A Dynamic Neural Field Approach for Intelligent Cockpits: Online Learning and Prediction of Traveling Routines

Abstract

Empirical studies of human mobility patterns reveal a high degree of spatio-temporal regularity, with individuals frequently traveling to the same destinations at relatively the same time. Moreover, the vehicle frequently transports the same occupants carrying specific objects. Indeed, there is a rich body of work on predicting users’ future destinations. However, existing computational approaches primarily rely on offline learning methods that require stationary data distributions, thus not accounting for situations in which traveling habits may change over time. The present research focuses on individual mobility prediction for an advanced driver assistance system within the In-Vehicle Information Systems domain. Specifically, we propose a novel approach based on the framework of Dynamic Neural Fields that learns the traveling routines of a vehicle, including the drivers, passengers, and objects. In our neurodynamics approach, the learning takes place continually and online, allowing the system to accommodate changes in traveling habits over time. The proposed strategy goes beyond learning the mobility routines of a vehicle, enabling predictive assistance for questions like When is the next departure? Where to go? but also extended assistance such as predicting Who is the next driver? Which passengers/objects will enter/leave? We evaluate the performance of the approach in predicting trip information on 11-week datasets of real traveling routines. A vehicle with such cognitive abilities has the potential to improve drivers’ readiness, enhance trip comfort, and prevent objects from being forgotten.

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