Interaction-driven Behavior Prediction and Planning for Autonomous Vehicles

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This workshop will be held as full day workshop at the IEEE Intelligent Vehicles Symposium 2026 (IV), at Detroit, MI, United States

Looking forward to see you in Detroit!

Please check this website and the conference website in the future to stay up-to-date on times, rooms and accepted papers.

Go directly to the schedule

Topics

The topics of interest of the workshop include, but are not limited to:

Cooperative and comprehensible motion planning
Probabilistic decision making and motion planning (including MDPs, POMDPs, MMDPs)
Probabilistic behavior prediction (with help of semantic high-definition maps)
Second-order effects in heavy interactive scenarios
Evaluation and benchmarking of the aforementioned topics

Workshop Content

Research on Automated Vehicles has experienced vast progress over the last decades. Today, first prototypes are sufficiently safe to drive on selected roads in public traffic. Nevertheless, safety comes at the price of overly conservative behavior, leading to inconvenient situations, for example, at unprotected left turns or merging scenarios. Presumably, the main reasons for this behavior include:

Errors in the prediction of other traffic participants, especially in interactive scenarios
Lack of probabilistic considerations in motion planning

Comfortable Automated Driving

While safety should never be put at risk, worst-case behavior of others should not be the default for the motion plan of an automated vehicle. Rather, with a safe reaction to such worst-case behavior always in reserve, the intended trajectory should be comfortable, less conservative, and thereby potentially closer to human expectations. Proposal and exchange of these kinds of approaches is the first aim of the workshop.

Multimodal Behavior Prediction

For such behavior, sophisticated behavior prediction approaches for other traffic participants are necessary, going beyond constant velocity assumptions. Predictions must be probabilistic and allow for maneuver options for other vehicles. Often, there is not “the right prediction,” but many. The choice is influenced by destinations, individual driving behaviors, and potentially even the driver’s mood. Thus, a simple evaluation against a ground truth is not possible. Prediction approaches (including machine learning), and proposals for their evaluation, are the second main goal of this workshop.

Comprehensible Automated Driving

For motion planning in highly interactive scenarios, a “ground truth” or “best option” may not exist. To be comprehensible and predictable for other road users, a good plan should be a subset of an expected prediction for a vehicle in the same situation. The combination of planning and prediction, including evaluation and benchmarking, is the third aim of the proposed workshop.

Effects of Automation on Traffic

Data-driven predictions can end up being implicitly conditioned on second-order effects. For example, seeing a recording vehicle or no driver in an autonomous car can influence traffic participant’s decisions. Fixed settings in automated functions, such as safe distances, can influence the traffic flow on highways. While this can potentially introduce a distribution shift for prediction algorithms, it could also be leveraged to purposefully shape traffic. We invite approaches investigating these second-order effects, propagating in highly interactive scenarios.

The IEEE ITSS Technical Committee on Self-Driving Automobiles supports this workshop.

Schedule

Time	Name & Affiliation	Type of Presentation	Presentation Title
08:30 - 08:31	!Attention, workshop start at 08:30 instead of 08:00!	Welcome Ceremony
08:31 - 09:00	Anna Mészáros - TU Delft, Netherlands	Paper	Studying the Effect of Explicit Interaction Representations on Learning Scene-Level Distributions of Human Trajectories
09:00 - 09:30	Sabbir Ahmed - UT El Paso, USA	Paper	Examining the effectiveness of traffic-smoothing autonomous vehicle controllers in the presence of lane-changing maneuvers
09:30 - 10:00	Due to travel issues unfortunately early break		Presenters can not come
10:00 - 10:30		Morning Break
10:30 - 11:00	Etienne Bührle - Karlsruhe Institute of Technology, Germany	Paper	Mean Field Control of Traffic Scenes
11:00 - 12:00	Lounis Adouane - Heudiasyc / Université de Technologie de Compiègne, France	Keynote	From Risk-Aware Motion Planning to Trustworthy Vehicle-to-X Autonomy: A Reliable Bottom-Up Control Architecture for Complex Interactive Environments
12:00 - 12:30	Faizan Tariq - Honda Research Institute, San Jose, USA	Paper	Adaptive Time Step Flow Matching for Autonomous Driving Motion Planning
12:30 - 13:30		Lunch Break
13:30 - 14:00	Marion Neumeier - Technische Hochschule Ingolstadt, Germany	Paper	Uncertainty-Aware Trajectory Prediction for Automated Driving
14:00 - 14:30	Constantin Selzer - Munich University of Applied Sciences, Germany	Paper	PlanTRansformer: Unified Prediction and Planning with Goal-Conditioned Transformer
14:30 - 15:00		Afternoon Break
15:00 - 15:30	Monica Fossati - Centre Inria d’Université Côte d’Azur, France	Paper	Focus on Relevant Road Users with Multi-Rules Reachable Sets
15:30 - 16:00	Chuheng Wei - UC Riverside & Purdue University, USA	Paper	Beyond Perception: A Survey and Future Directions of VLM-based Autonomous Driving Datasets
16:00 - 16:30	Panel Session	Panel Session	Impact of the new wave of AI on behavior prediction. Special guest: Ahmed Hussein
16:30 - 17:00	George Daoud - OntarioTech, Canada	Video Paper Presentation	A Comparative Study of Graph Neural Network Layer Selection for Interaction Modelling in Driving Trajectory Prediction
17:00 - 17:30		Poster Session and Goodbye Ceremony