A Spatio-temporal Data Model for Road Network in Data Center Based on Incremental Updating in Vehicle Navigation System

The technique of incremental updating,which can better guarantee the real-time situation of navigational map,is the developing orientation of navigational road network updating.The data center of vehicle navigation system is in charge of storing incremental data,and the spatio-temporal data model for storing incremental data does affect the efficiency of the response of the data center to the requirements of incremental data from the vehicle terminal.According to the analysis on the shortcomings of several typical spatio-temporal data models used in the data center and based on the base map with overlay model,the reverse map with overlay model (RMOM) was put forward for the data center to make rapid response to incremental data request.RMOM supports the data center to store not only the current complete road network data,but also the overlays of incremental data from the time when each road network changed to the current moment.Moreover,the storage mechanism and index structure of the incremental data were designed,and the implementation algorithm of RMOM was developed.Taking navigational road network in Guangzhou City as an example,the simulation test was conducted to validate the efficiency of RMOM.Results show that the navigation database in the data center can response to the requirements of incremental data by only one query with RMOM,and costs less time.Compared with the base map with overlay model,the data center does not need to temporarily overlay incremental data with RMOM,so time-consuming of response is significantly reduced.RMOM greatly improves the efficiency of response and provides strong support for the real-time situation of navigational road network.

A Fuzzy Logic-Based Framework for Route Choice in Vehicle Navigation Systems

Vehicle Navigation Systems (VNS) is an important component of Intelligent Transportation Systems (ITS). These Systems are designed to assist drivers in making pre trip and enroute travel choice decisions, and typically, they must provide route choice, route guidance and other related services. Although there have been a lot of existed systems in the market, and most of them used lots of contemporary technologies, they are believed short of ″true intelligence″, because they paid little attention to the subjective issues in driver′s route choice behavior, such as travel objectives and personal preferences, etc. \;However, the VNS is designed for its users, and the successful implementation of VNS is largely dependent on the driver′s acceptance. If the driver feels that the VNS can′t give him (her) a satisfactory choice, he (she) will not use it, then, the marketing value of VNS will decline. And on the whole, the transport benefit that is mainly gained by the wide use of ITS will lost. \;Supported by the research project of ″Beijing Intelligent Urban Transportation Systems″, this paper presents a conceptual model to deal with this problem. We first defined the driver′s objective as a linguistic statement that has a set of attributes. These attributes are then treated as the fuzzy sets on the universal of all the existed routes. By determining each attribute′s membership function and assign driver dependent perception to these attributes, we can change the multi criteria route choice problem into a fuzzy logic based decision making problem. Then, to meet the demands of dynamic real time route selection, we use a limited routes set for choice and can swiftly get a satisfactory solution that we think is the driver′s actually needs.

Real-time GNSS precise point positioning with smartphones for vehicle navigation

The availability of raw Global Navigation Satellite System(GNSS)measurements from Android smart devices gives new possibilities for precise positioning solutions,e.g.,Precise Point Positioning(PPP).However,the accuracy of the PPP with smart devices currently is a few meters due to the poor quality of the raw GNSS measurements in a kinematic scenario and in urban environments,particularly when the smart devices are placed inside vehicles.To promote the application of GNSS PPP for land vehicle navigation with smart devices,this contribution studies the real-time PPP with smartphones.For data quality analysis and positioning performance validation,two vehicle-based kinematic positioning tests were carried out using two Huawei Mate30 smartphones and two Huawei P40 smartphones with different installation modes:the vehicle-roof mode with smartphones mounted on the top roof outside the vehicle,and the dashboard mode with smartphones stabilized on the dashboard inside the vehicle.To realize high accuracy positioning,we proposed a real-time smartphone PPP method with the data processing strategies adapted for smart devices.Positioning results show that the real-time PPP can achieve the horizontal positioning accuracy of about 1–1.5 m in terms of root-mean-square and better than 2.5 m at the 95th percentile for the vehicle-based kinematic positioning with the experimental smartphones mounted on the dashboard inside the vehicle,which is the real scenario in vehicle navigation.

