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Title: Safe game-theoretic planning for autonomous vehicles

Background: Game-theoretic frameworks combined with machine learning techniques (e.g., inverse reinforcement learning [1]) has been shown to successfully yield interaction-aware models for autonomous vehicles from relatively small amounts of data and with high generalisability ability [2,3,4]. Despite these models' generalisability, the autonomous vehicles might still encounter novel situations where either the collected data or the assumptions in the game-theoretic model reduce the performance, potentially making the autonomous vehicle unsafe. The objective of this project is to integrate a safety-aware feature in the game-theoretic frameworks, which prevents the autonomous vehicle from entering unsafe modes of driving. Adding this feature will allow an autonomous vehicle to operate in a safe interaction-aware manner, with only slight adjustments when unsafe modes of driving are close.


Description: Towards the objective above, the first step is to consider the safety-aware framework in [5] based on reachability analysis, which checks if an unsafe state can be reached in the near future. This safety-aware framework will be integrated with game-theoretic models from [2,3,4], which predicts future nominal behaviour between drivers on the road by explicitly modelling the drivers’ interaction. In this integration, the safety-aware framework will intervene whenever the predicted future nominal behaviour from the game-theoretic model results in an unsafe state. Evaluations will be carried out in a driving simulator, where the examples from [2,3,4] may serve as a start.

Work plan:

1. Read relevant background knowledge and conduct a literature review.

2. Combine the safety-aware framework from [5] with the game-theoretic models from [2,3,4].

3. Implement the formalism in Python.

4. Validate the approach via simulations.

The start date of the project will be individually discussed.

Prerequisites:

1. Elementary knowledge in applied mathematics, including mathematical control theory (e.g., SF2832 or SF2852 or similar) and numerical analysis (e.g., SF1544 or similar). Recommended is also a course related to machine learning (e.g., EL2805 or SF2957 or similar).

2. Elementary knowledge in programming, including Python.

Supervisors: Elis Stefansson (elisst@kth.se) and Yulong Gao  (yulongg@kth.se)
Examiner : Karl H. Johansson - (kallej@kth.se)

References:

[1] Ziebart, Brian D., et al. "Maximum entropy inverse reinforcement learning." Aaai. Vol. 8. 2008.

[2] Sadigh, Dorsa, et al. "Planning for autonomous cars that leverage effects on human actions." Robotics: Science and Systems. Vol. 2. 2016.

[3] Fisac, Jaime F., et al. "Hierarchical game-theoretic planning for autonomous vehicles." 2019 International Conference on Robotics and Automation (ICRA). IEEE, 2019.

[4] Stefansson, Elis, et al. "Human-robot interaction for truck platooning using hierarchical dynamic games." 2019 18th European Control Conference (ECC). IEEE, 2019.

[5] Leung, Karen, et al. "On infusing reachability-based safety assurance within planning frameworks for human–robot vehicle interactions." The International Journal of Robotics Research 39.10-11 (2020): 1326-1345.

 


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