A wavelet‐based location referencing strategy for digital maps in navigation systems

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.

In today's digital era, map and navigation applications have become essential tools for users to find locations, obtain the fastest routes, and explore new places. Google Maps is one of the most popular and comprehensive mapping applications that offers various features, such as navigation. However, despite being highly beneficial, Google Maps has several limitations, particularly regarding map markers, personalized, creating individual routes, and offline map usage on navigation. To address these issues, this research develops an innovative application called StellarPath. StellarPath is a mobile-based digital map location marker application that adopts Location Based Service (LBS) methods for more effective navigation. This application focuses on offline usability as a digital map marker and includes features such as offline maps stored indefinitely, offline navigation, manual route creation, and higher personalization, allowing users to save and manage markers with additional information offline. Test results show a success rate of 92.5%, indicating the application's effectiveness. The conclusion drawn is that the application can download and store maps offline, although it has shortcomings in managing downloaded maps. The offline navigation feature allows users to draw, save, and manage manual routes on the map. Users can create and save location markers with additional information offline. Navigation is flexible, with the option to start navigation by tapping on the map and features that can be enabled or disabled. This research aims to provide a more flexible and personalized navigation solution compared to existing standard map and navigation applications, such as Google Maps.

The main tasks of car navigation systems are positioning, routing, and guidance. This paper describes a novel, two-step approach to vehicle positioning founded on the appropriate combination of the in-car sensors, GPS signals, and a digital map. The first step is based on the application of a Kalman filter, which optimally updates the model of car movement based on the in-car odometer and gyroscope measurements, and the GPS signal. The second step further improves the position estimate by dynamically comparing the continuous vehicle trajectory obtained in the first step with the candidate trajectories on a digital map. This is in contrast with standard applications of the digital map where the current position estimate is simply projected on the digital map at every sampling instant.

<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>

<div class="htmlview paragraph">Video surveys are being used in Doncaster to provide an up-to-date inventory of the highway network and street furniture. The techniques being used are closely related to those under development for the collection of network attribute data for Advanced Transport Telematics (ATT) / Intelligent Vehicle Highway Systems (IVHS) systems, in particular for route guidance and navigation systems.</div> <div class="htmlview paragraph">The success of systems such as Autoguide, EURO-SCOUT (formerly called ALI-SCOUT), CARIN, Travelpilot and dual-mode route guidance will be critically dependent upon the quality of the data on which navigation and routeing recommendations are based. Experience, in London and Berlin in particular, has shown that the information presented to the driver throughout his journey needs to be accurate, precisely timed and in a large number of cases specifically tailored to the details of individual junction layouts. It is not possible to provide the quality of data necessary for system acceptance without detailed surveys of the roads on which guidance is to be given.</div> <div class="htmlview paragraph">Video survey techniques offer the flexibility to view road and junction layouts in a controlled office environment, minimising the need for costly re-surveying, and significantly improving data quality and consistency. The system in use in Boncaster is providing the basis of an innovative methodology in which in-vehicle computers are interfaced with navigation systems and professional video techniques.</div> <div class="htmlview paragraph">This results in the integration of the digital map base with the information required to generate the vitally important turning recommendations for the driver and the accurate data necessary for successful map-matching.</div> <div class="htmlview paragraph">The paper presents results and conclusions from Doncaster. It reviews the need for this type of accurate data, describes experiences in existing trials of ATT/IVHS systems, analyses developments in digital map creation techniques and presents the context into which the new generation of data collection methodology will be integrated.</div>

Estimating the map-matched position of a vehicle on a digital map is a key technology for modern navigation and advanced driver assistance systems (ADAS). Inaccurate sensor measurements and a simplified road network representation cause uncertainties in the map-matched position. This paper describes a particle filter method for finding a robust and stable solution for the map-matching problem at the lane-level in ambiguous situations. The proposed approach fuses low-cost sensor data including camera detections of the lane markings with commercial map data. A new spatio-temporal filtering algorithm is introduced for estimating the hypotheses for the map-matched positions together with a confidence measure from the set of weighted particles. The results of tests at road bifurcations indicate that this method produces stable decisions for the correct road segment. It was found that the integration of lane based features improves the performance of the map-matcher at the road level.

Terrain aided navigation is a well approved navigation technique. It is important part of modern navigation system especially in military avionics. The basic principle, composing and technologic application of terrain aided navigation system is introduced. In brief, several pivotal techniques of terrain aided navigation system are discussed, which include position estimate, digital maps, real elevation data acquisition, storage technology, integrated navigation and digital correlation equipment, then describes and analyzes some appropriate resolving scheme and examples.

It is argued that the consumers of business, government, and corporate systems are assuming a tremendous cost overhead as a result of failure to develop cooperative data sharing systems in the field of vehicle navigation and digital mapping. Background experience of the growth and emergence of outstanding technological advancements is highlighted. The author calls upon government, business, and technological leaders to formulate an initiative toward resolving the data acquisition problems. The focus of this initiative must recognize existing addressing system inputs in use by worldwide businesses, governments and private citizens. >

In this paper we propose a full-view car navigator system which registers full-view spherical images with a digital route map via the GPS (global positioning system) information. A full-view image is captured by a spherical image sensor; the vibration of spherical camera is compensated by the information from an inertial sensor; the rectified spherical image is finally registered to digital route map via GPS information. Since an image sequence is directly registered no 3D model building is required. Since a full-view image is used the urban scene with high buildings can be wholly recorded and there are no limits on view direction. It results in a friendly human interface in our car navigator system.