GIL:a tightly coupled GNSS PPP/INS/LiDAR method for precise vehicle navigation

Accurate positioning and navigation play a vital role in vehicle-related applications,such as autonomous driving and precision agriculture.With the rapid development of Global Navigation Satellite Systems(GNSS),Precise Point Positioning(PPP)technique,as a global positioning solution,has been widely applied due to its convenient operation.Nevertheless,the performance of PPP is severely affected by signal interference,especially in GNSS-challenged environments.Inertial Navigation System(INS)aided GNSS can significantly improve the continuity and accuracy of navigation in harsh environments,but suffers from degradation during GNSS outages.LiDAR(Laser Imaging,Detection,and Ranging)-Inertial Odometry(LIO),which has performed well in local navigation,can restrain the divergence of Inertial Measurement Units(IMU).However,in long-range navigation,error accumulation is inevitable if no external aids are applied.To improve vehicle navigation performance,we proposed a tightly coupled GNSS PPP/INS/LiDAR(GIL)integration method,which tightly integrates the raw measurements from multi-GNSS PPP,Micro-Electro-Mechanical System(MEMS)-IMU,and LiDAR to achieve high-accuracy and reliable navigation in urban environments.Several experiments were conducted to evaluate this method.The results indicate that in comparison with the multi-GNSS PPP/INS tightly coupled solution the positioning Root-Mean-Square Errors(RMSEs)of the proposed GIL method have the improvements of 63.0%,51.3%,and 62.2%in east,north,and vertical components,respectively.The GIL method can achieve decimeter-level positioning accuracy in GNSS partly-blocked environment(i.e.,the environment with GNSS signals partly-blocked)and meter-level positioning accuracy in GNSS difficult environment(i.e.,the environment with GNSS hardly used).Besides,the accuracy of velocity and attitude estimation can also be enhanced with the GIL method.

Autonomous vehicles: challenges, opportunities, and future implications for transportation policies

This study investigates the challenges and opportunities pertaining to transportation policies that may arise as a result of emerging autonomous vehicle （AV） technologies. AV technologies can decrease the transportation cost and increase accessibility to low-income households and persons with mobility issues. This emerging technology also has far-reaching applications and implications beyond all current expectations. This paper provides a comprehensive review of the relevant literature and explores a broad spectrum of issues from safety to machine ethics. An indispensable part of a prospective AV development is communication over cars and infrastructure （connected vehicles）. A major knowledge gap exists in AV technology with respect to routing behaviors. Connected- vehicle technology provides a great opportunity to imple- ment an efficient and intelligent routing system. To this end, we propose a conceptual navigation model based on a fleet of AVs that are centrally dispatched over a network seeking system optimization literature on two fronts： （i） This study contributes to the it attempts to shed light on future opportunities as well as possible hurdles associated with AV technology; and （ii） it conceptualizes a navigation model for the AV which leads to highly efficient traffic circulations.

Navigation Map Data Representation and Parallel Display Algorithm in an Embedded Environment

Map data display is the basic information representation mode under embedded real-time navigation. After a navigation display data set （NDIS_SET） with several dimensions and corresponding mathematical description formula are designed, a series of rules and algorithms are advanced to optimize embedded navigation data and promote data index and input efficiency. A new parallel display algorithm with navigation data named N PDIS is then presented to adapt to limited embedded resources of computation and memory after a normal navigation data display algorithm named NDIS and related problems are analyzed, N_PDIS can synchronously create two preparative bitmapa by two parallel threads and switch one of them to screen automatically. Compared with NDIS, the results show that N_PDIS is more effective in improving display efficiency.

