VVZ API is not affiliated with ETH Zurich. Data might be outdated or incorrect. Please view the official ETHZ Vorlesungsverzeichnis for binding information.

103-1100-00L 6 Credits MSC D-BAUG , D-MAVT , D-INFK , D-MATH , D-PHYS , D-ERDW , D-ITET

Computer Vision and Artificial Intelligence for Autonomous Cars

Lecturers & Examiners: Dr. Christos Sakaridis

Up until FS2022 offered as Deep Learning for Autonomous Driving

VVZ CR n/a

Last Updated: 2026-06-03 00:07:36

Abstract

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.

Objective

Students will learn about the fundamentals of autonomous cars and of the computer vision models and methods these cars use to analyze their environment and navigate themselves in it. Students will be presented with state-of-the-art representations and algorithms for semantic, geometric and temporal visual reasoning in automated driving and will gain hands-on experience in developing computer vision algorithms and architectures for solving such tasks. After completing this course, students will be able to: 1. understand the operating principles of visual sensors in autonomous cars 2. differentiate between the core architectural paradigms and components of modern visual perception models and describe their logic and the role of their parameters 3. systematically categorize the main visual tasks related to automated driving and understand the primary representations and algorithms which are used for solving them 4. critically analyze and evaluate current research in the area of computer vision for autonomous cars 5. practically reproduce state-of-the-art computer vision methods in automated driving 6. independently develop new models for visual perception

Content

The content of the lectures consists in the following topics: 1. Fundamentals (a) Fundamentals of autonomous cars and their visual sensors (b) Fundamental computer vision architectures and algorithms for autonomous cars 2. Semantic perception (a) Semantic segmentation (b) Object detection (c) Instance segmentation and panoptic segmentation 3. Geometric perception and localization (a) Depth estimation (b) 3D reconstruction (c) Visual localization (d) Unimodal visual/lidar 3D object detection 4. Robust perception: multi-modal, multi-domain and multi-agent methods (a) Multi-modal 2D and 3D object detection (b) Visual grounding and verbo-visual fusion (c) Domain-adaptive and outlier-aware semantic perception (d) Vehicle-to-vehicle communication for perception 5. Temporal perception (a) Multiple object tracking (b) Motion prediction The practical projects involve implementing complex computer vision architectures and algorithms and applying them to real-world, multi-modal driving datasets. In particular, students will develop models and algorithms for: 1. Semantic segmentation and depth estimation 2. Sensor calibration for multi-modal 3D driving datasets 3. 3D object detection using lidars

Resources

Lecture Notes

Lecture slides are provided in PDF format.

General Information

Language: English
Levels: MSC
Frequency: Yearly recurring

Examination

Type: session examination
Mode: written 120 minutes
Aids: One A4 sheet of paper. Simple non-programmable calculator.

The final grade will be calculated from the session examination grade and the overall projects grade, with each of the two elements weighing 50%.The projects are an integral part of the course, they are group-based and their completion is compulsory. Receiving a failing overall projects grade results in a failing final grade for the course. Students who do not pass the projects are required to de-register from the exam.

Registration & Places

Max Places: 90

Course Components

Type	Title	Time & Place	Hours
lecture	Computer Vision and Artificial Intelligence for Autonomous Cars	No time listed	3 h weekly
practical/laboratory course	Computer Vision and Artificial Intelligence for Autonomous Cars The lecturer will communicate the exact lesson times of ONLINE courses.	No time listed	2 h weekly

Offered In

Geomatics Master
- Electives (The entire course programs of ETH Zurich and the University of Zurich are open to the students to individual selection.)
  - Recommended Electives of Master Degree Programme
Computer Science Master
- Majors
  - Major in Visual and Interactive Computing
    - Elective Courses
- Minors
  - Minor in Computer Vision
Electrical Engineering and Information Technology Master
- 01 - Courses of specializations
  - Track: Signal Processing and Machine Learning (The core courses and specialisation courses below are a selection for students who wish to specialise in the area of "Signal Processing and Machine Learning ", see . The individual study plan is subject to the tutor's approval.)
    - Specialisation Courses (These specialisation courses are particularly recommended for the area of "Signal Processing and Machine Learning", but you are free to choose courses from any other field in agreement with your tutor. A minimum of 40 credits must be obtained from specialisation courses during the MSc EEIT.)
Robotics, Systems and Control Master
- Core Courses
Data Science Master
- Master Studies (Programme Regulations 2023)
  - Electives
    - Subject-Specific Electives
- Master Studies (Programme Regulations 2017)
  - Core Courses
    - Core Electives
Cyber Security Master
- Minor
  - Visual and Interactive Computing
    - Elective Courses
Space Systems Master
- Deep Track Courses (At least 20 credits must be completed within the deep track courses. Surplus credit points can be counted towards the electives.)
  - Robotics
    - Deep Track Robotics (These courses can be credited either as a specialization subject or as an elective subject.)