9,579 research outputs found

    The path inference filter: model-based low-latency map matching of probe vehicle data

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    We consider the problem of reconstructing vehicle trajectories from sparse sequences of GPS points, for which the sampling interval is between 10 seconds and 2 minutes. We introduce a new class of algorithms, called altogether path inference filter (PIF), that maps GPS data in real time, for a variety of trade-offs and scenarios, and with a high throughput. Numerous prior approaches in map-matching can be shown to be special cases of the path inference filter presented in this article. We present an efficient procedure for automatically training the filter on new data, with or without ground truth observations. The framework is evaluated on a large San Francisco taxi dataset and is shown to improve upon the current state of the art. This filter also provides insights about driving patterns of drivers. The path inference filter has been deployed at an industrial scale inside the Mobile Millennium traffic information system, and is used to map fleets of data in San Francisco, Sacramento, Stockholm and Porto.Comment: Preprint, 23 pages and 23 figure

    Context-aware GPS Integrity Monitoring for Intelligent Transport Systems (ITS)

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    The integrity of positioning systems has become an increasingly important requirement for location-based Intelligent Transports Systems (ITS). The navigation systems, such as Global Positioning System (GPS), used in ITS cannot provide the high quality positioning information required by most services, due to the various type of errors from GPS sensor, such as signal outage, and atmospheric effects, all of which are difficult to measure, or from the map matching process. Consequently, an error in the positioning information or map matching process may lead to inaccurate determination of a vehicle’s location. Thus, the integrity is require when measuring both vehicle’s positioning and other related information such as speed, to locate the vehicle in the correct road segment, and avoid errors. The integrity algorithm for the navigation system should include a guarantee that the systems do not produce misleading or faulty information; as this may lead to a significant error arising in the ITS services. Hence, to achieve the integrity requirement a navigation system should have a robust mechanism, to notify the user of any potential errors in the navigation information. The main aim of this research is to develop a robust and reliable mechanism to support the positioning requirement of ITS services. This can be achieved by developing a high integrity GPS monitoring algorithm with the consideration of speed, based on the concept of context-awareness which can be applied with real time ITS services to adapt changes in the integrity status of the navigation system. Context-aware architecture is designed to collect contextual information about the vehicle, including location, speed and heading, reasoning about its integrity and reactions based on the information acquired. In this research, three phases of integrity checks are developed. These are, (i) positioning integrity, (ii) speed integrity, and (iii) map matching integrity. Each phase uses different techniques to examine the consistency of the GPS information. A receiver autonomous integrity monitoring (RAIM) algorithm is used to measure the quality of the GPS positioning data. GPS Doppler information is used to check the integrity of vehicle’s speed, adding a new layer of integrity and improving the performance of the map matching process. The final phase in the integrity algorithm is intended to verify the integrity of the map matching process. In this phase, fuzzy logic is also used to measure the integrity level, which guarantees the validity and integrity of the map matching results. This algorithm is implemented successfully, examined using real field data. In addition, a true reference vehicle is used to determine the reliability and validity of the output. The results show that the new integrity algorithm has the capability to support a various types of location-based ITS services.Saudi Arabia Cultural Burea

    A high accuracy fuzzy logic based map matching algorithm for road transport

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    Recent research on map matching algorithms for land vehicle navigation has been based on either a conventional topological analysis or a probabilistic approach. The input to these algorithms normally comes from the global positioning system (GPS) and digital map data. Although the performance of some of these algorithms is good in relatively sparse road networks, they are not always reliable for complex roundabouts, merging or diverging sections of motorways, and complex urban road networks. In high road density areas where the average distance between roads is less than 100 m, there may be many road patterns matching the trajectory of the vehicle reported by the positioning system at any given moment. Consequently, it may be difficult to precisely identify the road on which the vehicle is travelling. Therefore, techniques for dealing with qualitative terms such as likeliness are essential for map matching algorithms to identify a correct link. Fuzzy logic is one technique that is an effective way to deal with qualitative terms, linguistic vagueness, and human intervention. This article develops a map matching algorithm based on fuzzy logic theory. The inputs to the proposed algorithm are from GPS augmented with data from deduced reckoning sensors to provide continuous navigation. The algorithm is tested on different road networks of varying complexity. The validation of this algorithm is carried out using high precision positioning data obtained from GPS carrier phase observables. The performance of the developed map matching algorithm is evaluated against the performance of several well-accepted existing map matching algorithms. The results show that the fuzzy logic-based map matching algorithm provides a significant improvement over existing map matching algorithms both in terms of identifying correct links and estimating the vehicle position on the links

