This workshop will be held at the 34rd IEEE Intelligent Vehicles Symposium (IV), on June 4th, 2023, in Anchorage, Alaska, USA.
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.
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
- 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/
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.
|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|
|10:48||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|
Bosch Center for
Artificial Intelligence (BCAI)
Robert Bosch GmbH
University of California
Karlsruhe Institute of
University of California
|Arnaud de La Fortelle
of Technology (KIT)
Please get in touch with email@example.com 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.
At IV2022, the organizers hosted a previous edition of this workshop: https://kit-mrt.github.io/iv2022-workshop/.
The organizers gratefully acknowledge support by the Deutsche Forschungsgemeinschaft (German Research Foundation) within the Priority Program SPP 1835 - Cooperative Interacting Automobiles.