Abstract
Lane-level positioning is required for several location-based services such as advanced driver assistance systems, driverless cars, predicting driver’s intent, among many other emerging applications. Yet, current outdoor localization techniques fail to provide the required accuracy for estimating the car’s lane.
In this paper, we present LaneQuest: an accurate and energy-efficient smartphone-based lane detection system. LaneQuest leverages hints from the ubiquitous and low-power inertial sensors available in commodity off-the-shelf smartphones about the car’s motion and its surrounding environment to provide an accurate estimate of the car’s current lane position. For example, a car making a u-turn, most probably, will be in the left-most lane; a car passing by a pothole will be in the pothole’s lane; and the car angular velocity when driving through a curve reflects its lane. Our investigation shows that there are amble opportunities in the environment, i.e. lane “anchors”, that provide cues about the car lane. To handle the ambiguous location, sensors noise, and fuzzy lane anchors; LaneQuest employs a novel probabilistic lane estimation algorithm. Furthermore, it uses an unsupervised crowd-sourcing approach to learn the position and lane span distribution of the different lane-level anchors.
Our evaluation results from implementation on different Android devices and driving traces in different cities covering 260 km shows that LaneQuest can detect the different lane-level landmarks with an average precision and recall of more than 91%. This leads to an accurate detection of the exact car lane position 84% of the time, increasing to 92% of the time to within one lane. This comes with a low-energy footprint, allowing LaneQuest to be implemented on the energy-constrained mobile devices.