Robust Crowdsensing for Intelligent Road Services

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The success of intelligent transportation systems (ITS) relies on their ability to provide drivers and municipalities with accurate real-time information. Crowdsourcing has been shown effective in navigation systems where traffic congestion information is updated by drivers. Robust Crowdsensing for Intelligent Road Services presents a framework to collect crowd-based information, monitoring road conditions and hazards; and, driver-based information, including driving style, preferences, skills and experience, to build representative driver profiles. Our proposed system, iDriveSense, integrates sensor technologies available in both the vehicles and the driver smartphones to provide advanced, robust localization and accurate monitoring of vehicle dynamics and driver behaviour. Robustness is achieved through calibration and cross-referencing on two levels: a driver level and a cloud multi-driver level. Four PhD and four MSc students will receive comprehensive training where there is increasing demand from drivers and from the automobile manufacturing and software sectors. Expertise in the technologies of intelligent transportation, road monitoring, driver profile building, GPS and inertial navigation will be gained through hands-on experience in designing and realizing mobile applications under Android and iOS platforms, real-time embedded system design, hardware integration and software development. The necessity for safer roads has created a demand for this skill-set both in Canada and globally. Our research team includes the expertise of our industrial partner, Intelligent Mechatronic Systems Inc. (IMS) of Waterloo, ON. IMS has emerged as a world leader in developing in-vehicle connected-car solutions like iLane® and DriveSync®. Our research will reduce congestion and accidents on roads by enabling road labeling based on accessibility and conditions and providing personalized alerts to improve road safety for all drivers on Canadian roads.