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Driving in an urban environment is hectic and often adventurous. Getting accurate routing instructions, finding parking spots, receiving customized information that helps individual drivers reach their destination will significantly reduce the stress of driving, save fuel and reduce unnecessary delays and pollution levels. In this paper we present a system that combines smart navigation with intelligent parking assist and driver diagnostics to considerably improve driving comfort, safety and mobility in an urban environment. The smart navigation employs an on line traffic simulator which provides traffic predictions and improves the accuracy of existing navigation systems which rely on limited traffic data. The intelligent parking assist system predicts the availability of parking at the start of the journey and these predictions get updated as the destination is approached. The system uses machine learning to understand the habits and preferences of the individual driver so that the preferred parking availability information is presented to the driver. The driver diagnostics part learns the driving characteristics of the driver i.e. whether aggressive, semi aggressive or passive, reaction times, following distances etc. and provides this information to the smart navigation and parking assist system for better estimation of travel times. In addition, it can be used to support collision warnings and other driver assist devices. The proposed system has been successfully demonstrated using an AUDI vehicle in the area of Los Angeles and San Francisco.
Ioannou et al. (Fri,) studied this question.