Publicación: Improving autonomous vehicle automation through human-system interaction
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Atribución-NoComercial-SinDerivadas 4.0 Internacional
info:eu-repo/semantics/openAccess
info:eu-repo/semantics/openAccess
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Self-driving cars (a.k.a. Autonomous Vehicles) have many challenges to tackle before having them fully deployed in our roads and cities. A critical one, which has been somehow neglected till recently, is to consider the driver in the system-user loop of vehicle performance. The purpose here is to tackle some of the current pending challenges involved in scaling up the level of autonomy of these systems. We have designed two user-vehicle experiences in two different sites with a common methodology that serves as an umbrella to collect all features required to model the driver-user. These two sites allow us to contrast and fine-tune this modelling issue. The approach consists in following a Learning Apprentice approach, where both the user behaviour and the system behaviour are learned and improved in a symbiotic ecosystem. This paper focuses on discussing the advantages of this approach and the main issues that require further research.
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Behavioural science, Control systems, Image processing, Transportation, Model design, Human Factors, Human- Vehicle Interactions, Computer Vision, Human-centred computing, Automation, User models, User centred design, Self-driving cars, Autonomous vehicles, Machine Learning
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E.T.S. de Ingeniería Informática
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Inteligencia Artificial