自主駕駛車技術

The course is designed for senior undergraduates and graduates who want to learn the key techniques of self-driving cars and/or want to become self-driving engineers/scientists. This course will cover the cutting-age of robotics, computer vision and machine learning for enabling self-driving cars including sensors & sensing, probabilistic state estimation, localization, mapping, tracking, sematic understanding, deep learning, control & path planning, software engineering and hardware systems.

This is an advanced course describing the key technologies used in self-driving cars. The course is highly related to robotics, computer vision and machine learning. The students must have good C/C++ programming skills and they should have some hands-on experiences on robotics, computer vision or machine learning before taking this course.

Lectures, guest lectures, hands-on experiments. Course Website: https://goo.gl/B7Gi69

Assignments, Project Report and Presentation, and Competition.

• Course overview and summary Self-driving car competition Self-Driving Cars: Past, Present and Future
• Sensors & Sensing
• Probabilistic State Estimation
• Gaussian and Nonparametric Filters
• Mapping
• Tracking
• Sematic Understanding
• Control & Planning
• Software Engineering & Hardware Systems
• Introduction

參考書: Probabilistic Robotics by Thurn, Burgard and Fox. Autonomous Mobile Robots by Siegwart, Nourbakhsh and Scaramuzza. Deep Learning by Goodfellow, Bengio and Courville. Computer Vision: Models, Learning and Inference by Simon J. D. Prince, 2012. (http://www.computervisionmodels.com) Computer Vision: Algorithms and Application, by Szeliski, Springer, 2011. 課程網頁:TBD 自行製作 and 教科書商提供

地點

Rm 766, EE, NCTU.

時間

Scheduling the meetings by Emails

聯絡方式

bobwang@nctu.edu.tw