Teaching CVAIAC

Computer Vision and Artificial Intelligence for Autonomous Cars

Autumn 2026 · ETH Zurich · 6 ECTS

About the course

This course introduces the core computer vision techniques and algorithms that autonomous cars use to perceive the semantics and geometry of their driving environment, localize themselves in it, and predict its dynamic evolution. Emphasis is placed on techniques tailored for real-world settings, such as multi-modal fusion, domain-adaptive and outlier-aware architectures, and multi-agent methods.

Lecturer
Christos Sakaridis
When
TBA
Where
TBA

Slides, exercise sheets, recordings, and exam materials are password-protected. Access credentials are announced in the lecture and on the course forum.

ETH course catalogue TBA Piazza forum TBA

Lecture team

Lecturer

Teaching Assistants

Autonomous car

Lectures

Date Time Room Topic Slides Video
TBA TBA TBA Fundamentals of Autonomous Cars TBA TBA
TBA TBA TBA Fundamental Computer Vision Architectures and Algorithms for Autonomous Cars TBA TBA
TBA TBA TBA Fundamental Computer Vision Architectures and Algorithms (continued) TBA TBA
TBA TBA TBA Semantic Segmentation TBA TBA
TBA TBA TBA Depth Estimation TBA TBA
TBA TBA TBA Object Detection TBA TBA
TBA TBA TBA Instance Segmentation and Panoptic Segmentation TBA TBA
TBA TBA TBA Unimodal 3D Object Detection TBA TBA
TBA TBA TBA 3D Reconstruction and Localization TBA TBA
TBA TBA TBA Domain Adaptation TBA TBA
TBA TBA TBA Multi-modal 2D and 3D Object Detection TBA TBA
TBA TBA TBA Visual Grounding, Anomaly Segmentation and Vehicle-to-Vehicle Communication TBA TBA
TBA TBA TBA Multiple Object Tracking and Motion Prediction TBA TBA

Practical sessions

Date Time Room Topic Slides Video
TBA TBA TBA Getting Started with Python and SLURM TBA TBA
TBA TBA TBA Project 1 Introduction: Semantic Segmentation and Depth Estimation TBA TBA
TBA TBA TBA Project 1: Attention Mechanisms and Transformers TBA TBA
TBA TBA TBA Project 1 Q&A TBA TBA
TBA TBA TBA Project 1 Q&A TBA TBA
TBA TBA TBA Project 1 Q&A TBA TBA
TBA TBA TBA Project 2 Introduction: 3D Detection from Point Clouds TBA TBA
TBA TBA TBA Project 2 Q&A TBA TBA
TBA TBA TBA Project 2 Q&A TBA TBA
TBA TBA TBA Project 2 Q&A TBA TBA
TBA TBA TBA Project 2 Q&A TBA TBA
TBA TBA TBA Project 2 Q&A TBA TBA

Projects

  1. Semantic segmentation and depth estimation

    Starts
    TBA
    Due
    TBA

    Implement and train multi-task models for dense visual perception on real-world driving data.

    Handout: TBA

  2. 3D object detection using LiDARs

    Starts
    TBA
    Due
    TBA

    Detect vehicles and other agents in LiDAR point clouds from a multi-modal driving dataset.

    Handout: TBA

Prerequisites

  • Linear algebra, multivariate calculus, and probability theory.
  • Basic computer vision and machine learning.
  • Programming proficiency in Python, PyTorch, scikit-learn, and scikit-image.

Exam & grading

Exam

Examiner
Christos Sakaridis
Format
Written session examination
Duration
120 minutes
Language
English
Permitted
One A4 sheet of paper and a simple non-programmable calculator.
  • Offered only in the session following the end of the course.
  • A mock exam (short version) is provided, with and without solutions.

Grading

Projects 50% · Exam 50%

  • Projects are mandatory and conducted in groups.
  • Failing the projects results in failing the final grade.
  • Students who do not pass the projects must de-register from the session exam.

Learning objectives

  • Understand the principles of visual sensors used in autonomous cars.
  • Distinguish the dominant architectural paradigms in modern visual perception models.
  • Categorize visual perception tasks and explain the algorithms that solve them.
  • Critically analyze current research in computer vision for autonomous driving.
  • Reproduce and adapt state-of-the-art methods for visual perception.
  • Independently develop new visual perception models for autonomous cars.