Next generation intelligent transportation systems (ITS) of future road traffic monitoring will be required to provide reports of traffic congestion, road conditions, and driver behavior. Monitoring road surface conditions benefits drivers and the community. Road surface anomalies contribute to increased risk of traffic accidents, reduced driver comfort and increased wear of vehicles. Modern automobiles are already equipped with an abundance of technologies: GPS for navigation purposes; inertial sensors (accelerometers and gyroscopes) that monitor vehicle dynamics; vision and radar sensors for blind spot display; proximity sensors and lane departure warning systems.

Our research group devised a system for intelligent sensing for drivers, which we called iDriveSense. iDriveSense integrates vehicular and smartphone sensors within and amongst vehicles to provide robust road monitoring, driver profiles and route recommendations. Smartphones used inside land vehicles acquire data from the vehicle’s onboard sensors (through wireless connections such as bluetooth) and synchronize it with its own measurements of acceleration, angular rotation, and heading. This data is then filtered, processed, and sent to a road information system (RIS) cloud where it is used to build road and driver information repositories. Robustness is achieved through calibration and cross-referencing on two levels: a driver level and a cloud multi-driver level. We developed techniques for learning driver competence and route preferences.

While iDriveSense can benefit from the prevalence of sensors and processing in intelligent connected vehicles, it is intended for use with current automobiles in the market as well. These represent 71% of drivers who do not own vehicles with built-in connectivity but still desire the road and traffic services provided by such connectivity. iDriveSense transforms the vehicle into a smart node capable of providing vital information for road safety. Our solutions are not intended to replace existing RIS installations and/or navigation systems, but rather provide such systems with real-time robust information that can significantly improve the services they offer. Our research created unique design and integration specifications and new service and information management techniques, as well as algorithms to facilitate a real-time, ubiquitous RIS. The deployment of our research will contribute to reducing congestion and accidents on roads, enabling road labeling based on accessibility and conditions, providing personalized alerts and route recommendations, and improving road safety for all drivers.