Vehicular Positioning Research Articles

IoV-Based Collision Avoidance Architecture Using Machine Learning Prediction

It is not practical to assume that all vehicles must have the communication ability for the purpose of realizing the IoV-based collision avoidance architecture. Hence, we need some extra designs to make up for the deficiency. In the literature, some researches use a camera mounted on the infrastructure at an intersection to implement the collision detection. Particularly, they utilize the real-time object detection and dynamic prediction to predict the future positions of vehicles for the collision avoidance. In this paper, we propose a new method to predict the future positions of vehicles as well, which is based on a well-known real-time object detection project, YOLOv4. By incorporating the concept of vehicle dynamics with our machine learning architecture, our design can estimate the further future vehicular position more accurately and stably. Lastly, the experimental results show the performance of our algorithm for predicting the future vehicular positions and realizing the collision avoidance architecture.

Beamwidth Optimization for Millimeter-Wave V2V Communication Between Neighbor Vehicles in Highway Scenarios

Millimeter-wave (mmWave) communication is considered to be a promising candidate to enable multi-Gbps data rates in future vehicle-to-everything (V2X) communication. Beam alignment is quite crucial for beam-based mmWave communication and the beam sweeping method is widely adopted for the alignment at present. However, this kind of method will cause large overhead in beam alignment and is inefficient in high mobility environment due to Doppler spread. In this paper, we design an overhead-free vehicular-position-based beam alignment scheme for mmWave V2V communication between neighbor vehicles in highway scenarios. In the proposed beam alignment scheme, the beam is directly steered to the estimated vehicular position without any searching steps in beam training. To avoid beam misalignment caused by localization errors and to maximize transmission throughput, the problem is formulated as a tailored beamwidth optimization problem. A Monte Carlo based Beamwidth Optimization (MCBO) method is developed to divide this optimization into two phases and solve this problem statistically. Simulation results demonstrate that, comparing to the widely-adopted beam-sweeping based beam alignment schemes, the proposed vehicular-position-based overhead-free beam alignment scheme with MCBO method can provide significant throughput improvements in general car-following scenarios on the highway.

Cooperative Multi-Point Vehicular Positioning Using Millimeter-Wave Surface Reflection

Multi-point vehicular positioning is an essential operation for autonomous vehicles. However, the state-of-the-art positioning technologies, relying on reflected signals from a target (i.e., RADAR and LIDAR), cannot work without line-of-sight (LoS). Besides, it takes significant time for environment scanning and object recognition with potential detection inaccuracy, especially in complex urban situations. Some recent fatal accidents involving autonomous vehicles further expose such limitations. In this article, we aim at overcoming these limitations by proposing a novel relative positioning approach, called Cooperative Multi-point Positioning (COMPOP). The COMPOP establishes cooperation between a target vehicle (TV) and a sensing vehicle (SV) if a LoS path exists, where a TV explicitly lets an SV to know the TV’s existence by transmitting positioning waveforms. This cooperation makes it possible to remove the time-consuming scanning and target recognizing processes, facilitating real-time positioning. One prerequisite for the cooperation is a clock synchronization between a pair of TV and SV. To this end, we use a phase-differential-of-arrival (PDoA) based approach to remove the TV-SV clock difference from the received signal. With clock difference correction, the TV’s position can be obtained via peak detection over a 3D power spectrum constructed by a Fourier transform (FT) based algorithm. The COMPOP also incorporates nearby vehicles, without knowing their locations, into the above cooperation for the case without a LoS path. Specifically, several strong non-LoS (NLoS) links from the TV to the SV can be generated via mirror-like reflections over the neighboring vehicles’ metal surfaces. Following the same procedures in the LoS case, virtual TVs mirrored by nearby vehicles can be detected. By exploiting the geometric relation between the virtual and actual TVs, COMPOP can be achieved by intelligently combining the virtual TVs to position the actual TV. The effectiveness of the COMPOP is verified by several simulations concerning practical channel parameters.

