Workshop on Trust Calibration and Feedback Generation in AI-Enabled Vehicle Systems

At 35th IEEE Intelligent Vehicles Symposium

June 2, 2024, Jeju Shinhwa World, Jeju Island, Korea

Important Information

Call for Papers

Authors will present the accepted papers in the workshop.

Date and Location: June 2, 2024, Jeju Shinhwa World, Jeju Island.

Workshop Registration Link


With the fast progress in AI algorithms and their implementations in the automated vehicle (AV) area, highly automated cars become more mature and closer to people’s daily lives. At the same time, public attitude towards and acceptance of the new technologies play an essential role in their implementation. Trust in AI is a widely known issue in human-AI interaction, and in automated driving, maintaining appropriate level of trust in the AI-enabled vehicle system is the key for the acceptance and safe use of automated cars. Either over-trust or under-trust is undesirable as they may lead to misuse or disuse of the system. As a result, trust needs to be calibrated, which refers to the requirement that the human’s trust in the AI should match the AI’s true capabilities. Many factors can affect trust, and the theories and applications to calibrate trust in AV are prosperous in different research communities, such as intelligent transportation systems, psychology, human factors, computer science/AI, robotics/human-robot interaction, just to name a few.

Feedback is the most efficient and effective method to increase AV decision making transparency and facilitates the construction of an appropriate level of trust. Relevant research in feedback design, considering the feedback modality, interface design, feedback contents, feedback timing, and underneath psychological and behavioural theories, has drawn significant attention in recent years. As one on-going research area, many new theories and results are being actively proposed.

A successful solution to the trust in AV issue requires interdisciplinary collaborations among researchers from various domains. This workshop tries to bring together the researchers from Human Factors and Ergonomics Society (HFES), American Psychological Association (APA), and the Special Interest Group on Computer–Human Interaction in the Association for Computing Machinery (ACM SIGCHI) with the IEEE Intelligent Transportation System society on the key issues of trust calibration and feedback development of human-centered automated driving systems. We also aim to foster collaborations and mentorship relationships that last beyond the workshop.

Topics of Interests:

Tentative Schedule

A full-day workshop with invited talks, panel discussions, and poster presentations will be held following the schedule below:
Time Activities Details
8 am - 10 am Invited Talks 4 speakers, each for 30 minutes, including talks and Q/A. Tentative Theme: Active trust calibration strategies for AI-Enabled Vehicle Systems
10am - 12 pm Accepted Paper Presentations 4-5 papers that are accepted to the workshop will be invited for presentation. 20 minutes each talk.
12 pm - 1:30 pm Mentorship luncheon Paired lunch for senior researchers (including the invited speakers and organizers) and PhD students or early-career researchers to share experiences and develop mentor-mentee relationships that can extend after the workshop. Mentor-mentee pairs will be established based on a survey filled out prior to the workshop.
1:30 pm - 3:00 pm Invited Talks 3 speakers, each for 30 minutes, including talks and Q/A. Tentative Theme: Feedback and UI design for trust calibration
3:00 pm - 4:00 pm Panel Discussions All the invited speakers will answer any questions together from the workshop participants.
4:00 pm - 5:00 pm Happy Hour Provide an opportunity for all participants to network and establish potential collaborations after the workshop

List of Speakers


Workshop Organizers

Jing Chen
Rice University

Short Bio: Jing Chen is an assistant professor of human factors and human-computer interaction in the Department of Psychological Sciences at Rice University. She received a M.S. in industrial engineering and her Ph.D. in psychology from Purdue University in 2015, and B.S. and M.Ed. in psychology from Zhejiang University in Hangzhou, China, in 2007 and 2010, respectively. She has conducted research on the fundamental principles of human performance and decision-making, and their application to solving human-automation interaction problems in application domains such as autonomous driving and cybersecurity. Dr. Chen currently serves as President-Elect of American Psychological Association Division 21, co-Chair of IEEE Human-Centered AI in Transportation (HAIT) Technical Committee, and a member on the Focus on Myopia Committee of The National Academies of Sciences, Engineering, and Medicine. Dr. Chen is a recipient of the Earl Alluisi Award for Early Career Achievement and the George E. Briggs Dissertation Award from the American Psychological Association Division 21, and the Rising Star Award from the Association for Psychological Science. She is a Fellow of the American Psychological Association and the Psychonomic Society.

Renran Tian
Indiana University-Purdue University Indianapolis

Short Bio: Renran Tian is an assistant professor in the Department of Computer Information Technology at Indiana University-Purdue University Indianapolis. He received his Ph.D. in Industrial Engineering from Purdue University in 2013, and B.S. and M.S. in Mechanical Engineering from Tsinghua University in Beijing, China, in 2002 and 2005, respectively. Dr. Tian's research interests span human-centered computing, cognitive ergonomics, human-AI interaction, crowdsourcing, and autonomous driving. With over 50 peer-reviewed publications in prestigious journals and conference proceedings, Dr. Tian is an established expert in his field. He is serving as the Chair of Technical Committees on Human-Centered AI in Transportation in the IEEE Intelligent Transportation Systems Society. Dr. Tian has organized the "Prediction of Pedestrian Behaviors" workshop series and served as an organizing committee member, associate editor, and session chair for over 30 international conferences. Dr. Tian was awarded the NSF CAREER Award in 2022 from the Human-Centered Computing program about modelling pedestrian situated intent.