Vehicle Location and Navigation Systems

This paper described an intelligent route guidance system that implements rule-based reasoning according to integrated information in onboard devices. A knowledge base of rules is built, and a control method is designed to overcome deficiencies of embedded systems, such as small available storage and limited computing speed. A prototype system is developed and the experimental evaluation shows that it makes reasoning effective in onboard devices. This work will improve the intelligence level of route guidance in vehicle location and navigation systems.

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:

In this research, a passenger-centric passenger information system is proposed, which uses crowd sourcing and mobile phones so that passengers are not only information consumers but are also providers of information to the system. Passengers can allow the system to track their location through their smart phones when they are traveling on public transportation; this will compensate for the lack of a vehicle tracking system in public transportation, particularly in nonurban areas. Map-matching (MM) algorithms integrate location data (i.e.,latitude and longitude) obtained from positioning sensors (in this case, mobile GPS) with a digital geographic information system (GIS) road map. Any map-matching algorithm first identifies the road link on which a vehicle is traveling and then determines the vehicle's location on that road segment. In the proposed information system, at a given point of time, a number of vehicle locations (latitude and longitude) are received from passengers traveling on a bus. In order to provide a precise vehicle location at a given point of time, a novel map-matching algorithm using fuzzy logic, which integrates multiple vehicle locations (obtained from passenger's smart phones) with a GIS road map, has been developed. The developed map-matching algorithm was tested using real-world data collected on four different bus routes in Aberdeenshire, Scotland, and also GPS data collected in and around Nottingham, United Kingdom. It was identified that the developed MM algorithm is efficient and capable of supporting the proposed passenger information system. (C) 2013 American Society of Civil Engineers.

The global positioning system (GPS) trajectory data are extensively utilized in various fields, such as driving behavior analysis, vehicle navigation systems, and traffic management. GPS sensors installed in numerous driving recorders and smartphones facilitate data collection on a large-scale in a high-frequency manner. Therefore, map-matching algorithms are indispensable to identify the GPS trajectories on a road network. Although the local map-matching algorithm reduces computation time, it lacks sufficient accuracy. Conversely, the global map-matching algorithm enhances matching accuracy; however, the computations are time consuming in the case of large-scale data. Therefore, this study proposes a method to improve the accuracy of the local map-matching algorithm without affecting its efficiency. The proposed method first executes the incremental map-matching algorithm. It then identifies the mismatching links in the results based on the connectivity of the links. Finally, the shortest path algorithm and the longest common subsequence are used to correct these error links. An elderly driver’s driving recorder data were used to conduct the experiment to compare the proposed method with four state-of-the-art map-matching algorithms in terms of accuracy and efficiency. The experimental results indicate that the proposed method can significantly increase the accuracy and efficiency of the map-matching process when considering high-frequency and large-scale data. Particularly, compared with the two-benchmark global map-matching algorithms, the proposed method can reduce the error rate of map-matching by nearly half, only consuming 18% and 58% of the computation time of the two global algorithms, respectively.

Joint Charging and Routing Optimization for Electric Vehicle Navigation Systems

This prototype system is being tested at the University. The positioning component consists of a GPS Trimble TANS (Trimble Advanced Navigation System) receiver, a pair of Nu-Metrics odometers, and a map-matching module, all integrated by a seven-parameter state vector Kalman filter to yield continuous positioning of the vehicle. An on-board GRiDCase EXP microcomputer is used for real-time computations and display. The system is being field-tested in a route network in the northwest quadrant of the City of Calgary. Enhanced area master files from Statistics Canada are used to produce the road network database.< <ETX xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">&gt;</ETX>

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.

In various applications of land vehicle navigation and automatic guidance systems, Global Navigation Satellite System/Inertial Measurement Unit (GNSS/IMU) positioning performance crucially depends on the attitude determination accuracy affected by gyro and accelerometer bias instabilities. Traditional bias estimation approaches based on the Kalman filter suffer from implementation complexity and require non-intuitive tuning procedures. In this paper we propose, as an alternative, a simple observer that estimates inertial sensor biases exclusively in terms of quantities with obvious geometrical meaning. By this, any multidimensional vector-matrix operations are avoided and observer tuning is substantially simplified. The observer has been successfully tested in a farming vehicle navigation system.

