 |
| VECTOR | [3-0-0:3] |
|---|---|
| PREVIOUS CODE | INTR 6000C |
| DESCRIPTION | This postgraduate-level course introduces how game-theoretical methods are used to model strategic behaviors and to support decision making in transportation systems. Fundamental knowledge in game theory and mechanism design, including different game representations, equilibrium concepts and information asymmetry will first be covered. Variational inequality will then be introduced, with an emphasize of its importance in determining equilibrium solutions for transportation network models. |
| Section | Date & Time | Room | Instructor | Quota | Enrol | Avail | Wait | Remarks |
|---|---|---|---|---|---|---|---|---|
| L01 (6112) | Tu 09:00AM - 11:50AM | Rm 101, W4 | SUN, Xiaotong | 40 | 10 | 30 | 0 |
| VECTOR | [3-0-0:3] |
|---|---|
| DESCRIPTION | This course will introduce modern concepts, algorithms, and tools for data-driven transportation modeling and optimization. By taking this course, students will have the chance to master emerging data-driven methods for transportation systems modeling and optimization. |
| Section | Date & Time | Room | Instructor | Quota | Enrol | Avail | Wait | Remarks |
|---|---|---|---|---|---|---|---|---|
| L01 (6114) | Mo 06:00PM - 08:50PM | Rm 202, W4 | LIANG, Yuxuan | 40 | 14 | 26 | 0 |
| VECTOR | [3-0-0:3] |
|---|---|
| DESCRIPTION | Intelligent connected vehicles (ICVs) are believed to change people’s life in the near future by making the transportation safer, cleaner and more comfortable. Although many prototypes of ICVs have been developed to prove the concept of autonomous driving and the feasibility of improving traffic efficiency, there still exists a significant gap before achieving mass production of high-level ICVs. This course aims to present an overview of both the state of the art and future perspectives of key technologies that are needed for future ICVs. Through the study of this course, students will understand and master the basic concepts, key technologies and applications of ICV, and initially learn and master the ability to use that knowledge to solve practical problems, especially in cross-disciplinary communication and transportation context. |
| Section | Date & Time | Room | Instructor | Quota | Enrol | Avail | Wait | Remarks |
|---|---|---|---|---|---|---|---|---|
| L01 (6123) | Mo 09:00AM - 11:50AM | Rm 223, W1 | ZHENG, Xinhu | 30 | 16 | 14 | 0 |
| VECTOR | [3-0-0:3] |
|---|---|
| EXCLUSION | ROAS 5910 |
| CO-LIST WITH | ROAS 5910 |
| PREVIOUS CODE | INTR 6000B |
| DESCRIPTION | The course will cover a wide range of engineering psychology topics as well as how the research in these directions can affect policies and regulations in vehicle design and surface transportation. The students will gain an understanding of the characteristics and limitations of human beings from engineering psychology perspectives of view and how the design of traffic control devices, the roadway, the in-vehicle devices, regulations and traffic rules can be affected by the research in these directions. |
| Section | Date & Time | Room | Instructor | Quota | Enrol | Avail | Wait | Remarks |
|---|---|---|---|---|---|---|---|---|
| L01 (6125) | Fr 09:00AM - 11:50AM | Rm 222, W1 | HE, Dengbo | 15 | 3 | 12 | 0 |
| VECTOR | [3-0-0:3] |
|---|---|
| PREVIOUS CODE | INTR 6000K |
| DESCRIPTION | Driven by recent advancements in artificial intelligence (AI) and mobile edge computing (MEC), edge intelligence has emerged as a promising paradigm to support AI model training and inference over wireless networks. This postgraduate-level course aims to equip students with an understanding of edge intelligence from both communication and computing perspectives. The course begins by laying the groundwork with the fundamentals of wireless communications and mobile networks, followed by an exploration of the concepts and key technologies underpinning MEC. Building on this foundation, federated edge learning will be introduced, including its system modeling, convergence analysis, differential privacy mechanisms, and the integration of communication and learning design. Lastly, the course delves into device-edge co-inference techniques and the applications of edge intelligence in transportation, such as infrastructure-vehicle cooperative autonomous driving. |
| Section | Date & Time | Room | Instructor | Quota | Enrol | Avail | Wait | Remarks |
|---|---|---|---|---|---|---|---|---|
| L01 (6127) | Tu 03:00PM - 05:50PM | Rm 101, W4 | YAN, Jia | 30 | 7 | 23 | 0 |
| DESCRIPTION |
|---|