Prediction and Decision Making for Socially Interactive Autonomous Driving
Autonomous vehicles have to share the road and interact with various kinds of traffic participants in a social environment. The behavioral uncertainties of other road users make the probabilistic prediction and decision-making extremely challenging. Moreover, modeling human behavior under social interactions is inevitable to enable accurate predictions as well as human-like decision-making and behavioral planning.
In this workshop, we will thoroughly discuss state-of-the-art approaches for probabilistic and socially interactive prediction as well as decision-making and behavioral planning in highly interactive driving scenarios. Also, the fundamental aspects of prediction and decision-making for autonomous driving will be emphasized, such as problem formulation, dataset construction, as well as evaluation and verification, etc.
List of Topics
The topics include, but are not limited to:
- Social behavior and interactions, modeling and quantification of social factors;
- Probabilistic and reactive behavior modeling and prediction for various kinds of traffic participants (vehicles with human drivers, pedestrians, etc.);
- Driving data in highly interactive scenarios, collection, processing, annotation and augmentation;
- Decision-making under uncertainty, such as POMDP;
- Learning algorithms for decision-making and behavior modeling and their applications, such as reinforcement learning, inverse reinforcement learning (IRL), imitation learning;
- Deep generative models such as variational auto-encoder (VAE), generative adversarial network (GAN) and their applications;
- Bayesian neural network (BNN) and its applications;
- Probabilistic graphical models (PGM) for behavior modeling and prediction, such as dynamic Bayesian networks (DBN), hidden Markov models (HMM), etc.;
- Combination of learning algorithms and model-based planning/control methods;
- Uncertainty modeling and safety verification in deep learning, and applications in behavior modeling and decision-making;
- Evaluation, verification/test and failure analysis of prediction or decision-making/planning algorithms in the context of safety-critical systems.
Prof. Mykel Kochenderfer, Stanford University
Mykel Kochenderfer is a professor of Aeronautics and Astronautics at Stanford University. He is the director of the Stanford Intelligent Systems Laboratory (SISL), conducting research on advanced algorithms and analytical methods for the design of robust decision making systems, ranging from unmanned aircraft to driverless cars. He is also the director of the SAIL-Toyota Center for AI Research at Stanford and a co-director of the Center for AI Safety. He received a Ph.D. from the University of Edinburgh and B.S. and M.S. degrees in computer science from Stanford University. He is an author of the textbooks "Decision Making under Uncertainty: Theory and Application" and "Algorithms for Optimization", both from MIT Press.
- Deadline: February 7, 2019
- Notification of workshop paper acceptance: April 5, 2019
- Final Workshop paper submission: April 26, 2019
- Rules of Submission:
- Authors of accepted workshop papers will have their paper published in the conference proceeding. At least one author needs to be registered for the workshop and the conference.
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- Wei Zhan, UC Berkeley;
- Jiachen Li, UC Berkeley;
- Liting Sun, UC Berkeley;
- Yeping Hu, UC Berkeley;
- Masayoshi Tomizuka, UC Berkeley;