Vehicular Positioning Research Articles

Vehicular Positioning Based on GPS/IMU Data Fusion Aided by V2X Networks

Vehicular Positioning Based on GPS/IMU Data Fusion Aided by V2X Networks

Multi-agent Reinforcement Learning for Distributed Cooperative Vehicular Positioning

Multi-agent Reinforcement Learning for Distributed Cooperative Vehicular Positioning

Integration of Vehicle Dynamic Model and System Identification Model for Extending the Navigation Service Under Sensor Failures

Accurate and reliable localization is of great importance for autonomous vehicles (AV). Mainstream localization approaches in autonomous vehicles (AV) are limited by the reliability of onboard sensors, which could be vulnerable to sensor failure, such as signal outages of the camera and signal spoofing of the global navigation satellite systems (GNSS). Different from these active or passive sensors, the vehicle dynamic model (VDM), which is the application of physical laws to a vehicle in motion, is environmentally independent and is capable of providing vehicle motion estimation continuously. However, the performance of the VDM-based motion estimation is dominated by the accuracy of the system dynamics model. To tackle this issue, this study proposes a sensor-free localization method VDM-SI by integrating system identification into the design of vehicle dynamic models (VDM). A system identification process based on low-order process models is proposed to identify the system dynamics of the AV, where the identified system responses are taken as the control input of VDM to estimate the vehicular positioning. The localization experiments in two scenarios show that the mean absolute translation error of VDM-SI can be reduced by 70% compared to conventional VDM methods. In addition, VDM-SI is experimentally proven to improve the localization performance of sensor fusion-based localization systems with high noise levels. Furthermore, in the application of re-localization after sensors fail and recover, VDM-SI shows strength in enhancing the security of AVs in extreme conditions.

An Improved Inertial Preintegration Model in Factor Graph Optimization for High Accuracy Positioning of Intelligent Vehicles

Accurate and reliable positioning is of great importance for the realization of intelligent vehicles (IV). Factor graph optimization (FGO) has been popularized in the field of robotics for state estimation. As a ubiquitous sensor, the IMU is widely used for vehicular positioning based on the preintegration theory using FGO. However, the existing preintegration model fails to consider the earth's rotation, which challenges the attitude and positioning performance of vehicles equipped with high-precision inertial measurement units (IMU). To fill this gap, an accurate IMU factor that accounts for the earth's rotation rate is designed in this paper. First, an exact preintegration measurement model is derived based on a high-precision inertial navigation system (INS) mechanization. The proposed factor is more accurate and it enables the application of the FGO more suitable for inertial-based integrated navigation with different precision IMUs. In both simulation and field tests, the INS/ Global Navigation Satellite System (GNSS) integration, as a representative of inertial-based integrated navigation, is used to verify the performance of the proposed preintegration factor. The navigation results using our experimental car reveal that the proposed model leads to a more accurate estimation, outperforming the traditional preintegration models, especially in the aspect of attitude estimation.

Narrow band of vehicular things communication system using hybrid pelican-beetle swarm optimization approach for intelligent transportation system

Congestion is caused by the continually expanding number of vehicles worldwide. Intelligent Transportation Systems (ITSs) could be used to build a system that efficiently uses the existing infrastructure and current technological advancements. Several Vehicular applications use the restricted vehicle battery in V2X systems, and a key concern is communication secrecy. Earlier research has focused on channel measurement, autonomous piloting, high-speed transmissions, secretive communications, and QoS analysis but battery-limited V2X has received far less attention. Therefore, resolving the abovementioned complications in the current model is imperative. Thus, a new V2X communication model is developed based on NB-IoT (NarrowBand Internet of Things) approaches. NB -IoT is utilized in the V2X communication model to offer low-cost, high-range, and minimal power to the communication environment. The objective of the proposed NarrowBand of Vehicular Things Communication (NBVTCS)System is to analyze the performance metrics of vehicular communication including energy efficiency rate with different SNR, bit error rate with different vehicular positions, and overall throughput of the system. The simulation was carried out to attain an effective energy efficiency rate, the system's transmit power, bit error rate, and overall system throughput. The proposed vehicular architecture also optimized the abovementioned vehicular metrics with the help of the proposed Hybrid Pelican Beetle Swarm Optimization (HPBSO) approach to achieve a higher accuracy in communication parameters. Finally, the simulation outcome demonstrates that the developed vehicular model is achieved with a minimal energy consumption rate and maximum throughput and also compares the proposed HPBSO technique with other optimization techniques including DOX, EFO, POA, and BSO.

