One of the main goals of our workshop is to bridge the gap between Computational Cognitive & Behavior Science, Explainable AI, Transportation, and the Autonomous Driving community. Our SIAM workshop mainly targets theoretical frameworks and practical algorithms of perception, decision-making, and planning integrated with social factors and computational cognitive science to enable autonomous vehicles (AVs) to interact with human agents in a socially compatible way. Specifically, the topics are as follows, but not limited to:

  • Applications of AVs interacting with human agents;
  • Algorithms of perception, decision-making, planning for human-like AVs;
  • Cognitive aspects and models for autonomous driving;
  • Cognitive and mental modeling toward socially driving, e.g., Theory of Mind and Theory of Machine;
  • Social cues for AVs in interactive driving tasks;
  • Action-reaction cycle modeling and validation;
  • Explainable interaction and planning in interactive driving tasks;
  • Evaluation and quantification of inter-human interactions and their implementations to human-AV interactions;
  • Human driving behavior/intention modeling, simulation, and analysis;
  • Heterogeneous human-agent teams;
  • Interactive traffic scenes analysis;
  • Interaction pattern learning, extraction, and recognition;
  • Interactive simulations and humans-in-the-loop simulations;
  • Learning-based theory for social interaction among human drivers;
  • Social and group intelligence in multiple human agent interaction;
  • Spatiotemporal driving behaviors in interactive traffic scenes;

Call for papers

If you are interested in contributing, please take the following steps:
  • Select our Workshop: The 2nd International Workshop on Socially Interactive Autonomous Mobility (SIAM)
  • Submit Using the Workshop Code: SociallyInteractive
  • The remainder of the submission process will be identical to that of regular conference submissions. See more details at https://ieee-iv.org/2024/call-for-workshop-papers/.

    Workshop Paper Review: Papers submitted for the workshop will undergo the same review process as the conference papers and will be published in the same proceedings.

      Important Deadlines:
    • February 01, 2024 February 05, 2024 (firm deadline, no extension): Workshop Paper Submission Deadline
    • March 30, 2024: Workshop Paper Notification of Acceptance
    • April 22, 2024: Workshop Final Paper Submission Deadline

    Program (13:00-16:30, June 2nd, 2024)