Performance Evaluation of Different Grade IMUs for Diagnosis Applications in Land Vehicular Multi-Sensor Architectures

Vehicular positioning systems are necessary for the development of autonomous vehicles and advanced driver assistance systems (ADAS). In recent years, Inertial Measurement Units (IMU) based on micro-electromechanical systems (MEMS) have been included in proposals for multi-sensor positioning system architectures in order to take advantage of their cost and size. The measurement errors propagation to the positioning solution have limited its application for long-term positioning solution. However, it can play a key role in those applications where tri-axial attitude and accelerations can be indicators of curves, slopes, cants, etc. and it can be used as diagnostic of other sensors measurements. This work is focused on the use of IMUs for diagnostic applications and it compares medium-end (xSens MTi-100) and low-end (Bosch BMI160) grade MEMS-based IMUs in an experimental road test using a fusion of a high-end IMU (KVH GEO-FOG), GNSS and wheel speed sensor as reference. In addition, a research question about whether the calibration is the main reason between different grade IMUs has been formulated. Thus, a simple, manufacturable and cost-efficient calibration technique is applied to the low-end IMU in order to compare its performance improvement with the medium-end one. The raw measurements (angular rates and specific forces) and navigation states (tri-axial attitude and accelerations) are considered for diagnosis and they are statistically compared to evaluate the performance of each IMU. It is concluded that the calibration technique used makes the low-end IMU performance similar to that of the medium-end one. Consequently, this work contributes to optimizing the cost of land vehicular positioning systems when choosing the most appropriate sensor based on the accuracy and precision required for the application.

Vehicular Localization With Using China Electronic License Plate System

In the past five years, electronic license plate (ELP) systems have been widely deployed in most China big cities, which provide a novel solution for vehicular localization. Since vehicular position information plays a crucial role in intelligent transportation systems (ITS), the ELP based vehicular localization method is studied to overcome the shortcoming of camera based method in this paper. The ELP system, a customized UHF RFID system, uses RFID tag as a electronic license plate and RFID reader as ELP reader. Thus, the vehicular localization problem is transformed as a ELP localization problem. After analyzing, a ranging based solution is proposed to solve the vehicular localization problem. A multi-frequency based phase difference of arrival (PDOA) ranging algorithm is utilized to calculate the distances between the ELP and antennas. Then, the localization problem is modeled as an optimization problem and the reason why traditional algorithm can not solve this problem is discussed. After that, a novel local search based algorithm is proposed to solve the localization problem. Various experiments are designed and performed to verify the proposed method. Experimental results show that the proposed method achieves a centimeter level ranging and localization accuracy and the proposed localization algorithm converges quickly.

Blockchain-Enabled Internet of Vehicles With Cooperative Positioning: A Deep Neural Network Approach

Although vehicular global positioning system (GPS) has been widely applied in many traffic scenarios, it is far from achieving lane-level positioning due to its low accuracy. Existing cooperative positioning (CP) methods have improved vehicular positioning accuracy to varying degrees, which still have challenges in further improving the system’s robustness and security. In this article, we propose a novel framework of blockchain-enabled Internet of Vehicles (IoV) with CP for improving vehicular GPS positioning accuracy, system robustness, and security. First, a self-positioning correction scheme for the intelligent vehicles is proposed to improve their positioning accuracy, which uses the multitraffic signs as benchmarks to correct the vehicular position (given by GPS) by deep neural network (DNN) algorithm. We further design a multi-intelligent vehicle positioning error sharing model to reduce GPS positioning error of common vehicles (CoVs) in the same segment or area. In addition, to realize information sharing between vehicles and ensure system security, the connections among intelligent vehicles, CoVs, and roadside units are built by proposing a blockchain-enabled architecture that includes IoV subsystem and blockchain subsystem, where the corresponding mechanism of the information choosing, information sharing, and penalty is designed. Extensive simulation results show the accuracy, robustness, and security of our proposal in terms of vehicular positioning, information transferring, and sharing.