In recent years, navigation systems have been widely used in various amusement parks and sightseeing spots. Navigation systems, such as car navigation systems and navigation applications in smartphones, are also used in our daily life. By using a navigation system, users can not only find their location but also browse information on nearby attractions such as restaurants or department stores; furthermore, it can tell users the route to lead them to their destination. In these situations users will set their destinations by using the navigation system, but in tourism or for events, users have no idea about the current conditions. On the other hand, organizers will have a hard time trying to control the crowd flow as more and more tourists use their own mobile devices to search for the information they want. In this research, we propose a navigation system to control the crowd flow and lead users to specific destinations or areas naturally by presenting suitable information to them. In this paper, we will explain the experiments performed using the proposed system during an event held in Osaka called "Osaka Mizube Bar". The results of the experiment confirmed that the behavior of users can be affected by controlling the rank list that they check on their devices; furthermore, users can be led to a specific destination or area by taking advantage of this function.

The paper describes the design, implementation, and test of an autonomous vehicle navigation system using vehicle model and particle filter tracking algorithm. Typically, a vehicle navigation system comprises of real-time environment perception, vehicle localization, collision avoidance, path planning, and path following. In order to achieve the features for intelligent autonomous vehicle, a sensor suite of integrated inertial measurement unit (IMU), GNSS receiver, and incremental encoder is developed for vehicle position estimation. A map-aided path planning strategy is employed to generate a reference route. To this end, a UMI (User Machine Interface) is developed to facilitate the observation of a goal-oriented path tracking situation. The system utilizes particle filter algorithm to guide the vehicle following the planned path in terms of vehicle estimation control. The recursive particle filter is able to weight the cells and response the angle as well as estimated position information. All the sensors are integrated into an embedded computer platform and able to assess the autonomous driving capability. The test is conducted on campus by installing the sensor suite and embedded computer platform into an electricintegrated inertial measurement unit vehicle.

Public transport vehicles often share a road network with other road users making their journeys susceptive to changing road conditions and especially to congestion. Travelers using such public transport increasingly depend on real-time information to plan their journeys. While such information can be provided by Automatic Vehicle Location (AVL) systems, AVLs depend heavily on large-scale deployment of designated sensory equipment, which may prevent their pervasive adoption. This paper presents a system for estimating vehicle location based on information generated by data sources typically integrated within existing ITS platforms. This enables location estimation for public transport vehicles without the need for deploying a designated sensor infrastructure in each vehicle, thereby reducing deployment and maintenance cost significantly. A prototypical vehicle location estimation system has been realized as part of and using data provided by the iTransIT ITS framework. Initial evaluation results show that such a system is feasible in a distributed manner and that estimated results are within 20% compared to empirical data.

Accuracy of independent GPS navigation systems is tied to the map-matching (MM) algorithm, which overcomes GPS error and traffic map error to map ve-hicle GPS tracks onto corresponding roads. This paper presents a map-matching process compatible with junction adjusting. An innovative idea is expressed in pa-per to improve the accuracy of intersection node at vector map by utilizing the large amounts of GPS trajectory data, which come from map-matching procedure. With the adaptively corrected digital map, the simulation result gave better map-matching effect, and showed the effectiveness of map-matching compatible with junction adjusting.

In our current research, we are developing a practical mobile vehicle navigation system which is capable of controlling multiple vehicles on the general environment. In order to realize the practical use, navigation system should have high accuracy and be low cost. Therefore, in this paper, we propose the vehicle navigation system which realizes vehicles navigation using high accuracy localization scheme by ceiling cameras with infrared filters and LED markers. Our key ideas are two topics. One topic is system integration which consists of high accuracy recognition system for LED marker and AGV controller with low cost. The other topic is novel AGV navigation scheme with high accuracy under occlusion condition. For practical use, mobile vehicles are required to continue its tasks with safety and high accuracy on temporary occlusion condition. Our developed system can navigate target vehicle based on estimation odometry error with simple algorithm. The performance of our proposed control system is experimented with our prototype.

To solve the localization failure problem of terrain-aided navigation (TAN) system of the autonomous underwater vehicle (AUV) caused by large area of underwater flat terrain in the Arctic, a navigation system with relocation part is constructed to enhance the robustness of localization. The system uses particle filter to estimate the AUV’s position and reduce the nonlinear noise disturbance, and the prior motion information is added to avoid the mismatching caused by the similar altitude of low-resolution map. Based on the estimate data and the measured altitude data, the normalized innovation square (NIS) is used to evaluate the differentiation of terrain sequence, and the differentiation is used as a judgment of whether the AUV is in the switch location. A simulation experiment is carried out on the 500 m resolution underwater map of the Arctic. The results show that adding the prior motion information can restrain the divergence of the estimator; NIS can accurately reflect the sharp change of terrain sequence. After the relocation process, the AUV can still maintain the positioning accuracy within 2 km after running 50 km in the area including flat and rough terrain. This research solves the problem of localization errors in the Arctic flat terrain in the system level and provides a solution for the application of underwater navigation in the Arctic.

Heuristic resolution for multiple inheritance in object-oriented expert system building tools