Interaction-driven Behavior Prediction and Planning for Autonomous Vehicles

This workshop will be held at the 34rd IEEE Intelligent Vehicles Symposium (IV), on June 4th, 2023, in Anchorage, Alaska, USA.

Join via Zoom!

Registration for IV is now open!

Please register at here for the conference and workshop. IEEE ITSS members will receive a free workshops day only registration.

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

Important Deadlines:

• February 01, 2023: Workshop Paper Submission Deadline (firm deadline, no extension)
• March 30, 2023: Workshop Paper Notification of Acceptance
• April 22, 2023: Workshop Final Paper Submission Deadline

In case of different deadlines on the official website, the ones on the website are to be followed: https://2023.ieee-iv.org/call-for-workshops/

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 (a) errors in the prediction of other traffic participants, especially in interactive scenarios and (b) the 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 kind 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 as much as individual driving behaviors and potentially even the drivers’ mood. Thus, a simple evaluation against a ground truth is not possible. Prediction approaches, including but not limited to machine learning based approaches, as well as proposals for their evaluation, are the second main goal of this workshop.

Comprehensible Automated Driving: For motion planning in highly interactive scenarios alike, 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 but not limited to their 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 be also leveraged to purposefully shape traffic. We invite therefore also approaches investigating these second-order effects, propagating in highly interactive scenarios.

Preliminary Agenda

08:30	Opening Ceremony
08:35	Talk	Gonca Guersun	Developing Deep Neural Network Models for Behavior Prediction and Vehicle Interaction	Bosch Center for Artificial Intelligence, DE
08:54	Talk	Arij Bouazizi	Traffic Light and Uncertainty Aware Pedestrian Crossing Intention Prediction for Automated Vehicles	Mercedes-Benz AG, DE
09:13	Talk	Raphael Trumpp	Learning to Interact: Scalable State Representations for Autonomous Driving	Technical University of Munich, DE
09:32	Talk	Julian Schmidt	Exploring Navigation Maps for Learning-Based Motion Prediction	Mercedes-Benz AG, DE
09:51	Talk	Julian Wiederer	Unsupervised Learning for the Detection of Driving Anomalies	Mercedes-Benz AG, DE
10:10	Talk	Masha Itkina	Occlusion Inference Using People as Sensors	Stanford University/Toyota Research Institute (TRI), US
10:29	Coffee Break
10:48	Talk	Thomas Genevois	Interaction-aware Motion Planning as an Alternative to Proactive Planning for Human-aware Navigation	INRIA, FR
11:07	Talk	Maximilian Kloock	Distributed Decision-Making using Cooperative Model Predictive Control	RWTH Aachen University, DE
11:26	Talk	Chris van der Ploeg	Exploiting situational awareness for risk-averse motion planning	TNO Integrated Vehicle Safety, NL
11:45	Talk	Marion Neumeier	Connecting the Nodes: Graph-based Approaches for Predicting Trajectories	CARISSMA Institute of Automated Driving, DE
12:04	Talk	Zhe Fu	The MegaVanderTest: Massive CAV Experiment in Nashville Pits Machine Learning against Traffic Jam	UC Berkeley, US
12:23	Panel session and closing ceremony

Organizers

Sascha Hornauer MINES Paris	Maximilian Naumann Bosch Center for Artificial Intelligence (BCAI)	Eike Rehder Robert Bosch GmbH
Jiachen Li Stanford University	Wei Zhan University of California at Berkeley	Martin Lauer Karlsruhe Institute of Technology (KIT)
Masayoshi Tomizuka University of California at Berkeley	Arnaud de La Fortelle Heex Technologies	Christoph Stiller Karlsruhe Institute of Technology (KIT)

Please get in touch with sascha.hornauer@mines-paristech.fr or any of the organizers in case you have any further questions.

Publication and Attendance

• Registration for the workshop-day or the whole conference is necessary to attend, except for IEEE ITSS members, which will receive free attendance to the workshop-day.
• In case of acceptance of a submitted workshop paper, one author has to pay the full publication fee to include the paper into the proceedings.

Past Editions

At IV2022, the organizers hosted a previous edition of this workshop: https://kit-mrt.github.io/iv2022-workshop/.

Acknowledgments

The organizers gratefully acknowledge support by the Deutsche Forschungsgemeinschaft (German Research Foundation) within the Priority Program SPP 1835 - Cooperative Interacting Automobiles.