Prototype design and implementation for urban area in-car navigation system

This paper introduces the in-car navigation system prototype development and test results under the context of Singapore. In the system, integrated GPS/DR is used for continuous vehicle positioning. A digital road network map database is built for the whole Singapore. The database is designed to support map matching, route determination, and route guidance functions. The prototype system demonstrates GPS positioning of the vehicle in real-time, tracking the vehicle onto a digital map with fix updates every one or two seconds. With the map matched vehicle location on the map, voice and/or text guidance is provide to the driver. The vehicle position can also be transferred to control centre through communication data link for the purpose of fleet management.

Accurate vehicle localization is very important for various applications of intelligent transportation systems (ITS) including cooperative driving, collision avoidance, and vehicle navigation. In this paper, a constrained unscented Kalman filter (CUKF) algorithm is proposed to fuse differential global position system (DGPS), inertial navigation system (INS) and digital map to estimate the vehicle states. Using the road geometry information obtained from a digital map database, some state constraints can be formed. The measurements of DGPS and INS are used to set up the dynamic and measurement equations of the nonlinear filtering. The vehicle states are first estimated by the loosely coupled DGPS/INS system and the unconstrained UKF, and then the UUKF estimates are projected into the state constraints to obtain the final CUKF estimates. Synthetic and real data are used to evaluate the performance of the CUKF algorithm for fusing DGPS, INS and digital map.

<div class="htmlview paragraph">This paper describes a navigation System for private automobiles.</div> <div class="htmlview paragraph">This system shows the accurate position of the vehicle on a CRT map display and also displays useful Information such as the locations of hotels, golf clubs, etc.</div> <div class="htmlview paragraph">A sophisticated map matching method is employed to find the vehicle's position. In this method, a set of correlation coefficients between the locus and the mapped roads is used to determine the vehicle's location statistically. A unique automatic calibration function is also added to the magnetic field sensor to compensate for malfunctioning and detection errors caused by distortion due to the earth's magnetism and to magnetization of the vehicle at points of high field density such as railroad crossings.</div> <div class="htmlview paragraph">In this system, we can see the current position of the vehicle indicated by a pointer on the smooth scrolling map display.</div> <div class="htmlview paragraph">This digital map has scales of 1/25,000, 1/100,000 and 1/400,000 and covers all of Japan with two available display modes: “north up” and “heading up”.</div> <div class="htmlview paragraph">This system also shows the direction and distance to whatever destination the user has chosen.</div> <div class="htmlview paragraph">This navigation system has been installed in Nissan's 1989 Cedric/Gloria and Cima models.</div>

The Fiberoptic Guided Missile (FOG-M) was developed in the U.S. Army's Research, Development, and Engineering Center (RDEC) as a demonstration system for killing armor in an infantry application. The RDEC design uses a television sensor in the nose of the missile for in-flight target acquisition, bringing the video signal down a fiberoptic link that pays out behind the missile as it flies, to a gunner securely hidden in a defiladed launch vehicle. The gunner is able to select the target on a video screen and lock on an autotracker or alternately manually track the target to impact. The system design would use a common warhead for either armor or helicopter targets. The system is currently mounted on the High Mobility, Multipurpose Wheeled Vehicle (HMMWV), containing the gunner's station, launcher, and flight missiles. The gunner's station includes the capability for detailed mission planning, digital map display based on the Defense Mapping Agency's digital map databases, and display of the air battle situation for the gunner. Automatic targeting, control of multiple missiles in the air simultaneously, navigation using a digital correlator, and autotracking of moving targets in cluttered backgrounds with gunner selectable offset tracking capability are also available. The system has a capability to train the gunner using a perspective view scene generator that mimics the video scene he would be presented during missile flight. The scene generator uses the same hardware that displays the digital map, while a simulation of the missile runs in one of the system's computer processors to accurately move the seeker presentation around the scene. All of the other hardware used in embedded training is the same as the actual firing hardware. The system concept has been chosen by the Army as the non-line-of-sight (NLOS) component of the Forward Area Air Defense System (FAADS). The initial design is being upgraded for the MIL SPEC environment to allow early operational evaluation by the user. The lessons learned by these tests will be used to aid the full scale development program beginning in FY88. This paper describes aspects of the system designed by the Army's RDEC, with special emphasis on the mechanics of fiberoptic datalink winding and payout concerns.

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.

This digital geologic map database represents the distribution and character of Cenozoic geologic strata in six 15' quadrangles covering most of the Tillamook Highlands in the northwest Oregon Coast Range. The geologic map was published as Open-File Report 94-21, the Geologic Map of the Tillamook Highlands, Northwest Oregon Coast Range, by Wells and others.

