Abstract: A method and a system that uses standard cell-phone sensors to provide accurate and energy-efficient outdoor localization suitable for mobile navigation design. System and method employs a dead-reckoning localization approach and leverages these road landmarks, among others such as virtual landmarks, to reset the accumulated error and achieve accurate localization. For consuming low energy the system and method uses only energy-efficient sensors or sensors that are already running for other purposes. We present the design of System and method of leveraging crowd-sourcing to automatically learn virtual landmarks and their locations. Tests of the system and method on android devices in both city and highway driving show the accuracy of results for local cell phones to within 8.4 m median error in city roads and 16.6 m on highways. Moreover, compared to GPS and other systems, the battery lifetime was extended by 347%, achieving even better localization results than GPS.

Abstract: Knowledge of the vehicle's lane position is required for several location-based services such as advanced driver assistance systems, driverless cars, and predicting driver's intent, among many other emerging applications. We present LaneQuest: a system and method that leverages the ubiquitous and low-energy inertial sensors available in commodity smart-phones to provide an accurate estimate of the vehicle's current lane. LaneQuest leverages the phone sensors about the surrounding environment to detect the vehicle's lane. For example, a vehicle making a right turn most probably will be in the right-most lane, a vehicle passing by a pothole will be in a specific lane and the vehicle angular velocity when driving through a curve reflects its lane. The ambiguous location, sensors noise, and fuzzy lane anchors; LaneQuest employs a novel probabilistic lane estimation algorithm.

Abstract: A method and a system that uses standard cell-phone sensors to provide accurate and energy-efficient outdoor localization suitable for mobile navigation design. System and method employs a dead-reckoning localization approach and leverages these road landmarks, among others such as virtual landmarks, to reset the accumulated error and achieve accurate localization. For consuming low energy the system and method uses only energy-efficient sensors or sensors that are already running for other purposes. We present the design of System and method of leveraging crowd-sourcing to automatically learn virtual landmarks and their locations. Tests of the system and method on android devices in both city and highway driving show the accuracy of results for local cell phones to within 8.4 m median error in city roads and 16.6 m on highways. Moreover, compared to GPS and other systems, the battery lifetime was extended by 347%, achieving even better localization results than GPS.