Digital Road Maps and Path Optimization Applied to Vehicle Navigation Systems

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.

Joint Charging and Routing Optimization for Electric Vehicle Navigation Systems

Car navigation systems (CNS) have become an interesting research and industrial topic due to their wide deployment. However, updating and distribution of the road map database is a complex and high cost process. In this paper, we propose a real-time map generation and updating algorithm, which fulfills or corrects the unknown road area of the conventional road map database by using global positioning system (GPS) information and wireless communication technologies. To generate the road map of unknown roads, the proposed algorithm uses road images, which can be obtained when a driver travels unknown roads. When a driver travels unknown roads, which do not appear on the map of the car navigation system, the car navigation system extracts GPS and image information of the unknown roads. Then, the car navigation system generates the road map for the unknown roads by extracting location and road information, and updates the road map database in the car. After updating the local road map database, the car navigation system updates the master road map database, which is located at the network and is distributed to other car navigation systems. Thus, the road map database provider can update the road map database of other car navigation systems, which share the same master road map database. The proposed algorithm provides an efficient updating process of the road map database with a simplified process and reduced time for unknown road areas.

Summary: The driver sometimes is not satisfied by the route chosen by the Car Navigation System. Therefore, the function and the concept of the current Car Navigation System were examined. It can be said that the driver's subjectivity can be changed based on objective conditions. There are three main results. 1. Derive the objective element, factoring in predicted conditions. 2. Based on a conditional node, make predictions which will make a judgment about the subjective element. 3. Example all possible route and at the conditional node make a judgment about the subjective element based on the weight of all the conditions. The background of the research was obtained from a practical example of decision-making problems. It is a system rapidly spreading throughout Europe, America and Japan. The system can be described a functional part of the car, guiding the driver to the desired destination by the displaying road information. This is called a Car Navigation System in Japan. When this is used, the drivers depend on the map information shown on the display. Based on the input given by the driver, the Car Navigation System shows the most efficient route based on time and distance. Generally, drivers do not drive the most direct route to the destination point. Usually when driving, even if a person preplans the route, he will have a desire to make impulsive stops along the way. Depending on the driver, he might even stop for brief rest to enjoy the view or spend some time on a leisure activity. These types of stops enhances the driving pleasure and are determined subjectively based on the individual driver. The driver can input desired leisure spots into the Car Navigation System. This input causes the Car Navigation System to plan desired stops along the final destination point. However after driving, the driver sometimes is not satisfied by the route chosen by the Car Navigation System. The dissatisfaction is caused by the system's inability to anticipate the driver's immediate need. For example, based on the driver's pretend stop points, the system may direct the driver to a tennis court, but by the time of arrival, it could be raining or the road driven may be congested, slowly the drive. In these cases, the drive to the courts would have been time wasted. The driver's desired stop point is subjective input while the change in weather change or traffic patterns are objective elements. The destination decided is influenced by both of these factors. Even if these are many objective elements, they are transitional so events that may influence can be predicted. Therefore, the function and the concept of the current Car Navigation System were examined. Although the system's function can guide the driver to the desired destination points, but it cannot forecast changes in driving conditions. In the future, there is a prospect for a new function of Car Navigation System based on model designed using an AHP.

Localization of a vehicle is a key component for driving assistance or autonomous navigation. In this work, we propose a visual positioning system (VPS) for vehicle or mobile robot navigation. Different from general landmark-based or model-based approaches, which rely on some predefined known landmarks or a priori information about the environment, no assumptions on the prior knowledge of the scene are made. A stereo-based vision system is built for both extracting feature correspondences and recovering 3-D information of the scene from image sequences. Relative positions of the camera motion are then estimated by registering the 3-D feature points from two consecutive image frames. Localization of the vehicle is finally given by the reference to its initial position. I. INTRODUCTION Localization and tracking of a vehicle are major compo- nents for providing positions, directions and travel informa- tion to the driver. They also serve as key technologies for building autonomous navigation systems. Many approaches have been proposed for locating a mobile robot or a ve- hicle based on various techniques. The most commonly used methods include dead-reckoning techniques, navigation using active beacons, landmark-based navigation, map-based navigation, global positioning system (GPS), and vision- based positioning. Dead-reckoning is a procedure for determining the present location of a vehicle by advancing some previous position through known path and velocity information over a given period of time. Since an odometer and optical sensors for wheel direction detection are easily installed on a vehicle, dead-reckoning is usually a less expensive method for vehicle localization. However, the integration of incremental motion information over time will lead to accumulation of errors. In the case of long distance travel, accumulation of orientation error will make position errors diverge as the increase of driving time. Active beacons such as laser, sonar or radio can be used as media for vehicle or mobile robot navigation. These approaches use triangulation to measure the distance between a number of beacons and the mobile platform and then deter- mine the current location. The problems associated with this technique are the inaccuracy of the distance measurement caused by time-delay of the signals, and the installation and maintenance cost of a large number of beacons required for an area. Satellite based differential global positioning system (DGPS) is probably the most advanced and accurate method for identifying the position and orientation of an object. However, DGPS does not work well if the satellite signals are blocked. This situation commonly happens in the indoor environments, or urban areas with tall buildings, etc. In this work, we propose a visual positioning system (VPS) for vehicle or mobile robot navigation. Different from general landmark-based or model-based approaches which rely on some predefined known landmarks or ap rioriinfor- mation about the environment (e.g., 3-D models of buildings, objects, etc.), no assumptions on the prior knowledge of the scene are made. A stereo-based vision system is built for both extracting feature correspondences and recovering 3- D information of the scene from image sequences. A robust feature tracking method based on simultaneously considering the inter-frame images from the same camera and the stereo image pair from different cameras at a fixed time instant is developed. The relative position of the camera motion is then estimated by registering the 3-D feature points from two consecutive image frames. Our system includes the following basic modules:

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 car navigation service has opened through the global positioning system (GPS). People's lives have changed through car navigation services, and this service is indispensable nowadays. GPS provides reliable location accuracy in open space, but its location accuracy is degraded where multipath and other error factors are present. In particular, since GPS signals must be received in order to calculate the position, the navigation system cannot calculate the position in an indoor space such as underground parking lot, subway or tunnel where GPS signals are cut off. When car navigation is performed in an indoor parking lot where GPS signals are not transmitted, the user cannot calculate the current location, so the user has to go out by relying on the traffic sign because he does not know the current location. Also, when the user leave the parking lot, the car navigation system has problems. In an urban canyon, it is difficult to calculate accurate current position due to multipath. Because the route is calculated through the current location and route, the car navigation system provides the wrong route to the user. In addition, when entering a tunnel while using car navigation, the car navigation system calculates the position using the initial tunnel entry velocity estimated by the GPS. As the speed of the user increases or decreases, the position error also increases. Then the position suddenly changes when passing through the tunnel. And it is difficult to provide a proper guidance at the branch road or exit in the tunnel because of the positioning error. People are demanding a complete car navigation service, but the current navigation service provides the wrong route and position information to the user where the GPS signal quality is poor. In order to solve the above problem, in this paper, we have studied to improve the user's position accuracy by using LTE signal and mobile phone internal sensor. In general, observed time difference of arrival (OTDOA) technique is applied for the LTE based positioning. Because this method gives tens of meters of error, it is difficult to be used for the car navigation service. In our research, we estimate the position using the LTE signal strength. The difference from conventional fingerprinting method is that instead of using only current signal strength, the signal strength pattern accumulated while moving is utilized to estimate the user position. The signal strength increases as the user approach the access point (AP), and decreases as the user move away from the AP. Therefore, when a user moves and stores signal strength with respect to a specific AP, a pattern that could occur only in the corresponding region is saved. There are many similar candidates in the database when using only the current signal strength. However, when using a pattern, similar candidates exist only around the true position. And, when using several LTE signals, the positioning accuracy could be increased. Since the user receives multiple LTE signals, the pattern becomes surface. The surface is compared with the database, and the position with the highest correlation value is estimated as the current position. We call this method surface correlation (SC). In order to verify the performance of the proposed method, experiments were conducted in the Gangnam beltway tunnel in Seoul. The testbed consisted of three tunnel and total distance is almost 11.8 Km. In the tunnel, repeater and several antennas are installed to widen the area of the LTE signal. When an LTE signal is received by the vehicle, the largest signal strength is received every time when the vehicle passes the nearest antenna. As a result, for a specific LTE signal, when the vehicle has passed through the LTE signal region, a unique pattern with multiple peaks is generated. In order to estimate the position, we first estimate the position with the SC technique. Then, the estimated position is corrected by comparing the peak of the currently received pattern with the peak of the database. Through this method, the position was estimated with an error of less than 50meter in the tunnel. Conclusion In this research, the LTE signal strength based positioning technology is presented. Especially, this technology, SC, provides the positioning performance that could be used for the car navigation in the tunnel where GPS signal is blocked. We expected that user would be use the accurate car navigation service through this technology in the GPS challenging environment.

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. >

Accurate localization in land vehicle navigation systems is highly dependent on the global navigation satellite system (GNSS). However, GNSS signal outages are common in urban areas due to obstacles such as tall buildings and tunnels. To mitigate these issues, digital road maps and dead reckoning sensors, like odometers, are often integrated to provide continuous vehicle localization. This paper presents a robust estimation method to solve the fusion problem of GNSS, odometer, and digital road map measurements in the presence of GNSS out- ages. The proposed solution utilizes a marginalized particle filter (MPF), which combines the robustness of particle filtering with the efficiency of a Kalman filter to handle the linear and non-linear parts of the state and/or measurement equations, respectively. When GNSS signals are unavailable, the MPF fuses all available pseudo-range data with odometric and map information to enhance vehicle positioning. The effectiveness of the proposed method is demonstrated using real-world data in an urban transportation scenario, highlighting signifi- cant performance improvements and real-time application potential.