    A dynamic two-dimensional (D2D) weight-based map-matching algorithm

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    Existing map-Matching (MM) algorithms primarily localize positioning fixes along the centerline of a road and have largely ignored road width as an input. Consequently, vehicle lane-level localization, which is essential for stringent Intelligent Transport System (ITS) applications, seems difficult to accomplish, especially with the positioning data from low-cost GPS sensors. This paper aims to address this limitation by developing a new dynamic two-dimensional (D2D) weight-based MM algorithm incorporating dynamic weight coefficients and road width. To enable vehicle lane-level localization, a road segment is virtually expressed as a matrix of homogeneous grids with reference to a road centerline. These grids are then used to map-match positioning fixes as opposed to matching on a road centerline as carried out in traditional MM algorithms. In this developed algorithm, vehicle location identification on a road segment is based on the total weight score which is a function of four different weights: (i) proximity, (ii) kinematic, (iii) turn-intent prediction, and (iv) connectivity. Different parameters representing network complexity and positioning quality are used to assign the relative importance to different weight scores by employing an adaptive regression method. To demonstrate the transferability of the developed algorithm, it was tested by using 5,830 GPS positioning points collected in Nottingham, UK and 7,414 GPS positioning points collected in Mumbai and Pune, India. The developed algorithm, using stand-alone GPS position fixes, identifies the correct links 96.1% (for the Nottingham data) and 98.4% (for the Mumbai-Pune data) of the time. In terms of the correct lane identification, the algorithm was found to provide the accurate matching for 84% (Nottingham) and 79% (Mumbai-Pune) of the fixes obtained by stand-alone GPS. Using the same methodology adopted in this study, the accuracy of the lane identification could further be enhanced if the localization data from additional sensors (e.g. gyroscope) are utilized. ITS industry and vehicle manufacturers can implement this D2D map-matching algorithm for liability critical and in-vehicle information systems and services such as advanced driver assistant systems (ADAS)

    Advanced Map Matching Technologies and Techniques for Pedestrian/Wheelchair Navigation

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    Due to the constantly increasing technical advantages of mobile devices (such as smartphones), pedestrian/wheelchair navigation recently has achieved a high level of interest as one of smartphones’ potential mobile applications. While vehicle navigation systems have already reached a certain level of maturity, pedestrian/wheelchair navigation services are still in their infancy. By comparing vehicle navigation systems, a set of map matching requirements and challenges unique in pedestrian/wheelchair navigation is identified. To provide navigation assistance to pedestrians and wheelchair users, there is a need for the design and development of new map matching techniques. The main goal of this research is to investigate and develop advanced map matching technologies and techniques particular for pedestrian/wheelchair navigation services. As the first step in map matching, an adaptive candidate segment selection algorithm is developed to efficiently find candidate segments. Furthermore, to narrow down the search for the correct segment, advanced mathematical models are applied. GPS-based chain-code map matching, Hidden Markov Model (HMM) map matching, and fuzzy-logic map matching algorithms are developed to estimate real-time location of users in pedestrian/wheelchair navigation systems/services. Nevertheless, GPS signal is not always available in areas with high-rise buildings and even when there is a signal, the accuracy may not be high enough for localization of pedestrians and wheelchair users on sidewalks. To overcome these shortcomings of GPS, multi-sensor integrated map matching algorithms are investigated and developed in this research. These algorithms include a movement pattern recognition algorithm, using accelerometer and compass data, and a vision-based positioning algorithm to fill in signal gaps in GPS positioning. Experiments are conducted to evaluate the developed algorithms using real field test data (GPS coordinates and other sensors data). The experimental results show that the developed algorithms and the integrated sensors, i.e., a monocular visual odometry, a GPS, an accelerometer, and a compass, can provide high-quality and uninterrupted localization services in pedestrian/wheelchair navigation systems/services. The map matching techniques developed in this work can be applied to various pedestrian/wheelchair navigation applications, such as tracking senior citizens and children, or tourist service systems, and can be further utilized in building walking robots and automatic wheelchair navigation systems

    Human Motion Trajectory Prediction: A Survey

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    With growing numbers of intelligent autonomous systems in human environments, the ability of such systems to perceive, understand and anticipate human behavior becomes increasingly important. Specifically, predicting future positions of dynamic agents and planning considering such predictions are key tasks for self-driving vehicles, service robots and advanced surveillance systems. This paper provides a survey of human motion trajectory prediction. We review, analyze and structure a large selection of work from different communities and propose a taxonomy that categorizes existing methods based on the motion modeling approach and level of contextual information used. We provide an overview of the existing datasets and performance metrics. We discuss limitations of the state of the art and outline directions for further research.Comment: Submitted to the International Journal of Robotics Research (IJRR), 37 page
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