Geometric trust-based secure localization technique for resiliency of GPS outage and location error in vehicular cyber-physical systems (VCPS)

Management of vehicle traffic is a challenging task as it is non-deterministic by nature. Vehicular Cyber-Physical Systems (VCPS) is the emerging field of dynamics of vehicle management. Vehicle localization is considered an important task in VCPS. Many researchers proposed methodologies for this based on the Global Positioning System (GPS) which poses few location identification errors. Also, there are more vulnerabilities to the existing vehicular positioning system due to Zig-Zag attacks and bad-mouth attacks. In this work, an error-free and secure environment for communication between dynamically moving vehicle models has been proposed. In our proposed model a localization technique based on mathematical geometry which is capable of GPS outages and encompasses the dynamism of vehicle and on-road trajectory has been developed. The proposed model includes Extended Kalman filter-based routing to predict the neighbouring vehicle position. To avoid vulnerabilities created by the malicious nodes, a trust-based computation is performed by each node on its neighbours perceiving the authenticity of received messages. To validate the methodology, NS2 tool has been used to simulate the VCPS and to test the efficiency with different scenarios such as erroneous location, GPS outage, and malicious attack. The result shows that the proposed approach is more optimal and secure than the existing methodologies.

Data-Driven Dynamic Event-Triggered Fault-Tolerant Platooning Control

This paper addresses the dynamic event-triggered (DET) fault-tolerant model-free adaptive platooning control (MFAPC) problem of vehicle platoon systems subject to sensor faults. First of all, a novel redefined output is introduced to assist in proving the synchronous tracking of the position and velocity. Based on which, the equivalent dynamic linearization technique is used for transforming the nonlinear vehicular platooning systems into an linear data model. Then, an observer is designed to introduce the estimation of the pseudo-partial derivative (PPD) parameter, which can also eliminate the symbol restriction on it. In addition, a DET mechanism is introduced to save network resources more effectively, and the neural network method is applied to deal with the sensor faults for the safe vehicle driving. Further, a novel DET-based fault-tolerant MFAPC strategy is developed to realize the vehicular position and velocity tracking only using the input-output data. At last, an example is provided to prove the effectiveness of designed MFAPC framework.

Securing UAV Flying Base Station for Mobile Networking: A Review

A flying base station based on an unmanned aerial vehicle (UAV) uses its mobility to extend its connectivity coverage and improve its communication channel quality to achieve a greater communication rate and latency performances. While UAV flying base stations have been used in emergency events in 5G networking (sporadic and temporary), their use will significantly increase in 6G networking, as 6G expects reliable connectivity even in rural regions and requires high-performance communication channels and line-of-sight channels for millimeter wave (mmWave) communications. Securing the integrity and availability of the base station operations is critical because of the users’ increasing reliance on the connectivity provided by the base stations, e.g., the mobile user loses connectivity if the base station operation gets disrupted. This paper identifies the security issues and research gaps of flying base stations, focusing on their unique properties, while building on the existing research in wireless communications for stationary ground base stations and embedded control for UAV drones. More specifically, the flying base station’s user-dependent positioning, its battery-constrained power, and the dynamic and distributed operations cause vulnerabilities that are distinct from those in 5G and previous-generation mobile networking with stationary ground base stations. This paper reviews the relevant security research from the perspectives of communications (mobile computing, 5G networking, and distributed computing) and embedded/control systems (UAV vehicular positioning and battery control) and then identifies the security gaps and new issues emerging for flying base stations. Through this review paper, we inform readers of flying base station research, development, and standardization for future mobile and 6G networking.

