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This paper presents a novel approach for learning self-awareness models forautonomous vehicles. The proposed technique is based on the availability ofsynchronized multi-sensor dynamic data related to different maneuvering tasksperformed by a human operator. It is shown that different machine learningapproaches can be used to first learn single modality models using coupledDynamic Bayesian Networks; such models are then correlated at event level todiscover contextual multi-modal concepts. In the presented case, visualperception and localization are used as modalities. Cross-correlations amongmodalities in time is discovered from data and are described as probabilisticlinks 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 detectionand autonomous decision making based on learned self-awareness models.

Learning Multi-Modal Self-Awareness Models for Autonomous Vehicles from Human Driving