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_4: We present Dejavu, a system that uses standard cell-phone sensors to provide accurate and energy-efficient outdoor localization suitable for car navigation. Our analysis shows that different road landmarks have a unique signature on cell-phone sensors; For example, going inside tunnels, moving over bumps, going up a bridge, and even potholes all affect the inertial sensors on the phone in a unique pattern. Dejavu employs a dead-reckoning localization approach and leverages these road landmarks, among other automatically discovered abundant virtual landmarks, to reset the accumulated error and achieve accurate localization. To maintain a low energy profile, Dejavu uses only energy-efficient sensors or sensors that are already running for other purposes. We present the design of Dejavu and how it leverages crowd-sourcing to automatically learn virtual landmarks and their locations. Our evaluation results from implementation on different android devices in both city and highway driving show that Dejavu can localize cell phones to within 8.4 m median error in city roads and 16.6 m on highways. Moreover, compared to GPS and other state-of-the-art systems, Dejavu can extend the battery lifetime by 347 , achieving even better localization results than GPS in the more challenging in-city driving conditions.
@cite_22: The majority of today's navigation techniques for intelligent transportation systems use global positioning systems (GPS) that can provide position information with bounded errors. However, due to the low accuracy that is experienced with standard GPS, it is difficult to determine a vehicle's position at lane level. Using a Markov-based approach based on sharing information among a group of vehicles that are traveling within communication range, the lane positions of vehicles can be found. The algorithm's effectiveness is shown in both simulations and experiments with real data.
@cite_32: GPS positioning and map-matching are key technologies used in Car Navigation Systems. Next generation of Cooperative Vehicle Infrastructure Systems (CVIS) will surely require positioning component with higher accuracy, integrity and availability, for instance for the needs of lanelevel applications. The concept of Map Aided Location (MAL) was introduced recently: it consists in considering the map contents (geometry and topology) as constraints in the positioning problem. The main contribution of the work reported in this article concerns the evolution model of the vehicle : whereas previous research investigations were based on usual models in plane coordinates, we propose here an evolution model adapted to the local curvature of the road. Therefore, road constraints are emphasized, particularly when GPS outages occur. An enhanced map (Emap) - where all lanes are represented with decimeter accuracy - is used in this Map Aided Location process.
@cite_24: A localization system that exploits L1-GPS estimates, vehicle data, and features from a video camera as well as lane markings embedded in digital navigation maps is presented. A sensitivity analysis of the detected lane markings is proposed in order to quantify both the lateral and longitudinal errors caused by 2D-world hypothesis violation. From this, a camera observation model for vehicle localization is proposed. The paper presents also a method to build a map of the lane markings in a first stage. The solver is based on dynamical Kalman filtering with a two-stage map-matching process which is described in details. This is a software-based solution using existing automotive components. Experimental results in urban conditions demonstrate an significant increase in the positioning quality.
@cite_10: Lane-level positioning and map matching are some of the biggest challenges for navigation systems. Additionally, in safety applications or in those with critical performance requirements (such as satellite-based electronic fee collection), integrity becomes a key word for the navigation community. In this scenario, it is clear that a navigation system that can operate at the lane level while providing integrity parameters that are capable of monitoring the quality of the solution can bring important benefits to these applications. This paper presents a pioneering novel solution to the problem of combined positioning and map matching with integrity provision at the lane level. The system under consideration hybridizes measurements from a global navigation satellite system (GNSS) receiver, an odometer, and a gyroscope, along with the road information stored in enhanced digital maps, by means of a multiple-hypothesis particle-filter-based algorithm. A set of experiments in real environments in France and Germany shows the very good results obtained in terms of positioning, map matching, and integrity consistency, proving the feasibility of our proposal.
Current state-of-the-art localization techniques can only provide location estimates with an average accuracy around 10m @cite_4 , which is not suitable for lane-level localization. To overcome this, researchers proposed different techniques @cite_10 @cite_22 @cite_32 that are based on using an external accurate GPS device (L1 GPS or DGPS) for localization and combining it with other sensors to provide more accurate lane-level location estimates. For example, in @cite_10 authors fuse a high-accuracy GNSS receiver, an odometer, and a gyroscope, along with an enhanced digital map (that describes the road geometry using a particle filter-based algorithm) to position the user based on a combined GNSS-dead-reckoning approach and map matches her to her lane. Similarly, in @cite_24 authors fused an L1-GPS device, camera and an enhanced digital map that stores lane markings information using a dynamical Kalman filter with map matching to get an accurate user position.