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.

Emerging automotive engineering solutions extensively rely on navigation tools. Shortest path evaluation is core competency of optimal path planning, one of the most critical component of any navigation application. Fibonacci heap variant of state-of-the-art Dijkstra’s algorithm solve single source shortest path problems in asymptotic O(E + V log V) worst-case time. In spite Fibonacci heap is theoretically dominant in time efficiency, it might not work much better empirically with real-world data, because although large and intricate, the real-world road network graphs are sparse and the graph topology greatly affects the performance of the algorithms. SGO algorithm, a more efficient approch to address single source shortest path problem, is applied to solve optimal path planning problem in automotive navigation applications. Each road segment of the road graph leads to a particular location thus will hold unique distance values all the time. The SGO calculates the total distance from start location to the end of the each road segment and evaluates shortest path based on these calculations. The SGO algorithm and the Fibonacci and Binary heap variants of Dijkstra’s algorithm were tested on real-world intercity and intra-city road network graphs to evaluate running time efficiency of the algorithms in automotive navigation systems. The experimental results show that the SGO algorithm outperformed both the variants. The empirical superiority of the SGO algorithm suggests its usefulness in automotive navigation systems.

현재 육상에서의 GPS는 자동차 항법체계(Car Navigation System)와 개인휴대용 단말기를 이용한 위치정보체계 등이 활발히 활용되고 계속 연구되고 있는 추세에 있지만 해양에서는 그렇지 못한 실정으로 원래 GPS는 선박의 항해용 목적으로 개발된 것이지만, 해상에서 선박의 운항에 정확도는 크게 어려움을 주지 않는다는 이유로 선박의 항로확인을 위한 근사적인 위치의 표현만을 수행해왔다. 최근 항만이 복잡해지고 선박의 증가로 해양에서도 선박의 정밀한 유도가 필요하게 되었다. 본 연구에서는 GPS에서 사용하고 있는 타원체인 WGS84 타원체의 자리좌표를 우리나라에서 사용하고 있는 타원체인 BESSEL 타원체의 지리좌표로 변환하는 요소인 3-parameter에 의한 타원체변환알고리즘 및 육도와 해도에서 사용하고 있는 평면 직교좌표계인 TM투영과 UTM투영의 알고리즘제작 하였다. 또한, 현재 항법용 GPS 센서가 가지고 있는 정확도를 검증하기 위해 GPS의 정적측량, 동적측량에 의한 오차량을 비교검토하였으며, 선박의 항로추적을 위해서 선박에서 수신된 위치 정보의 편차량을 분석하였다. 이렇게 검토된 자료를 기본으로 하여 실시간 선박항로를 추적할 수 있는 저가의 항로추적시스템 개발을 본 연구의 최종목표로 연구를 수행하였다. The most great GPS is working on CNS(Car navigation System) and Personal terminal, but ocean has not been. First studied GPS is sailing to ship but We have been connivance that The ship is not need detail navigation. Lately, harbor facilities and transportation service in harbors are complication. We have need accurate ship navigation system. In this paper, author developed algorithms of ellipsoid conversion between WGS84 and BESSEL and develope algorithms of map projection between ellipsoid coordinate system, and plane cartesian system. Author used 3-parameter in covering ellipsoids and used TM and UTM projection in converting between ellipsoid and plane cartesian coordinate. And author analyzed errors through static surveying and dynamic surveying of GPS for proving accuracy of GPS sensor, Furthermore author analyzed deflection error of received position. Finally author developed real time ship navigation system using cheep GPS sensor.

Aiming at solving the navigation and obstacle avoidance of the unmanned vehicle,the multi sensor data fusion technology and unmanned vehicle obstacle avoidance navigation algorithm were studied profoundly. According to the requirements of the application of unmanned vehicle navigation and obstacle avoidance system, multisensor data fusion technology was applied to unmanned vehicle navigation and obstacle avoidance control system. In addition, A*VFF navigation and obstacle avoidance algorithm based on fuzzy neural network was improved. Finally, through the construction of the simulation platform, simulation experiment of the unmanned vehicle obstacle avoidance navigation was completed, and a better route was planned for unmanned vehicle in a more complex environment. The results showed that it realized the autonomous navigation of unmanned vehicle and obstacle avoidance function. Based on the above findings, it is concluded that the application of artificial intelligence detection system has good performance.

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.

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.