A map matching algorithm based on a particle filter

Parallelization is the key method to speedup advanced driving assistance systems (ADAS) and autonomous vehicle applications especially using emerging multicore and manycore processors. Localization and map matching are among the fundamental parts of such applications. This paper presents a parallel implementation and performance analysis of particle filter based vehicle localization and map-matching algorithm both on a multicore processor using Open Multi-Processing (OpenMP) and on a graphics processing unit (GPU) using the Compute Unified Device Architecture (CUDA). In the proposed implementation, GPS and odometer data is fused with digital map information. Tests were performed on real data captured in vehicle environment comprising various speed and road conditions. Test results show that speedups up to 75 times can be achieved on parallel GPU implementation over sequential counterpart. As this speedup complies with real-time performance requirements, high computational cost of using map topology information with large number of particles in particle filter implementation is proven to be handled by emerging technologies. The effect of number of particles on error rate of localization and map matching is also investigated.

With the increasing availability of GPS-equipped mobile devices, location-based services have become an integral part of everyday life. Among one of the initial steps of positioning data management, map matching aims to reduce the uncertainty in a trajectory by matching the GPS points to the road network on a digital map. Most existing work has focused on estimating the likelihood of a candidate route based on the GPS observations, while neglecting to model the probability of a route choice from the perspective of drivers. In this work, we propose a novel feature-based map matching algorithm that estimates the cost of a candidate route based on both GPS observations and human factors. To take human factors into consideration is highly important, especially when dealing with low sampling rate data where most of the movement details are lost. Additionally, we simultaneously analyze a subsequence of coherent GPS points by utilizing a new segment-based probabilistic map matching strategy, which is less susceptible to the noisiness of the positioning data. We have evaluated both the offline and the online versions of our proposed approach on a public large-scale GPS dataset, which consists of 100 trajectories distributed all over the world. The experimental results show that our method is robust to sparse data with large sampling intervals (e.g., 60s ∼ 300s) and challenging track features (e.g., u-turns and loops). Measurements including map matching accuracy and system efficiency have been thoroughly evaluated and discussed. Compared with two state-of-the-art map matching algorithms, our method substantially reduces the route mismatch error by 6.4% ∼ 32.3% (either offline or online with the window size set to 360s), with a slight increase in terms of the processing time. The experimental results show that our proposed method obtains the state-of-the-art map matching results in all the different combinations of sampling rates and challenging features.

In this research, a passenger-centric passenger information system is proposed, which uses crowd sourcing and mobile phones so that passengers are not only information consumers but are also providers of information to the system. Passengers can allow the system to track their location through their smart phones when they are traveling on public transportation; this will compensate for the lack of a vehicle tracking system in public transportation, particularly in nonurban areas. Map-matching (MM) algorithms integrate location data (i.e.,latitude and longitude) obtained from positioning sensors (in this case, mobile GPS) with a digital geographic information system (GIS) road map. Any map-matching algorithm first identifies the road link on which a vehicle is traveling and then determines the vehicle's location on that road segment. In the proposed information system, at a given point of time, a number of vehicle locations (latitude and longitude) are received from passengers traveling on a bus. In order to provide a precise vehicle location at a given point of time, a novel map-matching algorithm using fuzzy logic, which integrates multiple vehicle locations (obtained from passenger's smart phones) with a GIS road map, has been developed. The developed map-matching algorithm was tested using real-world data collected on four different bus routes in Aberdeenshire, Scotland, and also GPS data collected in and around Nottingham, United Kingdom. It was identified that the developed MM algorithm is efficient and capable of supporting the proposed passenger information system. (C) 2013 American Society of Civil Engineers.

Map-Matching Poor-Quality GPS Data in Urban Environments: The pgMapMatch Package

GPS (Global Positioning System) has become an integral part of a vehicle system which provides speed, time, direction etc besides the navigation data. Speed is one of the primary attributes of vehicle accident. Many lives could have been saved if emergency service could receive accident information timely. This paper proposes to detect an accident from the map matched position of a vehicle by utilizing the GPS speed data and map matching algorithm and send accident location to an Alert Service Center. The GPS provides speed and position in every 0.1 second. The position data will be used in the map matching algorithm to locate the vehicle on the road. The present speed will be compared with the previous speed in every 0.1 second through a Microcontroller Unit. Whenever the speed will be falling below the safe calculated threshold speed, the system will generate an accident situation. It will check the vehicle location from map matching module and generate an accident situation if the vehicle is found outside the road network. This will reduce the false accident detection drastically. The map matched accident location is then sent by utilizing the GSM network. The proposed system will save many accident victims with timely rescue.