Optimal path finding algorithms based on SLSD road network model

A solution to compute the optimal path based on a single-line-single-directional（SLSD）road network model is proposed.Unlike the traditional road network model,in the SLSD conceptual model,being single-directional and single-line style,a road is no longer a linkage of road nodes but abstracted as a network node.Similarly,a road node is abstracted as the linkage of two ordered single-directional roads.This model can describe turn restrictions,circular roads,and other real scenarios usually described using a super-graph.Then a computing framework for optimal path finding（OPF）is presented.It is proved that classical Dijkstra and A algorithms can be directly used for OPF computing of any real-world road networks by transferring a super-graph to an SLSD network.Finally,using Singapore road network data,the proposed conceptual model and its corresponding optimal path finding algorithms are validated using a two-step optimal path finding algorithm with a pre-computing strategy based on the SLSD road network.

Design and Implementation of Bidirectional Dijkstra Algorithm

Bidirectional Dijkstra algorithm whose time complexity is 8O(n~2) is proposed. The theory foundation is that the classical Dijkstra algorithm has not any directional feature during searching the shortest path. The algorithm takes advantage of the adjacent link and the mechanism of bidirectional search, that is, the algorithm processes the positive search from start point to destination point and the negative search from destination point to start point at the same time. Finally, combining with the practical application of route-planning algorithm in embedded real-time vehicle navigation system (ERTVNS), one example of its practical applications is given, analysis in theory and the experimental results show that compared with the Dijkstra algorithm, the new algorithm can reduce time complexity, and guarantee the searching precision, it satisfies the needs of ERTVNS.

Filtering and Estimation of Vehicular Dead Reckoning System Based on Hopfield Neural Network

The algorithm of Hopfield neural network filtering and estimation is studied. The model of vehicular dead reckoning system fitting for the algorithm is constructed, and the design scheme of system filtering and estimation based on Hopfield network is proposed. Compared with Kalman filter, the algorithm does not require very precise system model and the prior knowledge of noise statistics and does not diverge easily. The simulation results show that the vehicular dead reckoning system based on Hopfield network filtering and estimation has the good position precision, and needn't require the inertial sensors with high precision. Therefore, the algorithm has the good practicability.

Connectivity-Enhanced Route Selection and Adaptive Control for the Chevrolet Volt

The National Renewable Energy Laboratory and General Motors evaluated connectivity-enabled efficiency enhancements for the Chevrolet Volt. A high-level model was developed to predict vehicle fuel and electricity consumption based on driving characteristics and vehicle state inputs. These techniques were leveraged to optimize energy efficiency via green routing and intelligent control mode scheduling, which were evaluated using prospective driving routes between tens of thousands of real-world origin/destination pairs. The overall energy savings potential of green routing and intelligent mode scheduling was estimated at 5% and 3%, respectively. These represent substantial opportunities considering that they only require software adjustments to implement.

Dual-mode dynamic window approach to robot navigation with convergence guarantees

In this paper,a novel,dual-mode model predictive control framework is introduced that combines the dynamic window approach to navigation with generic path planning techniques through a dual-mode model predictive control framework.The planned path adds information on the connectivity of the free space to the obstacle avoidance capabilities of the dynamic window approach.This allows for guaranteed convergence to a goal location while navigating through an unknown environment at relatively high speeds.The framework is applied in a combined simulation/hardware implementation to demonstrate the computational feasibility and the ability to cope with the constraints of a dynamic system.

Relay navigation strategy study on intelligent drive on urban roads

In order to solve navigation problem of intelligent vehicle driving on urban roads and to achieve the navigation in intersection area, intersection transition area and section area. The relay navigation strategy and algorithm can solve the navigation problem of intelligent vehicle driving in typical urban roads such as intersection area, intersection transition area and section area, realizing seamless handover among different typical areas. Bezier curve function model was introduced to different typical areas, which solved the self-adaption recognition problem in different typical areas and revised positional accuracy with the help of cloud computing positioning service. In order to explain the strategy implement, an instance based on the strategy was adopted. Instance analysis indicates that as for the navigation problem in intersection area, intersection transition area and section area, if the relay navigation strategy is utilized, the self-adaption recognition problem in different typical areas can be handled. Based on the relay navigation strategy, the drive of intelligent vehicle on urban roads can effectively solve the self-adaption recognition problem in different typical areas in urban and further solve driving problems of intelligent vehicle of the same category in urban roads.

