13 Feb 2025 (Thu) 17:00-18:00 | Industrial Seminar by Dr. Luyi Mo

Autonomous driving: Embracing the era of large-scale commercialization

Speaker:

Dr. Luyi Mo

Vice President of Pony.ai

Date:    Feb 13 (Thu), 2025

Time:   5:00pm – 6:00pm

Venue:  Tam Wing Fan Innovation Wing Two, G/F Run Run Shaw Building, HKU

Abstract

The popularization of autonomous driving technology has brought significant changes to people's modes of transportation and lifestyles, and has also introduced new growth points for the automotive industry. We are witnessing the rapid iteration of autonomous driving technology and the continuous expansion of its application scenarios, while also facing many challenges. Starting from 2023, several cities in United States and China allowed fully driverless Robotaxi operating to the general public and hence the commercialization of Robotaxi business is accelerating. This speech aims to explore how to continuously drive industrial transformation and achieve large-scale commercialization of autonomous driving, especially Robotaxi, based on the technology and commercial practice of Pony.ai.

Speaker's Bio

Dr. Luyi Mo is a Vice President of Pony.ai, a global leader in the large-scale commercialization of autonomous mobility. She is responsible for overseeing Pony.ai's Guangzhou and Shenzhen offices as well as its Robotaxi service and operation. She successfully led the team to deploy China's first Robotaxi service in 2018 and continuously drove the efforts to make Pony.ai as the first company to operate fully driverless Robotaxi service in all tier-1 cities in China. Prior to joining Pony.ai, Dr. Mo worked as a senior software engineer at NetEase, specializing in game engine development. Dr. Mo holds a bachelor’s degree in mathematics from Zhejiang University and a Ph.D. in computer science from the University of Hong Kong. During her time in university, Dr. Mo was the first female world champion from China since 1977 at the 35(th) Annual ACM International Collegiate Programming Contest World Finals in 2011.

23 Jan 2025 (Thu) 17:30-18:30 | Seminar by Prof. Chenyang Wu

The impact of occasional activities on travellers’ preferences for mobility-as-a-service bundles and mode choice behaviour

Speaker:

Prof. Chenyang Wu

School of Aviation, Northwestern Polytechnical University, China

Date:    January 23, 2025 (Thursday)

Time:   5:30 pm – 6:30 pm

Venue:  Room 612B, 6/F Haking Wong Building, The University of Hong Kong

Abstract

Mobility-as-a-Service (MaaS) is a promising solution for sustainable mobility, and can offer mobility options at low ownership costs. However, people have complicated travel diaries and are likely to change travel plans. This study investigates the impact of non-mandatory trips on MaaS subscription preferences and mode choice. We designed stated choice experiments to collect potential MaaS users' choice behaviour, and developed mixed logit models that incorporate risks associated with activities and the response of travelers to the scale of such uncertainty on mode shift decisions. The results reveal that respondents tend to be more multimodal after subscribing to a MaaS bundle, and the use of a taxi is greatly encouraged after subscription. In terms of users’ risk preference, we find that more risk-averse individuals tend to be reluctant to subscribe to MaaS bundles when the level of uncertainty increases. Moreover, our findings demonstrate that travelers are more willing to pay for flexible travel options and have a higher value of time when facing uncertainties (i.e., in the presence of occasional activities).

About the Speaker

Prof. Chenyang Wu is an Associate Professor at School of Aviation, Northwestern Polytechnical University. She holds a PhD degree from Imperial College London in 2020, an M.S. degree from Stanford University in 2015, and a bachelor's degree from Tongji University in 2013. Prof. Chenyang Wu's research focuses on travel behaviour, urban air mobility, shared mobility and multimodal transport system. She has published extensively in reputable journals such as Transportation Research Part C, and presented her work at prestigious conferences like Transportation Research Record. She currently leads two NSFC (National Science Foundations of China) project and participates in two China National Key R&D programs. She won the Young technology star of Shaanxi Province in 2024 and the Young Elite Scientists Sponsorship Program from the China Association for Science and Technology in 2022.

12 Dec 2024 (Thu) 11:00 AM-12:30 PM | Seminar by Dr. Qiaochu He

Autonomous driving: Queueing game, information design and human-AI interactions

Speaker:

Dr. Qiaochu He, Southern University of Science and Technology

Date:    12 December 2024 (Thursday)

Time:   11:00 am - 12:30 pm

Venue:  Innovation Wing Two, G/F Run Run Shaw Building, HKU

Abstract

I am presenting recent research on service operations and business models within a mixed autonomy paradigm, focusing on navigation algorithms influenced by differentiated information and driver-algorithm interactions. Leveraging queueing game models, Bayesian persuasion-based information design, and reinforcement learning for experimental validation, this research examines complex traffic flows, platform operation strategies, the effectiveness of intelligent navigation models, and behavioral patterns in human-machine interaction. These efforts aim to enhance traffic system efficiency and social welfare, thereby advancing the development and application of autonomous driving technologies.

Specifically, I will present findings from five (working) papers. At the micro level, we had a POM paper explores the interaction between autonomous vehicles (AVs) and human-driven vehicles (HVs) using queueing game frameworks. In another POM paper, we proposed smart navigation algorithms through information design methods to mitigate traffic congestion in routing games. A third working paper extends this framework to incorporate targeted algorithms for further theoretical development. Additionally, two ongoing projects employ experimental economics and artificial intelligence (AI) techniques to investigate behavioral interactions between navigation algorithms and human drivers, thereby validating theoretical models of human-AI interaction. These experiments also assess whether AI algorithms can optimize traffic flow via implicit cooperation, even in the absence of centralized control. Furthermore, the studies attempt to explain algorithm aversion behaviors through a dynamic information design framework.

Speaker Bio:

Qiao-Chu He is a tenured Associate Professor at the SUSTech Business School. His primary research interests lie in operations management, with applications in areas such as smart cities, marketplaces and platforms, and human-algorithm/AI interactions. He serves as the principal investigator for several research projects funded by the National Natural Science Foundation of China (NSFC), the National “Ten-Thousand” Program for Young Scholars (2019), and the Hong Kong General Research Fund (HK-GRF). He holds a Bachelor of Engineering from Tsinghua University and a Ph.D. in Operations Research from the University of California, Berkeley. He has previously taught at HKUST and UNC Charlotte. His students hold faculty positions at Business School including HUST (Huazhong), USTC (Hefei) and the University of Liverpool (UK).