Map Matching Algorithm for Real-Time Data Processing of Non-route GPS Data in Seoul

Purpose There is a certain error in the satellite positioning of the vehicle. The error will cause the drift point of the positioning point, which makes the vehicle trajectory shift to the real road. This paper aims to solve this problem. Design/methodology/approach The key technology to solve the problem is map matching (MM). The low sampling frequency of the vehicle is far from the distance between adjacent points, which weakens the correlation between the points, making MM more difficult. In this paper, an MM algorithm based on priority rules is designed for vehicle trajectory characteristics at low sampling frequencies. Findings The experimental results show that the MM based on priority rule algorithm can effectively match the trajectory data of low sampling frequency with the actual road, and the matching accuracy is better than other similar algorithms, the processing speed reaches 73 per second. Research limitations/implications In the algorithm verification of this paper, although the algorithm design and experimental verification are considered considering the diversity of GPS data sampling frequency, the experimental data used are still a single source. Originality/value Based on the GPS trajectory data of the Ministry of Transport, the experimental results show that the accuracy of the priority-based weight-based algorithm is higher. The accuracy of this algorithm is over 98.1 per cent, which is better than other similar algorithms.

The process of map matching takes a sequence of possibly noisy GPS coordinates from a vehicle trace and estimates the actual road positions---a crucial first step needed by many GPS applications. There has been a plethora of methods for map matching published, but most of them are evaluated on low-noise datasets obtained from a planned route. And comparisons with other methods are very limited. Based on our previous unifying framework used to catalog different mathematical formulas in many published methods, we evaluate representative algorithms using the low-noise dataset from the GIS Cup 2012 and a high-noise dataset collected from Shanghai downtown. Our experiments reveal that global max-weight and global geometrical map matching methods are the most accurate, but each has its weaknesses. We therefore propose a new map matching algorithm that integrates Fréchet distance with global weight optimization, which is more accurate across all sampling intervals.

Map matching can provide useful traffic information by aligning the observed trajectories of vehicles with the road network on a digital map. It has an essential role in many advanced intelligent traffic systems (ITSs). Unfortunately, almost all current map-matching approaches were developed for GPS trajectories generated by probe sensors mounted in a few vehicles and cannot deal with the trajectories of massive vehicle samples recorded by fixed sensors, such as camera detectors. In this paper, we propose a novel map-matching model termed Fixed-MM, which is designed specifically for fixed sensor data. Based on two key observations from real-world data, Fixed-MM considers (1) the utility of each path and (2) the travel time constraint to match the trajectories of fixed sensor data to a specific path. Meanwhile, with the laws derived from the distribution of GPS trajectories, a path generation algorithm was developed to search for candidates. The proposed Fixed-MM was examined with field-test data. The experimental results show that Fixed-MM outperforms two types of classical map-matching algorithms regarding accuracy and efficiency when fixed sensor data are used. The proposed Fixed-MM can identify 68.38% of the links correctly, even when the spatial gap between the sensor pair is increased to five kilometers. The average computation time spent by Fixed-MM on one point is only 0.067 s, and we argue that the proposed method can be used online for many real-time ITS applications.

In this paper, a map-matching (MM) algorithm which combines an estimated position with digital road data is proposed. The presented algorithm using a virtual track is appropriate for a MEMS-based pedestrian dead reckoning (PDR) system, which can be used in mobile devices. Most of the previous MM algorithms are for car navigation systems and GPS-based navigation system, so existing MM algorithms are not appropriate for the pure DR-based pedestrian navigation system. The biggest problem of previous MM algorithms is that they cannot determine the correct road segment (link) due to the DR characteristics. In DR-based navigation system, the current position is propagated from the previous estimated position. This means that the MM result can be placed on a wrong link when MM algorithm fails to decide the correct link at once. It is a critical problem. Previous algorithms never overcome this problem because they did not consider pure DR characteristics. The MM algorithm using the virtual track is proposed to overcome this problem with improved accuracy. Performance of the proposed MM algorithm was verified by experiments.

