New generation Car Navigation Systems enhancing Human-Computer Interaction and exploiting sensors and machine learning on the smartphone

This Doctoral Consortium submission proposes to investigate how to provide user-centred, personalized navigation services by ex- ploiting data collected from smartphones and applying machine- learning techniques. The presented research focuses on inferring useful information about the driver’s context, routines, and habits to predict their travel needs and offer valuable services. Currently, we are tackling smart-parking-related issues, such as automatic detection of parking events and cruising-for-parking detection. This work discusses the types of data collected from smartphones, the challenges in data processing and analysis, and the potential benefits of integrating this data into car navigation systems. More- over, we highlight the importance of context-aware interfaces and implicit interaction to reduce the driver’s cognitive load and dis- traction, thus improving safety during trips.