Research and/or Engineering Questions/Objective The vehicle of the future will support its driver by advising him regarding potential hazards. Essential prerequisite therefore is the sensor based perception of the traffic situation. For the recognition of traffic related objects, camera based sensors, deepness cameras, vehicle sensors as well as radar and lidar sensors are used. For the future development of ADAS the fusion of multiple sensor data to a consistent environmental picture will play a key role. The evaluation approach of real world driving tests will no longer be sufficient due to the complexity of the system interactions. New simulation methods are needed to evaluate ADAS by using virtual test driving with realistic vehicle behavior and complex traffic environment. Methodology Therefore it is important to integrate camera based components in a “closed loop”-simulation platform to be able to test sensor data fusion technologies under realistic conditions. To test new driver assistance systems in a simulation environment today animation data is filmed, subsequently this data is used to test an image processing algorithm or a fusion algorithm. But this method cannot be applied if wide-angle cameras such as cameras with fisheye lenses will be used. Within a research frame work for autonomous driving functions a new simulation technology was developed to integrate virtual cameras beside the well know environment sensor in the vehicle dynamic simulation CarMaker. For this purpose the real-time animation was extended with a sophisticated virtual camera model so called “VideoDataStream” to generate simultaneous video data (also PMD for 3D images). The camera positions as well as the camera properties could be applied individually. Additionally it is possible to freely define the type of the camera lens (e.g. fisheye) with lens settings like opening angle and the typical lens failures (e.g. distortion and vignetting). With this new technology it is possible that e.g. camera and radar data can be provided time and place synchronal for the fusion algorithm which should be tested! Results The video data could be used for evaluating image processing and sensor data fusion in Model-/Software-/Hardware-in-the-Loop applications within virtual test driving conditions. Here the created method and examples of image based perception of the vehicle environment as well as sensor data fusion algorithms shall be presented. Among others this covers first of all the recognition of traffic lanes, traffic signs and other traffic partners as well as the fusion of the single information up to a comprehensive environment picture. A further field of application will be the conjunction with navigation systems and digital maps, by which the virtual vehicle supports the navigation system with related GPS position and gets back the “MPP—Most Probable Path” with the “electronic horizon”, which is a type of predictive sensor, with all related preview information in front of the vehicle which are defined in the ADASIS protocol. Conclusion By using the introduced method the capability and efficiency of function development and testing in the area of Advanced Driver Assistant Systems will significantly be improved. Due to a powerful simulation environment a broad range of validation tests can be shifted into simulation because also complex test scenarios can be replicated and the tests are reproducible. The simulation data can be provided time and place synchronal, which is absolutely important, e.g. for a fusion algorithm which should be tested.

An informative digital map is a prerequisite for future Intelligent Transportation System (ITS) applications such as lane level navigation systems. Preparing and updating such detailed digital maps by using existing methods such as surveying and image digitization is not practical due to the time and cost involved. We propose a method that statistically mines Global Positioning System (GPS) trajectory data obtained from vehicles to generate a lane level digital map of the road. The proposed method is capable of generating a digital map of the road which contains lane centerlines. The proposed method is independent of the lane width, lane parallelism and can handle lane splits and merges. In addition, a method that can be used to generate a map of lane boundaries is also presented. The proposed methods have been tested using the GPS data collected using vehicles equipped with GPS enabled mobile phones. Results show that the proposed method for lane centerline generation is successful in different road geometries.

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.

Up-to-date map data is a must for current and future navigation and advanced driver assistance system (ADAS) applications. Today, digital maps are normally stored on DVDs or hard disks, with periodic updates only available on replacement disks. However, new mechanisms for updating maps have been investigated and some of them already reached the market. As the real world is changing every day, detecting changes to the road network quickly and at a low cost is a challenge. Although mapmakers continuously survey the European road network for changes, map information is not always up-to-date or accurate. This paper presents the test and validation results and two example applications from the FeedMAP project and how they can be used for increasing driving safety by integrating map deviation detection and incremental update technology into ADAS applications using the ADAS Horizon concept.

For connected and autonomous electric vehicles (CAEVs), drivers must choose a power-efficient route because the energy source of a CAEV is a battery. The driver can find the power-efficient route and reduce the risk of the battery running out if the CAEV navigation system can accurately predict the battery’s state of charge (SOC) based on the power consumption prediction for the selected route. As the driver’s driving pattern and the vehicle’s battery capacity are different for individual vehicles, it is important to predict the power consumption and battery SOC for the selected route. In this paper, the system model of CAEV to collect on-board diagnostics (OBD), OBD data cloud (ODC) to manage the OBD data, and the sequence of message exchange between them is proposed. Further, we propose an algorithm that can learn power consumption based on machine learning (ML) in the ODC. It predicts the battery SOC at the end of a trip using the current SOC when the onboard navigation system requests prediction for a specific route of the CAEV. The algorithm’s performance has been evaluated by the OBD dataset of personal vehicles collected in Michigan based on three road types: downtown, urban road, and highway. The results confirmed that the proposed MLR+DNN model could predict battery consumption with an average accuracy of 92%. Additionally, an average accuracy of 94% was obtained in selecting the most similar personal driving cycle (PDC) using hierarchical grey relational analysis (GRA), which is about 15% higher than the accuracy obtained by using the dynamic time warping (DTW) method.