Confidence-driven weighted retraining for predicting safety-critical failures in autonomous driving systems
Informazioni aggiuntive
Autori
Stocco A.,
Tonella P.
Tipo
Articolo pubblicato in rivista scientifica
Anno
2022
Lingua
Inglese
Sommario
Safe handling of hazardous driving situations is a task of high practical relevance for building reliable and trustworthy cyber-physical systems such as autonomous driving systems. This task necessitates an accurate prediction system of the vehicle's confidence to prevent potentially harmful system failures on the occurrence of unpredictable conditions that make it less safe to drive. In this paper, we discuss the challenges of adapting a misbehavior predictor with knowledge mined during the execution of the main system. Then, we present a framework for the continual learning of misbehavior predictors, which records in-field behavioral data to determine what data are appropriate for adaptation. Our framework guides adaptive retraining using a novel combination of in-field confidence metric selection and reconstruction error-based weighing. We evaluate our framework to improve a misbehavior predictor from the literature on the Udacity simulator for self-driving cars. Our results show that our framework can reduce the false positive rate by a large margin and can adapt to nominal behavior drifts while maintaining the original capability to predict failures up to several seconds in advance.
Parole chiave
AI testing, Autonomous driving systems, Continual learning, Misbehavior prediction
Periodico
Journal of software: evolution and process
Volume
34
Numero ( Mese )
10
Pagine (o numero dell’articolo)
e2386
DOI
Diffusione
Licenza
CC BY
Visibilità
Pubblico
Status open access
Hybrid