Radio-based vehicle dynamic tracking in GNSS degraded environments

The vehicular position information plays an important role in the vehicle communication. In open environments without signal blockage, global navigation satellite system (GNSS) has achieved on-road level accuracy and good reliability, However, the vehicle dynamic tracking is difficult in GNSS degraded environments. Usually, vehicle dynamic tracking consists of three portions: static positioning system, manoeuvring model and fusion algorithm. A radiobased positioning system aided by roadside units (RSUs) is employed as static positioning system, while the adaptive Kalman filter algorithm is used as fusion algorithm. In this paper, a new manoeuvring model is studied. Firstly, the problem of 'current' model is analysed. Secondly, a 'current-ellipse' manoeuvring model is proposed to adapt the strong and weak manoeuvring simultaneously. The experiments show that it can improve the tracking accuracy. Finally, a vehicle tracking case is discussed, its results show that root mean square error (RMSE) is smaller than 2 m, and the positioning accuracy can meet the position-based vehicular communication.

Kinematic and Dynamic Vehicle Model-Assisted Global Positioning Method for Autonomous Vehicles with Low-Cost GPS/Camera/In-Vehicle Sensors.

Real-time, precise and low-cost vehicular positioning systems associated with global continuous coordinates are needed for path planning and motion control in autonomous vehicles. However, existing positioning systems do not perform well in urban canyons, tunnels and indoor parking lots. To address this issue, this paper proposes a multi-sensor positioning system that combines a global positioning system (GPS), a camera and in-vehicle sensors assisted by kinematic and dynamic vehicle models. First, the system eliminates image blurring and removes false feature correspondences to ensure the local accuracy and stability of the visual simultaneous localisation and mapping (SLAM) algorithm. Next, the global GPS coordinates are transferred to a local coordinate system that is consistent with the visual SLAM process, and the GPS and visual SLAM tracks are calibrated with the improved weighted iterative closest point and least absolute deviation methods. Finally, an inverse coordinate system conversion is conducted to obtain the position in the global coordinate system. To improve the positioning accuracy, information from the in-vehicle sensors is fused with the interacting multiple-model extended Kalman filter based on kinematic and dynamic vehicle models. The developed algorithm was verified via intensive simulations and evaluated through experiments using KITTI benchmarks (A project of Karlsruhe Institute of Technology and Toyota Technological Institute at Chicago) and data captured using our autonomous vehicle platform. The results show that the proposed positioning system improves the accuracy and reliability of positioning in environments in which the Global Navigation Satellite System is not available. The developed system is suitable for the positioning and navigation of autonomous vehicles.

Integrated Positioning for Connected Vehicles

In the era of autonomous cars, accurate vehicular positioning becomes very essential. The global navigation satellite systems (GNSS) suffer from signal blockage and severe multipath in urban canyons, which degrades the positioning accuracy and availability. Therefore, vehicles solely relying on positioning from GNSS receivers have limited performance. In this research, we present a novel unified cooperative positioning solution which enhances positioning accuracy and availability in urban canyons. The proposed system exploits the fact that vehicles have different positioning resources and is based on angle approximation, which artificially generates the hindered pseudorange by sharing angle information between vehicles using dedicated short-range communication. In addition, we propose a system that employs the proposed cooperative technique to assist the loose integration between the inertial navigation system (INS) and the GPS system (using extended Kalman filter) during partial GPS outages. Using raw data from inertial sensors and GPS receivers in the real road trajectories, we implement the cooperative INS/GPS loose integration and show that our cooperative integrated system outperforms the non-cooperative integrated system. The performance metrics used are the 2-D positioning root-mean-square error, the maximum 2-D positioning error, and the positioning accuracy gain (PAG). Specifically, the PAG gain is around 88%, 80%, and 60% when the number of blocked satellites is one, two, and three, respectively.

