A Preference Learning Approach to Develop Safe and Personalizable Autonomous Vehicles

Author:

Ruya Karagulle, Nikos Arechiga, Andrew Best, Jonathan DeCastro, Necmiye Ozay

Keyword:

Computer Science, Artificial Intelligence, Artificial Intelligence (cs.AI), Systems and Control (eess.SY)

journal:

date:

2023-10-29 16:00:00

Abstract

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.

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