自主駕駛車技術
Self-Driving Cars
| 節 | 週四 |
|---|---|
3 10:10–11:00 | 自主駕駛車技術 EE635（光復） 3 節連堂 |
4 11:10–12:00 | |
N 12:20–13:10 |
* 根據陽明交大上課時間表所列
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.
無備註
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
History of Self-Driving Cars R&D Self-Driving Cars Industry
Sensors & Sensing
IMU, GPS and digital compass and barometer. Camera, Lidar and Radar Sensor Modeling
Probabilistic State Estimation
Probabilistic Concepts Vehicle Environment Interaction Bayes Filters
Gaussian and Nonparametric Filters
Kalman filter Extended Kalman filter Unscented Kalman filter Multi-State Constraint Kalman filter Particle Filter Fixed-lag Smoothing Localization
Mapping
Grid mapping SLAM Structure from Motion Optimization Spatial-temporal Calibration
Tracking
Motion Modeling and Learning Data Association Multi-target tracking
Sematic Understanding
Fundamentals of Machine Learning Neural Network Convolutional Neural Network Transfer Learning Lane Line Detection Traffic Sign Classification
Control & Planning
Vehicle Control Path Planning
Software Engineering & Hardware Systems
Silicon Valley Software Engineering Practices Hardware Systems for Self-Driving Cars
Introduction
Course overview and summary Self-driving car competition
| 週次 | 主題 |
|---|---|
| 第 1 週 | 2024-09-05(四) |
| 第 2 週 | 2024-09-12(四) |
| 第 3 週 | 2024-09-19(四) |
| 第 4 週 | 2024-09-26(四) |
| 第 5 週 | 2024-10-03(四) |
| 第 6 週 | 2024-10-10(四) |
| 第 7 週 | 2024-10-17(四) |
| 第 8 週 | 2024-10-24(四) |
| 第 9 週 | 2024-10-31(四) |
| 第 10 週 | 2024-11-07(四) |
| 第 11 週 | 2024-11-14(四) |
| 第 12 週 | 2024-11-21(四) |
| 第 13 週 | 2024-11-28(四) |
| 第 14 週 | 2024-12-05(四) |
| 第 15 週 | 2024-12-12(四) |
| 第 16 週 | 2024-12-19(四) |
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