Updating of digital road map

The Japan Digital Road Map Association (JDRMA) was founded to establish the standards for digital road map (DRM) and to produce a DRM database in Japan. The JDRMA is now in the process of refining the database using 1/25,000 scale topographical maps. The JDRMA supplies users with an updated version of the database every year. Car navigation is currently the largest application of DRM. Police departments, fire departments, ambulance services, and taxi companies use location systems that use DRM. Significant application of DRM is expected to come in the future in the planning and research fields. Road sign checking system, traffic accident analysis system, road census reference system and logistics system are now being studied. As the applications expand, more advanced DRMs must be made available, To meet this requirement, development of more detailed and precise DRMs and linkage to dynamic information systems is being pursued.

Location based services (LBS) are capturing global spotlights increasingly, in which users tend to pick up navigation service as their favorites. This paper examines the dynamic routing problems based on real-time traffic information in LBS. At first, the overall structure of dynamic navigation system is illustrated, which is composed of four ingredients: traffic data collection, digital road map, static routing and dynamic re-routing. Traffic data collection is the task of traffic information center (TIC), where various sources of data are fused into link travel times that are used as criteria to choose candidates in path determining. Digital road map includes digital road network and historical traffic data, where the topology relationship of road network is elementarily explored and the method to represent statistical traffic data is referred to. Routing with historical traffic data and dynamic re-routing with live traffic data are two main functions of dynamic navigation service. The flow of dynamic re-routing algorithm is exhibited. Lastly, a first-in-first-out (FIFO) time-depend shortest path algorithm is discussed for dynamic routing, which adopts the adaptive A^* algorithm based on binary heap priority queue and RB-tree.

ObjectiveTo assess progress in meeting young people's sex education needs in Britain by examining the current situation and changes over the past 20 years in sources of information about sexual...

To provide safe and secure road space is a very important consideration for road administrators, and road evenness has a great impact on driving safety and riding comfort for road users. Furthermore, frequent and quantitative monitoring of the road evenness is a critical issue to establish PMS (Pavement Management System). In order to promote the PMS in local area, we investigate the evenness in local cities, using a new type of compact profiler and present evenness conditions combining different pieces of road information. In order to conduct the study, we used the Digital Road Map (DRM) and ESRI ArcGIS. Because, the DRM electronic road map has been built for road management and vehicle navigation systems for the purpose of linking road network and geographical information. Different road roughness conditions are shown in IRI, each road category and land use characteristics.

The paper focuses on the development of GDF as a standard for digital road maps. GDF is a geographical data format in which the data are defined in three different levels: level O contains the geometry and the topology of the information; level 1 defines real world objects; and on level 2, functionally related level 1 features are grouped into higher order objects. The creation of the European digital road map, which was made possible by the standard and on the expected growth of mass market applications such as car information systems, is also discussed.

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.

This paper introduces the its-car navigation system prototype we developed 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 center through communication data link for the purpose of fleet management.

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.

Currently available driver assistance systems (i) warn the driver based on vehicle state sensors (e.g., door open, outside temperature near or below the freezing point), (ii) offer route guidance information (navigation systems based on GPS and digital road maps), or—in some critical situations—(iii) even actively influence vehicle handling under carefully delimited conditions (anti-blocking-system, electronic-stability-program). This contribution reports about investigations to combine passive GPS- and map-based route guidance with model-based machine vision in order to automatically assess or even execute driving maneuvers in inner-city traffic situations. Information provided by todays route guidance systems is treated as a generic description of lane structure. The schematic description of lane structures extractable from commercially available standard digital maps is automatically instantiated by a machine vision approach which interprets video image sequences recorded by cameras from within a driving vehicle. The resulting model of the lane structure in front of the vehicle is subsequently exploited in order to control vehicle maneuvers in real-time as a proof of principal system competences. The machine-vision-based execution of driving maneuvers can at any time be overridden by the driver.

125 years after Bertha Benz completed the first overland journey in automotive history, the Mercedes Benz S-Class S 500 INTELLIGENT DRIVE followed the same route from Mannheim to Pforzheim, Germany, in fully autonomous manner. The autonomous vehicle was equipped with close-to-production sensor hardware and relied solely on vision and radar sensors in combination with accurate digital maps to obtain a comprehensive understanding of complex traffic situations. The historic Bertha Benz Memorial Route is particularly challenging for autonomous driving. The course taken by the autonomous vehicle had a length of 103 km and covered rural roads, 23 small villages and major cities (e.g. downtown Mannheim and Heidelberg). The route posed a large variety of difficult traffic scenarios including intersections with and without traffic lights, roundabouts, and narrow passages with oncoming traffic. This paper gives an overview of the autonomous vehicle and presents details on vision and radar-based perception, digital road maps and video-based self-localization, as well as motion planning in complex urban scenarios.

A major challenge in the production and use of geographic information is assessment and control of the quality of the spatial data. The rapid growing number of sources of geospatial data, ranging from high-resolution satellite and airborne sensors, GPS, and derivative geospatial products, pose severe problems for integrating data. Content providers face the problem of continuously ensuring that the information they produce is reliable, accurate and up-to-date. Integrity constraints are able to resolve certain issues in the data, like valid attribute values or relationships between data objects. The main issue is however the consistency of the data with respect to the current “real-world” situation. Today the industry still relies on human operators, who collect and interpret aerial photos and field data to check and correct the current state of the data. Especially for detailed data over large regions, like digital road maps or topographic maps, this is a very labor-intensive and costly process. In addition, human processing is a source of error and inconsistency. Automated detection of change and anomalies in the existing databases using image information can form an essential tool to support quality control and maintenance of spatial information.

This paper explores the different functional roles of digital road map databases in an overall Intelligent Vehicle Highway Systems (IVHS) environment, using examples from project Pathfinder, TravTek, and other IVHS configurations. Particular emphasis is placed on information requirements necessary to integrate these subsystems into a mature IVHS, supporting multiple product offerings.

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.

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.

Genetic algorithms are proposed as a new learning paradigm for combinatorial optimization that models a natural evolution mechanism. The authors attempt to apply genetic algorithms to the vehicle routing problem. As it is easy to generate the same gene while a generation shift goes on, it is feared that a solution will fall into a local minimum. The authors propose a new method that does not permit overlapping of genes. Some experiments are performed on digital road maps. The authors' results show that the genetic algorithms can effectively find optimum solutions.< <ETX xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">&gt;</ETX>