This paper attempts to identify which side of the road a bicycle is currently riding on using a common camera for realizing an advanced bicycle navigation system and bicycle riding safety support system. To identify the roadway area, the proposed method performs semantic segmentation on a front camera image captured by a bicycle drive recorder or smartphone. If the roadway area extends from the center of the image to the right, the bicyclist is riding on the left side of the roadway (i.e., the correct riding position in Japan). In contrast, if the roadway area extends to the left, the bicyclist is on the right side of the roadway (i.e., the incorrect riding position in Japan). We evaluated the accuracy of the proposed method on various road widths with different traffic volumes using video captured by riding bicycles in Tsuruoka City, Yamagata Prefecture, and Saitama City, Saitama Prefecture, Japan. High accuracy (>80%) was achieved for any combination of the segmentation model, riding side identification method, and experimental conditions. Given these results, we believe that we have realized an effective image segmentation-based method to identify which side of the roadway a bicycle riding is on.

This paper presents two new types of markers of M-CubITS (M-sequence Multimodal Markers for ITS; M-Cubed for ITS) that is a ground-based positioning system, in order to advance the WYSIWYAS (What You See Is What You Are Suggested) navigation environments providing intuitive guidance. One of the new markers uses warning blocks of textured paving blocks that are often at important points as for pedestrian navigation, for example, the top and bottom of stairs, branch points, and so on. The other uses interlocking blocks that are often at wide spaces, e.g., pavements of plazas, parks, sidewalks and so on. Furthermore, we construct the integrated pedestrian navigation system equipped with the automatic marker-type identification function of the three types of markers (the warning blocks, the interlocking blocks, and the conventional marker using guidance blocks of textured paving blocks) in order to enhance the spatial availability of the whole M-CubITS and the navigation system. Consequently, we show the possibility to advance the WYSIWYAS navigation environments through the performance evaluation and the operation confirmation of the integrated system.

The Earth's ionosphere can hinder radio propagation with two serious problems: group delay and phase advance. Ionospheric irregularities are significantly troublesome since they make the amplitude and phase of the radio signals fluctuate rapidly, which is known as ionospheric scintillation. Severe ionospheric scintillation could cause loss of phase lock, which would degrade the positioning accuracy and affect the performance of navigation systems. Based on the phase screen model, this paper presents a novel power spectrum model of phase scintillation and a model of amplitude scintillation. Preliminary results show that, when scintillation intensity increases, the random phase and amplitude fluctuations become stronger, coinciding with the observations. Simulations of the scintillation effects on the acquisition of Beidou signals predict acquisition probability. In addition, acquisition probabilities of GPS and Beidou signals under different scintillation intensities are presented. And by the same SNR the acquisition probability decreases when the scintillation intensity increases. The simulation result shows that scintillation could cause the loss of the acquisition performance of Beidou navigation system. According to the comparison of Beidou and GPS simulations, the code length and code rate of satellite signals have an effect on the acquisition performance of navigation system.

Pedestrian dead reckoning (PDR) based on human gait locomotion is a promising solution for indoor location services, which independently determine the relative position of the user using multiple sensors. Most existing PDR methods assume that all sensors are mounted in a fixed position on the user's body while walking. However, it is inconvenient for a user to mount his/her mobile phone or additional sensor modules in a specific position on his/her body such as the torso. In this paper, we propose a new PDR method and a prototype system suitable for indoor navigation systems on a mobile phone. Our method determines the user's relative position even if the sensors' orientation relative to the user is not given and changes from moment to moment. Therefore, the user does not have to mount the mobile phone containing sensors on the body and can carry it in a natural way while walking, e.g., while swinging the arms. Detailed algorithms, implementation and experimental evaluation results are presented.

This letter presents a novel approach for traffic light detection in a video frame captured by an in-vehicle camera. The algorithm consists of rotated principal component analysis (RPCA), modified amplitude thresholding with respect to the histograms of the PC planes and final filtering with a neural network. The proposed algorithm achieves an average detection rate of 96% and is very robust to variations in the image quality.

Today, pedestrian navigation systems for mobile phones use 2-dimensional maps as a navigation media in general. But 3-dimensional maps or scenery images are easier to understand for users than 2-dimensional maps. To use 3-dimensional maps or scenery guidance, the measuring accuracy of user position is essential to understand guidance images. In this paper, we will present a pedestrian navigation system using real scenery photographs as a navigation media, and based on experiments we will report the evaluation result of influence of positioning accuracy on the understandability of navigation. It is shown that 3 meters or less error of positioning is tolerable for pedestrian navigation systems using scenery images.

