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

Call for Papers Open Now!

We are soliciting workshop papers which will be published in the IEEE Xplore IV Workshops Proceedings (Separate from IEEE IV Proceedings). We welcome novel and experimental work that is aligned with our workshop topics. In case your topic does not appear in our list but you think it could fit our workshop please reach out using the email address at the end of this page and we can discuss the matter.

Please submit papers using the Workshop Code 1e97y at Papercept. You can also find this information and links by opening the menu point for Interaction-Driven Behavior Prediction and Planning for Autonomous Vehicles. at:

https://ieee-iv.org/2026/contributions/call-for-workshop-papers/

Paper Format

The paper must be formatted according to the IEEE IV 2026 formatting guidelines, which means it must:

Use the double-column layout
Have a minimum of four pages, and must not exceed 6 pages, including figures, references, appendix or any other content.

Please find templates and formatting instructions here: https://www.ieee.org/conferences/publishing/templates.html

The paper must follow IEEE policies regarding dual-submission and prior publication, explained e.g. here: https://journals.ieeeauthorcenter.ieee.org/become-an-ieee-journal-author/publishing-ethics/guidelines-and-policies/submission-and-peer-review-policies/

Important Dates

Workshop Papers Submission: January 30, 2026
Workshop Papers Acceptance Notification: February 28, 2025
Workshop Papers Final Submission: March 15, 2026

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.

Tentative Schedule

Time	Name & Affiliation	Type of Presentation
08:00 - 08:30		Welcome Ceremony
08:30 - 09:00	Tbd.	Paper
09:00 - 09:30	Tbd.	Paper
09:30 - 10:00	Tbd.	Paper
10:00 - 10:30		Morning Break
10:30 - 11:00	Tbd.	Paper
11:00 - 11:30		Keynote
11:30 - 12:00	Tbd.	Paper
12:00 - 13:00		Lunch Break
13:00 - 13:30	Tbd.	Paper
13:30 - 14:00	Tbd.	Keynote
14:00 - 14:30	Tbd.	Paper
14:30 - 15:00		Afternoon Break
15:00 - 15:30	Tbd.	Paper
15:30 - 16:00	Tbd.	Paper
16:00 - 16:30		Panel Session
16:30 - 17:00		Poster Session
17:00 - 17:30		Closing Ceremony