2018/06/07 by Mahdyar Ravanbakhsh, Ravanbakhsh, Mahdyar, Mohamad Baydoun +12 · 3 citations
Computer Science · #Anomaly Detection Techniques and Applications #Bayesian Modeling and Causal Inference #Computer Vision and Pattern Recognition (cs.CV) #FOS: Computer and information sciences #Time Series Analysis and Forecasting #cs.CV
paper · pdf · doi:10.48550/arxiv.1806.02609
FUSION 2018 - 21st International Conference on Information Fusion, Cambridge, UK
arxiv created 2018/06/07 · openalex publication_date 2018/06/07 · arxiv updated 2018/06/08 · openalex created_date 2022/10/04 · openalex updated_date 2026/07/28
This paper presents a novel approach for learning self-awareness models for autonomous vehicles. The proposed technique is based on the availability of synchronized multi-sensor dynamic data related to different maneuvering tasks performed by a human operator. It is shown that different machine learning approaches can be used to first learn single modality models using coupled Dynamic Bayesian Networks; such models are then correlated at event level to discover contextual multi-modal concepts. In the presented case, visual perception and localization are used as modalities. Cross-correlations among modalities in time is discovered from data and are described as probabilistic links connecting shared and private multi-modal DBNs at the event (discrete) level. Results are presented on experiments performed on an autonomous vehicle, highlighting potentiality of the proposed approach to allow anomaly detection and autonomous decision making based on learned self-awareness models.