Functional magnetic resonance imaging (fMRI) allows to display functional activities of certain brain areas. In combination with a three dimensional anatomical dataset, acquired with a standard magnetic resonance (MR) scanner, it can be used to identify eloquent brain areas, resulting in so-called functional neuronavigation, supporting the neurosurgeon while planning and performing the operation. But during the operation brain shift leads to an increasing inaccuracy of the navigation system. Intraoperative MR imaging is used to update the neuronavigation system with a new anatomical dataset. To preserve the advantages of functional neuronavigation, it is necessary to save the functional information. Since fMRI cannot be repeated intraoperatively with the unconscious patient easily we tried to solve this problem by means of image processing and pattern recognition algorithms. In this paper we present an automatic approach for transfering preoperative markers into an intraoperative 3-D dataset. In the first step the brains are segmented in both image sets which are then registered and aligned. Next, corresponding points are determined. These points are then used to determine the position of the markers by estimating the local influence of brain shift.

Initial leveling of strapdown inertial navigation system is a prerequisite work for distinguishing between gravity and acceleration effects in the accelerometer sensing's. This study presents an on-line methodology to resolve the initial leveling problem of a vehicle, which is subject to a large, long duration, and abrupt disturbance input with a deterministic nature under noisy circumstances. The developed method herein is the Kalman filter based scheme with a robust input estimator, generalized M estimator, and a testing criterion. The generalized M estimator identifies the unexpected disturbance inputs in real time. In addition, hypothetical testing based on the least-squares estimator is devised to detect the input's onset and presence. A required regression equation between the observed value of the residual sequence with an unknown input and theoretical residual sequence of the Kalman filter with no input is formulated. Input estimation and detection are then provided on the basis of the derived regression equation. Moreover, Monte Carlo simulations are performed to assess the superior capabilities of the proposed method in term of rapid responses, accuracy, and robustness. The efficient initial leveling can facilitate the entire alignment of the inertial system.

This letter proposes a map-matching method for automotive navigation systems. The proposed method utilizes the innovation of the Kalman filter algorithm and can achieve more accurate positioning than the correlation method which is generally used for the navigation systems. In this letter, the performance of the proposed algorithm is verified by some simulations.

It was more than 10 years ago that the first map navigation system, as an example of invehicle information system, has appeared in the market in Japan. Today's navigation system has been improved to the level that the latest system has 10 micro-processors, 7 MBytes of memories, and 4 GBytes of external data storage for map database. From the viewpoint of the automobile driver, there are still some problems with the system. Major problems in general are a lack of traffic information, better human interface, and a need for cost-reduction. The introduction of application specific ICs (ASICs) is expected to make systems smaller, costless, and give higher speed response. Today's in-vehicle information systems are reviewed function by function to discover what functions need to be implemented into ASICs for future systems, what ASICs will be required, and what technology has to be developed. It is concluded that more integration technology is expected including high parformance CPUs, large capacity memories, interface circuits, and some analog circuits such as DA converter. To develop this technology, some, major problems such as power consumption, number of input/output signals, as well as design aid and process technology are pointed out.

A new concept for a positioning satellite system based on a new satellite constellation has been studied. The system needs a minimum of four satellites injected into quasi-geostationary orbit (QGEO) with high inclination. Due to the QGEO characteristic, the satellites are orbiting within continuous visibility range of ground control stations (GCS), from which the satellite time is controlled through the link connections of the feeder and the intersatellite communication (ISC). Consideration is made for the required high accuracy and quality checks against malfunction, wherever the satellites may be positioned. The orbit data processing function, another major function, is performed independently of the time control. The case of global coverage attained by twelve satellites has been studied in this paper. When a constellation of satellites for a global navigation satellite system (GNSS) is designed, conditions to obtain a good geometric dilution of precision (GDOP) at all places and times should be considered. Therefore, the satellites will be spread out in wide directions and are in an asymmetrical arrangement when seen by an observer are considered when setting the parameters of the ephemerides of the constellation. Under the restraints of the designed constellation, the GDOP value distribution for a third of the world map with area time parameters is computed and summarized in histograms for the system evaluation.