Map matching is the process of finding a match for each GPS point in a vehicle's trajectory to roads on a digital map. Extensive research has been conducted during the last years yielding many algorithms based on different approaches. One of the challenges that face those algorithms is the interruption of GPS signals that occurs specially in dense urban environments. In these cases on-board sensors like odometers and accelerometers can be used temporarily for positioning, however due to the poor accuracy of these methods, the quality of map matching decreases significantly. In this paper, we propose a method that improves the quality of map matching when GPS signals are not available. This method is based on a particle filter using heading and velocity measurement. We evaluate this method through its integration with an existing topological map matching algorithm. We compare the performances when this algorithm is used alone and when associated with the particle filter.

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.

<p>Digital water and energy maps allow fast information retrieval, big data analysis and resources demand prediction for real time responses in 5-G networks. A regulatory systems framework is needed to enable and promote integrated actions grounded on map-based feedback information, to facilitate resources movements and knowledge transfer for water and energy security. At the same time, the proposed regulatory system needs to safeguard national security and personal privacy when general public and the private sectors have access to big databases.</p><p>The Guangdong-Hong Kong-Macao Greater Bay Area (GBA) in China is an initiative on regional economic development involving nine mainland cities and two Special Administrative Regions (SARs). As central policies cannot be efficiently executed in the whole regions, institutional fragmentation could be a prominent barrier to achieve regional water and energy optimum rather than individual city maxima for the water and energy nexus.</p><p>In this study, we propose a systems regulatory framework that integrates natural, urban and social systems across multiple scales in which the relevant laws, policies, decisions and actions are supported by digital maps. On a planning scale, our new regulatory system based on spatial map information promotes optimum uses of natural capitals and ecosystem services (ES). For linking different urban spatial processes on different scales, satellite images and Local Climate Zone (LCZ) maps are used to describe natural environment and urban characteristics from 200km to 10km resolutions for supporting land-use planning laws and estimating regional development carrying capacity to mitigate water and energy insecurity.</p><p>On an operational scale, smart meters and remote sensor systems provide real time water and energy information from a fast developing 5-G network for the proposed digital maps. Forecasted energy and water demands from the digital maps can be used for regional or local environment regulation reinforcement. Proposed spatial maps also improve transboundary collaboration by providing visualisation of legal targets and emission limits. Through digital maps, key agencies and sectors will have a capacity to share transboundary knowledge, information and responsibility, to foster smooth system flows in terms of culture, economy, policy and technology, by active participations and decentralized actions.</p><p>On an evaluation scale, open map information increases the transparency of legal targets and pollution limits. By rapid information retrieval and big data analysis from digital maps, regulators can assess the performance of water and energy security practices.</p><p>In summary, the proposed framework based on LCZ maps for the GBA can be applied to other rapidly developing regions with emerging 5-G networks. The integrated regulatory framework also guides water and energy security practices and transfer central policies to local actions by rapid information retrieval, big data analysis and prediction of demand for real time responses based on digital water and energy maps.</p><p></p><p></p><p></p><p></p><p></p>

One of the main problems of modern navigation systems for cars is location referencing, i.e. the description of a certain location in a digital map ‘A’ and the search of exactly this location in a different digital map ‘B’. In contrast to classical methods of finding a location by means of the surrounding geometry or even the use of attributes like names and types of streets, we propose a purely analytical approach based on the wavelet transform of the digital maps themselves.

Japan Digital Road Map Association (JDRMA) started to construct the digital road map database for the whole Japan in 1988. The first database was completed in 1989, and consequently the updating started in 1990. There are two kinds of data sources for updating the JDRMA database. One is the newly issued topographic maps, which include both roads and other features but are not updated every year. The other is the information from the road administrators; it is used to update the road networks. Every year the information of the newly opened or improved roads is collected and used to update the road network in the database. Other information is also used to supplement the two main information sources. Documents of transportation facilities other than roads and information from the users are utilized. The investigation to improve the updating procedure is continued. >

<div class="htmlview paragraph">Digital maps have large potential to enhance or enable Advanced Driver Assistance Systems (ADAS) by extending driver horizon and therefore to contribute to safer, smarter and cleaner road transport. As a predictive sensor called ADAS Horizon, in-vehicle digital maps are an important source of information providing look-ahead capability for ADAS applications and providing further information for on-board sensors to enhance environment perception.</div> <div class="htmlview paragraph">This paper reports on results from the European Commission project MAPS&ADAS, part of PReVENT Integrated Project, dedicated to the development, test and validation of a standardized interface (ADAS Interface) between ADAS applications and ADAS map data sources for accessing map data regarding vehicle position.</div>

The GPS-based navigation systems provide an accurate positioning of land-vehicles but this service is not always continuous in urban areas. These systems are usually enhanced with dead reckoning sensors, like odometers, and the use of a digital road map can improve the vehicle location when GPS fails. In this work, we focus on these GPS outages where the main problem is to combine another available measurements intelligently. We present here a global estimation method to solve the fusion problem of the GPS, odometer and digital road map measurements. It relies on an unscented Kalman filter whose main benefit is to deal with non-linear equations directly. In the case of an urban transport scenario, the comparison with an extended Kalman filter shows that the vehicle positioning is improved without requiring more computing time.