Interaction-driven Behavior Prediction and Planning for Autonomous Vehicles

This workshop will be held as half-day workshop at the 29th IEEE International Conference on Intelligent Transportation Systems (ITSC), at Naples, Italy!

Call for Papers

We invite original contributions presenting novel research in interaction-driven behavior prediction and motion planning for autonomous vehicles. Papers should be up to 4 pages (excluding references) and will not be published in the main IEEE ITSC proceedings, offering an opportunity to discuss early work intended for future journal or conference submissions. Workshop papers may address topics including, but not limited to, motion planning, probabilistic decision making and prediction, second-order effects in interactive scenarios and evaluation frameworks for interactive autonomous driving systems. For further information about our workshop focus, please read down below.

One of the authors of accepted papers is expected to travel to the ITSC and present the paper in our workshop.

Please use the ITSC 2026 template found here: IEEE conference template and limit the paper to 4-pages excluding references!

Please submit your papers via email to: sascha.hornauer@minesparis.psl.eu

Dates:

Paper deadline: ~~26th of June, 2026~~, DEADLINE EXTENDED to 10th of July, 2026.
Author notification: ~~10th of July, 2026~~, Moved to 20th of July
Camera-ready: 31st of July, 2026
Workshop day: Probably 15th of September (Not yet announced)

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.

Tentative Schedule

Type	Duration	At Time	Authors / Notes
Welcome Ceremony	10 min	0:00–0:10	—
Talk #1	25 min	0:10–0:35	(TBD)
Keynote + Q&A	30 min	0:35–1:05	Prof. Bassam Alrifaee – Universität der Bundeswehr, München, Germany
Keynote + Q&A	30 min	1:05–1:35	Prof. Maria Laura Delle Monache – UC Berkeley, USA
Talk #3	25 min	1:35–2:00	(TBD)
Talk #4	25 min	2:00–2:25	(TBD)
Closing Session	10 min	2:25–2:30	—