Publication: Learning Multi-Modal Self-Awareness Models Empowered by Active Inference for Autonomous Vehicles
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2023-04
Defense date
2023-07-13
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Abstract
For autonomous agents to coexist with the real world, it is essential to anticipate the dynamics
and interactions in their surroundings. Autonomous agents can use models of the human
brain to learn about responding to the actions of other participants in the environment
and proactively coordinates with the dynamics. Modeling brain learning procedures is
challenging for multiple reasons, such as stochasticity, multi-modality, and unobservant
intents. A neglected problem has long been understanding and processing environmental
perception data from the multisensorial information referring to the cognitive psychology
level of the human brain process. The key to solving this problem is to construct a computing
model with selective attention and self-learning ability for autonomous driving, which is
supposed to possess the mechanism of memorizing, inferring, and experiential updating,
enabling it to cope with the changes in an external world. Therefore, a practical selfdriving
approach should be open to more than just the traditional computing structure of
perception, planning, decision-making, and control. It is necessary to explore a probabilistic
framework that goes along with human brain attention, reasoning, learning, and decisionmaking
mechanism concerning interactive behavior and build an intelligent system inspired
by biological intelligence.
This thesis presents a multi-modal self-awareness module for autonomous driving systems.
The techniques proposed in this research are evaluated on their ability to model proper driving
behavior in dynamic environments, which is vital in autonomous driving for both action
planning and safe navigation. First, this thesis adapts generative incremental learning to
the problem of imitation learning. It extends the imitation learning framework to work
in the multi-agent setting where observations gathered from multiple agents are used to
inform the training process of a learning agent, which tracks a dynamic target. Since
driving has associated rules, the second part of this thesis introduces a method to provide
optimal knowledge to the imitation learning agent through an active inference approach.
Active inference is the selective information method gathering during prediction to increase a
predictive machine learning model’s prediction performance. Finally, to address the inference
complexity and solve the exploration-exploitation dilemma in unobserved environments, an exploring action-oriented model is introduced by pulling together imitation learning and
active inference methods inspired by the brain learning procedure.
Description
Mención Internacional en el tÃtulo de doctor
Keywords
Autonomous vehicles, Autonomous driving systems, Active inference, Bayesian methods, Imitation learning, Multi-modal self-awareness module