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.
@cite_11: A robust and efficient lane detection system is an essential component of Lane Departure Warning Systems, which are commonly used in many vision-based Driver Assistance Systems (DAS) in intelligent transportation. Various computation platforms have been proposed in the past few years for the implementation of driver assistance systems (e.g., PC, laptop, integrated chips, PlayStation, and so on). In this paper, we propose a new platform for the implementation of lane detection, which is based on a mobile phone (the iPhone). Due to physical limitations of the iPhone w.r.t. memory and computing power, a simple and efficient lane detection algorithm using a Hough transform is developed and implemented on the iPhone, as existing algorithms developed based on the PC platform are not suitable for mobile phone devices (currently). Experiments of the lane detection algorithm are made both on PC and on iPhone.
Computer vision techniques have been proposed to detect the car lane. For example, in @cite_11 authors use the iPhone camera to detect the lane markings. Using cameras for lane detection, however, is highly susceptible to errors due to various factors such as lighting condition (e.g. night time, sun glare, headlight glare, shadows from nearby buildings, etc), bad weather conditions (e.g. snow, rain), and other environmental noise (e.g., faded lane marks, surrounding objects like buildings, parked cars, etc). Moreover, both camera and GPS have high energy requirements for the limited phone battery.