  Current outdoor localization techniques fail to provide the required accuracy
for estimating the car's lane. In this paper, we present LaneQuest: a system
that leverages the ubiquitous and low-energy inertial sensors available in
commodity smart-phones to provide an accurate estimate of the car's current
lane. LaneQuest leverages hints from the phone sensors about the surrounding
environment to detect the car's lane. For example, a car making a right turn
most probably will be in the right-most lane, a car passing by a pothole will
be in a specific lane, and the car's 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's 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
260Km driving traces by 13 drivers in different cities shows that LaneQuest can
detect the different lane-level anchors with an average precision and recall of
more than 90%. This leads to an accurate detection of the exact car's lane
position 80% of the time, increasing to 89% 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.
