Systems and Algorithms for Autonomous Vehicles

Education facts

Code: 5EL283

The course provides an in-depth introduction to autonomous vehicles, addressing both the algorithms aspect and the systems aspect. The course consists of two parts. Part 1: Theory (5.5 hp). History and background of autonomous driving; perception-planning-control pipeline of autonomous vehicles; fundamentals of Machine Learning, Deep Learning and Reinforcement Learning; Convolutional Neural Networks for perception, including object classification, object detection, and segmentation; classic planning and control algorithms; Reinforcement Learning-based planning and control algorithms; safety and security issues; hardware and software platforms. Part 2: Lab Assignments (2.0 hp). Training and deployment of Deep Learning models for autonomous driving with a framework such as Tensorflow or PyTorch; implementation of part of the perception-planning-control pipeline in a simulation environment.

Entry requirements

Admission to the course requires 120 credits of previous studies including courses in the field of Artificial Intelligence (AI) or Machine Learning of at least 7.5 credits

Education offerings

Each offering has its own dates and conditions. Closed offerings are retained as history and do not mean that a new application is open.

• Systems and Algorithms for Autonomous Vehicles

  Umeå University

  Start date: 2 November 2026

  End date: 17 January 2027

  Pace of study: 50 %