PTCCR: A Path Transmission Costs-Based Multi-Lane Connectivity Routing Protocol for Urban Internet of Vehicles

In Internet of Vehicles (IoV), high vehicular mobility causes frequent changes in the density of vehicles, discontinuity in inter-vehicle communication, variation of network topology and constraints for routing protocols. Besides, with vehicular positions and street-level digital maps available, the intersection-based geographic routing becomes indispensable considering its ability for avoid forwarding packets through segments with low network density and high scale of network disconnections. In this paper, considering the benefits of intersection-based routing and challenges of high dynamic IoV, a Path Transmission Costs-based Multi-lane Connectivity Routing protocol (PTCCR) is proposed with the help from intersection nodes and/or neighboring nodes. First, we investigate the multi-lane connectivity based on vehicular speed under free-flow state for various types of road sections. Second, A Path Transmission costs (PTC) measurement mechanism is proposed considering the impact of the sequence of selected sections or intersections on the routing performance. After that, the PTC of sent packets is quantitatively analyzed and used as the path selection metric. Finally, the path with the largest multi-lane connectivity and lowest PTC is selected as the optimal path taking the transmission direction, neighbor's location and destination position into account. Numerical results show that our proposed PTCCR outperforms two state-of-art routings, i.e., the real-time intersection-based segment aware routing protocol (RTISAR) and Reliable Traffic Aware Routing protocol (RTAR), in terms of packet delivery ratio, average end-to-end delay and communication overhead.

Joint Optimization of Message Transmissions With Adaptive Selection of CCH Interval in VANETs

In vehicular networks, the safety-related messages (emergency and periodic messages) and non-safety message (RFS: request for service message) should share the scarce wireless resource. Emergency messages deliver time-critical information with guaranteed reliability, <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">e.g.</i> road-safety information, whereas periodic ones convey normal status update information, <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">e.g.</i> vehicular positions, show that is less time- and loss-critical. The RFS message is used for reserving Internet data service through the infrastructure on the road. In the channel access for vehicles, the synchronization interval (SI) is repeated every 100 ms and each SI is divided by one control channel interval (CCHI) for the transmission of safety-related messages and RFS message and by one service channel interval (SCHI) for the Internet service from the roadside infrastructure. It is crucial to set the length of CCHI appropriately in the SI because of the dynamic environment of VANETs. In this paper, we consider two optimization problems of message transmissions depending on whether the length of CCHI is flexible or not: 1) a fixed CCHI (non-adaptive) and 2) a variable CCHI (adaptive), respectively, and obtain the optimal MAC parameters where we adopt the IEEE 802.11e enhanced distributed channel access (EDCA) protocol with a “strict” priority for the emergency message. Finally, we conduct extensive simulations to validate our analytical findings.

Connected-Vehicle Data Exchanges and Positioning Computing Based on the Publish-Subscribe Paradigm

Connected vehicles for safety and traffic efficient applications require device-to-device connections supporting one-to-many and many-to-many communication, precise absolute and relative positioning and distributed computing. Currently, the 5.9 GHz Dedicated Short Range Communications (DSRC) and 4G-Long-Term Evolution (LTE) are available for connected vehicle services. But both have limitations in reliability or latency over large scale field operational tests and deployment. This paper proposes the device-to-device (D2D) connectivity framework based on publish-subscribe architecture, with Message Queue Telemetry Transport (MQTT) protocol. With the publish-subscribe communication paradigm, road mobile users can exchange data and information in moderate latency and high reliability manner, having the potential to support many Vehicle to Everything (V2X) applications, including vehicle to vehicle (V2V), vehicle to roadside infrastructure (V2I), and vehicle to bicycle (V2B). The D2D data exchanges also facilitate computing for absolute and relative precise real-time kinematic (RTK) posi-tioning. Vehicular experiments were conducted to evaluate the performance of the proposed publish-subscribe MQTT protocols in term of latency and reliability. The latency of data exchanges is measured by One-trip-time (OTT) and the reliability is measured by the packet loss rate (PLR). Our results show that the latency of GNSS raw data exchanges between vehicles through 4G cellular networks at the rate of 10 Hz and the data rates of 10 kbps are less than 300 ms while the reliability is over 96%. Vehicular positioning experiments have also shown that vehicles can exchange raw GNSS data and complete mov-ing-base RTK positioning with the positioning availability of 98%.

