A Preference Learning Approach to Develop Safe and Personalizable Autonomous Vehicles

Ruya Karagulle, Nikos Arechiga, Andrew Best, Jonathan DeCastro, Necmiye Ozay
Computer Science, Artificial Intelligence, Artificial Intelligence (cs.AI), Systems and Control (eess.SY)
2023-10-29 16:00:00
This work introduces a preference learning method that ensures adherence to traffic rules for autonomous vehicles. Our approach incorporates priority ordering of signal temporal logic (STL) formulas, describing traffic rules, into a learning framework. By leveraging the parametric weighted signal temporal logic (PWSTL), we formulate the problem of safety-guaranteed preference learning based on pairwise comparisons, and propose an approach to solve this learning problem. Our approach finds a feasible valuation for the weights of the given PWSTL formula such that, with these weights, preferred signals have weighted quantitative satisfaction measures greater than their non-preferred counterparts. The feasible valuation of weights given by our approach leads to a weighted STL formula which can be used in correct-and-custom-by-construction controller synthesis. We demonstrate the performance of our method with human subject studies in two different simulated driving scenarios involving a stop sign and a pedestrian crossing. Our approach yields competitive results compared to existing preference learning methods in terms of capturing preferences, and notably outperforms them when safety is considered.
PDF: A Preference Learning Approach to Develop Safe and Personalizable Autonomous Vehicles.pdf
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