Principle and performance of multi-frequency and multi-GNSS PPP-RTK

PPP-RTK which takes full advantages of both Real-Time Kinematic(RTK)and Precise Point Positioning(PPP),is able to provide centimeter-level positioning accuracy with rapid integer Ambiguity Resolution(AR).In recent years,with the development of BeiDou Navigation Satellite System(BDS)and Galileo navigation satellite system(Galileo)as well as the modernization of Global Positioning System(GPS)and GLObal NAvigation Satellite System(GLONASS),more than 140 Global Navigation Satellite System(GNSS)satellites are available.Particularly,the new-generation GNSS satellites are capable of transmitting signals on three or more frequencies.Multi-GNSS and multi-frequency observations become available and can be used to enhance the performance of PPP-RTK.In this contribution,we develop a multi-GNSS and multi-frequency PPP-RTK model,which uses all the available GNSS observations,and comprehensively evaluate its performance in urban environments from the perspectives of positioning accuracy,convergence and fxing percentage.In this method,the precise atmospheric corrections are derived from the multi-frequency and multi-GNSS observations of a regional network,and then disseminated to users to achieve PPP rapid AR.Furthermore,a cascade ambiguity fxing strategy using Extra‐Wide‐Lane(EWL),Wide-Lane(WL)and L1 ambiguities is employed to improve the performance of ambiguity fxing in the urban environments.Vehicle experiments in diferent scenarios such as suburbs,overpasses,and tunnels are conducted to validate the proposed method.In suburbs,an accuracy of within 2 cm in the horizontal direction and 4 cm in the vertical direction,with the fxing percentage of 93.7%can be achieved.Compared to the GPS-only solution,the positioning accuracy is improved by 87.6%.In urban environments where signals are interrupted frequently,a fast ambiguity re-fxing can be achieved within 5 s.Moreover,multifrequency GNSS signals can further improve the positioning performance of PPP-RTK,particularly in the case of small amount of observations.These results demonstrate that the multi-frequency and multi-GNSS PPP-RTK is a promising tool for supporting precise vehicle navigation.

B-DRIVE:A blockchain based distributed IoT network for smart urban transportation

In this paper,I present B-DRIVE—a blockchain-based distributed IoT(Internet of Things)network for smart urban transportation.The network is designed to connect a large fleet of IoT devices,installed on various vehicles and roadside infrastructures,to distributed data storage centers,called as Full-Nodes,to log and disseminate sensor generated data.It connects devices from around the city to multiple Full-Nodes to log timestamped data into the blockchain.These sensors vary from GPS(Global Positioning System),air quality meter,gyrometer to speed cameras in order to facilitate efficient urban mobility.The three identified hardware layers that comprise the network are the IoT layer,Storage layer,and User layer.They consist of Moving/Static-Nodes,Full-Nodes,and Smart devices,respectively.The Moving/Static-Nodes are primarily made up of moving vehicles and road-side infrastructures,respectively,thus acting as various data sources.Whereas,Full-Nodes and Smart devices are institutions and mobile phones,acting as data handler/disseminator and navigator/data visualizer,respectively.The data,or data blocks,received by Full-Nodes get appended into Full and Running-Blockchain,meant for specific purposes.The network is designed to be free from any block mining activity.It provides open access to anonymous sensor data to end-users,especially scientists,policy-makers and entrepreneurs,to develop innovative urban transportation solutions.It is believed that a system like B-DRIVE,along with existing VANETs(Vehicular Ad-hoc NETworks),is capable of answering some of the current urban transportation issues around traffic congestion,navigation,and vehicle parking.Other applications of blockchain data could vary from user activity mapping to VGI(volunteered geographic information)data quality assessment.Two identified limitations of the presented architecture are the low processing power of current IoT devices and the lack of urban IoT infrastructure.