Applications of intelligent computing in vehicular networks

Purpose This paper aims to introduce vehicular network platform, routing and broadcasting methods and vehicular positioning enhancement technology, which are three aspects of the applications of intelligent computing in vehicular networks. From this paper, the role of intelligent algorithm in the field of transportation and the vehicular networks can be understood. Design/methodology/approach In this paper, the authors introduce three different methods in three layers of vehicle networking, which are data cleaning based on machine learning, routing algorithm based on epidemic model and cooperative localization algorithm based on the connect vehicles. Findings In Section 2, a novel classification-based framework is proposed to efficiently assess the data quality and screen out the abnormal vehicles in database. In Section 3, the authors can find when traffic conditions varied from free flow to congestion, the number of message copies increased dramatically and the reachability also improved. The error of vehicle positioning is reduced by 35.39% based on the CV-IMM-EKF in Section 4. Finally, it can be concluded that the intelligent computing in the vehicle network system is effective, and it will improve the development of the car networking system. Originality/value This paper reviews the research of intelligent algorithms in three related areas of vehicle networking. In the field of vehicle networking, these research results are conducive to promoting data processing and algorithm optimization, and it may lay the foundation for the new methods.

The VANET-Solution Approach for Data Packet Forwarding Improvement

Vehicular Ad-hoc Networks (VANETs) are ad hoc wireless mobile networks which is important role in commercial applications and safety of public vehicular communications. In VANETs, data routing is a major problem because of its quick change in topology and vehicular high mobility. The routing protocols that base on vehicular position are attracting much interest because of the availability and advancement of devices equipped with GPS. A major problem with VANETs is the frequent disruptions in the path of vehicles due to the high mobility of the participating vehicles. This can lead to the breaking of established links and result in low throughput, delay in service delivery, less overhead as well as low rate of delivery. In this paper, the Civil Defiance Vehicle (CDV) approach is proposed. The proposed approach (CDV-VANET) compared to VANET, incorporates a potential score-based strategy which is traditionally better in performance and might achieve less delay in service delivery with a higher rate of delivery.

Cooperative Positioning Technique With Decentralized Malicious Vehicle Detection

In the cooperative vehicular positioning networks (CVPN), non-line-of-sight (NLOS) delay in the vehicle-to-vehicle (V2V) ranging and malicious attacks, such as location spoofing and the manipulation of V2V ranging, can be significant threats to vehicle safety. However, the resulting observation from a vehicle in the event of any of the threats is the same; difference observation between the measured range and the Euclidian distance based on vehicles’ shared location coordinates. In this paper, we propose a decentralized malicious vehicle detection technique for cooperative positioning technique (CPT), in which each vehicle evaluates the reliability of neighboring vehicles based on the observed difference and share the reliability with each other, so that each vehicle in the CVPN can collect the reliability of neighboring vehicles evaluated by their own neighboring vehicles. However, it is found that a malicious vehicle can make another attack; distribution of distorted reliability of its neighboring vehicles. To cope with the malicious attacks and NLOS delay effectively, we propose the weighted reliability and demonstrate that the proposed technique can detect malicious attacks (location spoofing, ranging manipulation, message distortion) and distinguish malicious vehicles from vehicles with a NLOS link. With Monte Carlo simulations using V2V radio parameters determined empirically, we demonstrate that the proposed technique achieves higher enhancement and superior robustness to malicious attacks than the conventional CPTs.

5G mmWave Positioning for Vehicular Networks

5G technologies present a new paradigm to provide connectivity to vehicles, in support of high data-rate services, complementing existing inter-vehicle communication standards based on IEEE 802.11p. As we argue, the specific signal characteristics of 5G communication turn out to be highly conducive for vehicle positioning. Hence, 5G can work in synergy with existing on-vehicle positioning and mapping systems to provide redundancy for certain applications, in particular automated driving. This article provides an overview of the evolution of cellular positioning and discusses the key properties of 5G as they relate to vehicular positioning. Open research challenges are presented.

Autonomous Car Parking System through a Cooperative Vehicular Positioning Network.