    Time Speaker Topic (click to see more details)
    13:00-13:10 Organizer Openning
    13:10-14:10 Presenters A 5-min poster presentation for each paper.
    14:10-15:10 Presenters Interactive Poster Presentations.
    15:10-15:40 Yee Mun Lee
    University of Leeds
    An overview of Pedestrian-Automated Vehicle interaction studies at the University of Leeds Abstract: In the future, Automated Vehicles (AVs) will need to interact with other road users, such as cyclists, pedestrians, and other vehicles. To enhance safety, improve traffic flow, and increase user acceptance and trust in AVs, pedestrians and other road users need to understand the AVs' intentions, communication, and behaviour. We have conducted over ten experimental studies understanding P-AV interaction in our pedestrian lab at the University of Leeds. This presentation will provide an overview of what we have learnt, the key findings and our future directions.
    15:50-16:20 Arkady Zgonnikov
    TU Delft
    In the driver's mind: cognitive modeling of human drivers in interactions with automated vehicles Abstract: Human behavior models are critical for the development of automated vehicles (AVs): they are used for behavior prediction, interaction planning, virtual training and testing, and benchmarking of AVs. However, diverse models used for each of these applications often face similar challenges when it comes to accuracy, generalization, interpretability, and scalability. In this talk, I will argue that improving the ability of AVs to interact with humans requires fundamental scientific research into human cognitive processes during traffic interactions. In my lab we are developing a new generation of human driver behavior models to address this challenge. I will present an overview of our recent work covering several complementary approaches for modeling human drivers’ decision making and operational behavior in traffic interactions, and will illustrate the potential of these models for AV development.
    16:20-16:30 Organizer Discussion and conclusions
    Accepted paper list:
    1. Tianyi Li, Shian Wang, Mingfeng Shang, Raphael Stern*. Can Cyberattacks on Adaptive Cruise Control Vehicles Be Effectively Detected?
    2. Mingfeng Shang, Shian Wang, Tianyi Li, Raphael Stern*. Interaction-Aware Model Predictive Control for Autonomous Vehicles in Mixed-Autonomy Traffic.
    3. Wissam Kontar, Yongju Kim, Xinzhi Zhong, Soyoung Ahn*. On the Need for Personalization in the Design of Autonomous Vehicle Driver Models.
    4. Frederik Werner*, René Oberhuber, Johannes Betz. Accelerating Autonomy: Insights from Pro Racers in the Era of Autonomous Racing - an Expert Interview Study.
    5. Kaifeng Wang*, Qi Liu, Xueyuan Li, Fan Yang. AF-DQN: A Large-Scale Decision-Making Method at Unsignalized Intersections with Safe Action Filter and Efficient Exploratory Training Strategy.
    6. Juhui Gim, Changsun Ahn*. Integrating Intrinsic Reasoning and Negotiation Mechanisms in Driver-Driver Social Interactions.
    7. Chaopeng Zhang*, Wenshuo Wang, Zhaokun chen, Junqiang Xi. 100 Drivers, 2200 Km: A Natural Dataset of Driving Style Toward Human-Centered Intelligent Driving Systems.
    8. Efimia Panagiotaki*, Tyler Reinmund, Brian Liu, Stephan Mouton, Luke Pitt, Arundathi Shaji Shanthini, Matthew Towlson, Wayne Tubby, Chris Prahacs, Daniele De Martini, Lars Kunze. RobotCycle: Assessing Cycling Safety in Urban Environments.
    9. Golam Md Muktadir, Taorui Huang*, Srishti Sripada, Rishi Saravanan, Amelia Yuan, Jim. Whitehead PedAnalyze - Pedestrian Behavior Annotator and Ontology.
    10. Yuhao Luo, Kehua Chen, Meixin Zhu*. GRANP: A Graph Recurrent Attentive Neural Process Model for Vehicle Trajectory Prediction.
    11. Qi Liu*, Yujie Tang, Xueyuan Li, Fan Yang, Xin Gao, Zirui Li. SIF-STGDAN: A Social Interaction Force Spatial-Temporal Graph Dynamic Attention Network for Decision-Making of Connected and Autonomous Vehicles.

    Invited Speakers

    Yee Mun Lee
    University of Leeds

    Yee Mun Lee is currently a senior research fellow at the Institute for Transport Studies, University of Leeds. She obtained her BSc (Hons) in Psychology and her PhD degree in driving cognition from The University of Nottingham Malaysia in 2012 and 2016 respectively. Her current research interests include investigating the interaction between automated vehicles and other road users, by using various methods, especially virtual reality experimental designs. Yee Mun was the leader of the 'Methodologies, Evaluation and Impact Assessment' Work Package of the EU-funded project, interACT, and was involved in L3Pilot, where she investigated the Users' Evaluation and Experience of a Level 3 system. She is now a Co-lead of the User Sub-project of another EU-funded project, Hi-Drive. Finally, Yee Mun is one of the SHAPE-IT project supervisors, where she continues her research on Human interaction with AVs in Urban Scenarios (www.shape-it.eu), and also actively involved in the International Organisation for Standardisation (ISO).

    Arkady Zgonnikov
    TU Delft

    Arkady Zgonnikov is an interdisciplinary cognitive scientist specializing in cognitive modeling of human behavior in human-robot interactions, with a particular focus on automated driving. He earned his MSc in Applied Mathematics from Saint Petersburg State University in 2009 and his PhD in Computer Science and Engineering from The University of Aizu in 2014. His early research concentrated on the mathematical modeling of intermittent motor control in human operators. In 2015, Arkady joined the Department of Psychology at the University of Galway as an Irish Research Council Postdoctoral Fellow, where he studied the response dynamics of decision making. In 2017, he returned to The University of Aizu to explore the interplay between decision making and motor behavior. In 2019, he joined the Department of Cognitive Robotics at Delft University of Technology as a postdoctoral researcher and was promoted to assistant professor in 2020. Arkady's current research aims to understand human cognition in traffic interactions through both mathematical and data-driven modeling. He is deeply concerned with the ethical and societal impacts of robotics and AI technology, striving to develop concrete methods that empower humans to meaningfully control artificial intelligent systems.

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