Nozari, S.; Krayani, A.; Marin, P.; Marcenaro, L.; Gomez, D.M.; Regazzoni, C. Modeling Autonomous Vehicle Responses to Novel Observations Using Hierarchical Cognitive Representations Inspired Active Inference. Computers2024, 13, 161.
Nozari, S.; Krayani, A.; Marin, P.; Marcenaro, L.; Gomez, D.M.; Regazzoni, C. Modeling Autonomous Vehicle Responses to Novel Observations Using Hierarchical Cognitive Representations Inspired Active Inference. Computers 2024, 13, 161.
Nozari, S.; Krayani, A.; Marin, P.; Marcenaro, L.; Gomez, D.M.; Regazzoni, C. Modeling Autonomous Vehicle Responses to Novel Observations Using Hierarchical Cognitive Representations Inspired Active Inference. Computers2024, 13, 161.
Nozari, S.; Krayani, A.; Marin, P.; Marcenaro, L.; Gomez, D.M.; Regazzoni, C. Modeling Autonomous Vehicle Responses to Novel Observations Using Hierarchical Cognitive Representations Inspired Active Inference. Computers 2024, 13, 161.
Abstract
Equipping autonomous agents for dynamic interaction and navigation is a significant challenge in intelligent transportation systems. This study aims to address this by implementing a brain-inspired model for decision-making in autonomous vehicles. We employ active inference, a Bayesian approach that models decision-making processes similar to the human brain, focusing on the agent's preferences and the principle of free energy. This approach is combined with imitation learning to enhance the vehicle's ability to adapt to new observations and make human-like decisions. The research involved developing a multi-modal self-awareness architecture for autonomous driving systems and testing this model in driving scenarios, including abnormal observations. The results demonstrated the model's effectiveness in enabling the vehicle to make safe decisions, particularly in unobserved or dynamic environments. The study concludes that the integration of active inference with imitation learning significantly improves the performance of autonomous vehicles, offering a promising direction for future developments in intelligent transportation systems.
Keywords
active inference; bayesian learning; imitation learning; action-oriented model; world model; autonomous driving
Subject
Computer Science and Mathematics, Artificial Intelligence and Machine Learning
Copyright:
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
