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MSc. presentation by M. Sandsjö and O. Sundbom: Edge Case Generation for Autonomous Vehicles

Different trafic scenarios are shown.

Seminarium

From: 2022-05-30 13:15 to 14:00
Place: Seminar Room KC 3N27
Contact: anton [dot] cervin [at] control [dot] lth [dot] se
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Måns Sandsjö and Oscar Sundbom are defending their Master's thesis at the Dept. of Automatic Control.

Where: Seminar room: KC:3N27

When: Monday 30 May 13:15--14:00

Authors: Måns Sandsjö and Oscar Sundbom

Title: Edge Case Generation for Autonomous Vehicles

Advisors: Anton Cervin, Dept. of Automatic Control; Guo Zhou, Scania CV

Examiner: Pontus Giselsson, Dept. of Automatic Control

 


Abstract:

In this thesis, a method generating critical situations for autonomous vehicles with fewer resources was investigated. To produce these key examples, the strategy in this thesis explores optimization and reinforcement learning. These key examples are defined as Edge cases; these cases are situations on the border between safe and unsafe. To be able to search for them, in this paper, they are defined numerically with Time To collision (TTC). This research provides two ways of searching for the edge cases with Particle Swarm Optimization and Deep Q-learning. Notably, the paper focuses on a new and extensive method of finding edge cases. Particle Swarm Optimization is used to search over a larger amount of scenarios, whereas the Deep Q-Learning tunes each scenario to generate an edge case. This — to the best of our knowledge — has never been provided before in such a principled manner. When evaluating the results of the methods, both the algorithms outperform standard methods of grid search and randomized search by a factor of three and five, respectively.