD); key concepts of machine learning (ML), especially reinforcement learning (RL), and deep reinforcement learning (DRL); hands-on exercises with one of the popular open-source ML frameworks such as Tensorflow or PyTorch. Training, deployment and validation ML-based autonomous driving algorithmsin in a simulation environment.