Agent for Autonomous Driving based on Simulation Theories
The field of automated vehicle demands outstanding reliability figures to be matched by the artificially driving agents. The software architectures commonly used originate from decades of automation engineering, when robots operated only in confined environments on predefined tasks. On the other hand, autonomous driving represents an “into the wild” application for robotics. The architectures embraced until now may not be sufficiently robust to comply with such an ambitious goal. This research activity proposes a bio-inspired sensorimotor architecture for cognitive robots that addresses the lack of autonomy inherent to the rules-based paradigm. The new architecture finds its realization in an agent for autonomous driving named “Co-driver”. The Agent synthesis was extensively inspired by biological principles that contribute to give the Co-driver some cognitive abilities. Worth to be mentioned are the “simulation hypothesis of cognition” and the “affordance competition hypothesis”. The former is mainly concerned with how the Agent builds its driving skills, whereas the latter yields an interpretable agent notwithstanding the complex behaviors produced. Throughout the essay, the Agent is explained in detail, together with the bottom-up learning framework adopted. Overall, the research effort bore an effectively performing autonomous driving agent whose underlying architecture provides considerable adaptation capability.
The thesis also discusses the aspects related to the implementation of the proposed ideas into a versatile software that supports both simulation environments and real vehicle platforms. The step-by-step explanation of the Co-driver is made up of theoretical considerations supported by working simulation examples, some of which are also released open-source to the research community as a driving benchmark. Eventually, guidelines are given for future research activities that may originate from the Agent and the hierarchical training framework devised. First and foremost, the exploitation of the hierarchical training framework to discover optimized longer-term driving policies.