The increasing development of the automotive industry towards a fully autonomous car has motivated the design of new value-added services in Vehicular Sensor Networks (VSNs). Within the context of VSNs, the autonomous car, with an increasing number of on-board sensors, is a mobile node that exchanges sensed and state information within the VSN. Among all the value added services for VSNs, the design of new intelligent parking management architectures where the autonomous car will coexist with traditional cars is mandatory in order to profit from all the opportunities associated with the increasing intelligence of the new generation of cars. In this work, we design a new smart parking system on top of a VSN that takes into account the heterogeneity of cars and provides guidance to the best parking place for the autonomous car based on a collaborative approach that searches for the common good of all of them measured by the accessibility rate, which is the ratio of the free parking places accessible for an autonomous car. Then, we simulate a real parking lot and the results show that the performance of our system is close to the optimum considering different communication ranges and penetration rates for the autonomous car.

A DSRC-Based Vehicular Positioning Enhancement Using a Distributed Multiple-Model Kalman Filter

Some inherent shortcomings of the global positioning systems (GPSs), such as limited accuracy and availability, limit the positioning performance of a vehicular location system in urban harsh environments. This motivates the development of cooperative positioning (CP) methods based on emerging vehicle-to-anything communications. In this paper, we present a framework of vehicular positioning enhancement based on dedicated short range communications (DSRC). An interactive multiple model is first used to track the distributed manners of both the vehicle acceleration variations and the switching of the covariances of DSRC physical measurements such as the Doppler frequency shift and the received signal strength indicator, with which a novel CP enhancement method is presented to improve the distributed estimation performance by sharing the motion states and the physical measurements among local vehicles through vehicular DSRC. We have also presented an analysis on the positioning performance, and a closed-formed lower bound, named the modified square position error bound (mSPEB), is derived for bounding the positioning estimation performance of CP systems. Simulation results have been supplemented to compare our proposed method with the stand-alone GPS implementation in terms of the root-mean-square error (RMSE), showing that the obtained positioning enhancement can improve comprehensive positioning performance by the percentage varying between about 35% and about 72% under different traffic intensities and the connected vehicle penetrations. More importantly, the RMSE achieved by our method is shown remarkably closed to the root of the theoretical mSPEB.

A Novel Approach for Improved Vehicular Positioning Using Cooperative Map Matching and Dynamic Base Station DGPS Concept

In this paper, a novel approach for improving vehicular positioning is presented. This method is based on the cooperation of the vehicles by communicating their measured information about their position. This method consists of two steps. In the first step, we introduce our cooperative map matching method. This map matching method uses the V2V communication in a vehicular ad hoc network (VANET) to exchange global positioning system (GPS) information between vehicles. Having a precise road map, vehicles can apply the road constraints of other vehicles in their own map matching process and acquire a significant improvement in their positioning. After that, we have proposed the concept of a dynamic base station DGPS (DDGPS), which is used by vehicles in the second step to generate and broadcast the GPS pseudorange corrections that can be used by newly arrived vehicles to improve their positioning. The DDGPS is a decentralized cooperative method that aims to improve the GPS positioning by estimating and compensating the common error in GPS pseudorange measurements. It can be seen as an extension of DGPS where the base stations are not necessarily static with an exact known position. In the DDGPS method, the pseudorange corrections are estimated based on the receiver's belief on its positioning and its uncertainty and then broadcasted to other GPS receivers. The performance of the proposed algorithm has been verified with simulations in several realistic scenarios.

Low-cost sensors aided vehicular position prediction with partial least squares regression during GPS outage

Vehicular position prediction is very important in intelligent transport systems (ITS), and the requirements of accuracy for position prediction have been significantly increasing in recent years. In this paper, we focus on designing a more low-cost and convenient method which can operate during GPS outages. In order to better deal with the position prediction during the lack of GPS signals, we introduce a windowed partial least squares regression (WPLSR) approach where vehicle position information from the low-cost sensors was used. Moreover, the window is adjustable and it reduces the step of regression in WPLSR algorithm. The sensor data outside the window that has nothing to do with the latest position prediction is eliminated. Therefore, the position accuracy can be improved significantly. Finally, the proposed method is evaluated by using road experiments from real urban areas. Compared with the conventional techniques such as PLSR and extended Kalman filter combined with an interactive multimodel (IMM-EKF), the results show that WPLSR presents the higher position accuracy especially during the GPS outages.