Complex road networks, inaccurate GPS receiver output, low sampling rate and many other associated issues pose real challenges for map matching process. Genetic algorithms have recently been trialed for rendering GPS fix on digital maps. This manuscript introduces an improvised genetic algorithm named as post-processing genetic-inspired map matching (GiMM) algorithm. The proposed GiMM intends to mitigate inherent challenges associated with originally proposed genetic algorithm for map matching. The fitness function used by GiMM makes use of Bucket Dijkstra’s and fast dynamic time wrapping (FDTW) algorithms to render GPS information on digital maps. Bucket Dijkstra’s suggests the shortest path available in between two points, and FDTW is responsible for comparing two data series. Unlike traditional genetic algorithm for map matching, GiMM was evaluated on sparse as well as dense GPS data. The performance of the GiMM algorithm was evaluated in real time using OpenStreetMap data and GPS dataset mapped onto a road network of 82 km. GiMM uses population size, generation count, accuracy and execution time as input parameters. A maximum accuracy of 99.4% with root-mean-square error 0.06 was observed, whereas a minimum of 60% accuracy was observed at 0.47 root-mean-square error. Number of iterations and population size were concluded to be the most influential parameters for the performance of genetic algorithms for map matching.

The computation speed and output latency of map matching are important considerations when processing location data, especially smartphone-generated noisy and sparse data, from a large number of users for real-time transportation applications. In this paper, we examine the factors affecting the efficiency of online map matching algorithms that are based on probabilistic sequence models such as Hidden Markov Models (HMM) and present several heuristic optimizations to improve their speed and latency. As shortest path computations account for most of the running time of probabilistic map matching algorithms, we propose a method for reducing the total number of such computations by pruning unlikely states in the probabilistic sequence model. Furthermore, we speed up the one-to-many shortest path computations by limiting the search space to an elliptical area that encompasses all the targeted destinations. We present a technique for reducing the latency of the Viterbi algorithm used to find the most likely state sequence in a HMM or a similar model. This technique enables the early output of partial state sequences based on an estimate of the probability of a state being part of the eventual most likely sequence. Experiments using real-world location data show that the heuristic optimizations significantly reduce the running time and output latency with negligible loss of accuracy.

Map information and sensor information is considered to be an effective way to assist indoor wireless location. Wireless location combined with map information and Sensor information can solve many problem such as positioning point cross the wall, falling into the wrong room. In this paper, a new indoor map matching algorithm was proposed based on particle filter. The indoor positioning system could be considered as non-linear system, so Non-parametric Monte Carlo (Monte Carlo) simulation method could achieve recursive Bayesian filtering, at the same time, the indoor GIS data, and sensor information, wireless location information was incorporated into the weight value of the particles to revise the observations constantly. Many experiments show that the indoor positioning accuracy is improved.

Map matching is a technique used to identify which path a vehicle is travelling on in a road map. Since it is a crucial fundamental step for a wide range of transportation applications, many map-matching algorithms have been devised ranging from simple geometric calculation methods to more sophisticated methods. However, the study of spatial relationship patterns between GPS coordinates and road segments mapped with map-matching has not received enough attention from researchers. This paper presents a framework, called Proxy Map Matching (PMM), to learn such patterns using machine learning techniques. However, we find that solely employing machine learning techniques on such data is not sufficient to capture the patterns. Solving this problem that way results in an inaccurate proxy model. In PMM, we construct several proxy map matchers and assign them to each group of data based on their spatial proximity, thereby achieving accuracy improvement. An experiment on real-world data shows that the framework achieves above 85% accuracy with integration of machine learning techniques, and outperforms the methods which solely employ machine learning techniques significantly. Moreover, the proposed proxy model can perform very fast matching. For 14,177 GPS coordinate pairs per second, we achieve 88.4% accuracy.

Accuracy of independent GPS navigation systems is tied to the map-matching (MM) algorithm, which overcomes GPS error and traffic map error to map ve-hicle GPS tracks onto corresponding roads. This paper presents a map-matching process compatible with junction adjusting. An innovative idea is expressed in pa-per to improve the accuracy of intersection node at vector map by utilizing the large amounts of GPS trajectory data, which come from map-matching procedure. With the adaptively corrected digital map, the simulation result gave better map-matching effect, and showed the effectiveness of map-matching compatible with junction adjusting.