A Hybrid State Representation-Based GNSS Filtering Model to Improve Vehicular Positioning Performance

In the era of intelligence, there is a huge demand for precise positioning in the fields such as automatic driving, assisted driving, and vehicle to everything. Due to the continuous and high-precision positioning capability, the global navigation satellite system (GNSS) has been an important foundation for large-scale location applications in open-sky conditions. For land vehicle kinematic navigation, the GNSS positioning method based on Kalman filtering (KF) has also become the primary choice. However, the general state model based on velocity and acceleration in KF lacks mining and utilization of the physical laws of vehicle motion, resulting in the insufficient performance of GNSS KF positioning in urban conditions. Thus, we constructed a hybrid state model, where the vehicle kinematics are classified into nonlinear and quasi-linear motions, and the corresponding velocity constraint (VC) and heading constraint (HC) models are constructed. Then, the reparameterization between VC-model and HC-model is derived through a strict formula, and the hybrid state model can switch adaptively by vehicle motion recognition. Field vehicle test data was collected with a low-cost GNSS receiver and processed by different methods. The test results of different observation conditions and different motions show that the hybrid state model has better positioning performance, and the root-mean-square (rms) errors in the occlusion condition are reduced by 15% compared with only the VC-model. Furthermore, the test results using the public data of the Google Decimeter Challenge project also verify the effectiveness and practicability of the filtering model in vehicular positioning.

Improving smartphone-based positioning accuracy with height constraint and application to pedestrian and vehicular positioning

Since the release of Android version 7 in 2016, the smartphone users have had access to the raw global navigation satellite system (GNSS) measurements (i.e., pseudorange, carrier-phase, Doppler, and carrier-to-noise density ratio (C/N0)) through the new application programming interface (API) called android location (API level 24). This capability opens opportunities to apply different positioning techniques, ranging from absolute to differential techniques, to the smartphone observations. Precise point positioning (PPP) is a powerful method for conducting accurate real-time positioning using a single receiver, and it can be applied to the smartphone observations as well. Most PPP smartphone positioning studies have so far focused on utilizing the GNSS only observations obtained from the smartphone's API. However, incorporating additional information as constraints, such as height information, can enhance accuracy and overall stability. Although the vertical positioning accuracy of GNSS is generally lower than the horizontal accuracy, utilizing recorded height from the smartphone GNSS chipset can still be beneficial. This incorporation increases the degree of freedom and strengthens the geometry between the receiver and satellites. In this study, we assess the effectiveness of the uncombined PPP (UPPP) model in the presence of height constraints. We utilize both pedestrian walking and vehicular datasets collected by a dual-frequency Xiaomi Mi8 device to evaluate the effect of adding height constraint to PPP model. The results demonstrate an average improvement of 22% and 26% on the root-mean-square (RMS) of horizontal error and the 50th percentile error, respectively, when employing the height constraints UPPP model. Additionally, the findings indicated a decrease in PPP convergence time, further supporting the positive impact of incorporating height constraints.

An Application-Oriented Method Based on Cooperative Map Matching for Improving Vehicular Positioning Accuracy

Accurate vehicular positioning is important for intelligent and connected vehicles (ICVs). However, in urban canyons, vehicles that rely solely on global navigation satellite system (GNSS) are susceptible to factors such as signal blocking and multi-path, reducing the positioning performance. In this paper, an application-oriented cooperative map matching (CMM) method is proposed, and a low-cost Global Positioning System (GPS)/BeiDou navigation satellite system (BDS) integrated positioning system is designed. The road constraints of a real traffic environment, which simplifies the computational complexity and facilitates practical applications, are modeled. The positioning system is designed to collect and store the positioning data for experimental analysis. Static and dynamic experiments are conducted to verify the effectiveness of the CMM method. From the experimental results, the mean absolute error (MAE) and root mean square error (RMSE) of the positioning with CMM correction in the static experiment are reduced by 9.0% and 4.9%, respectively. In the dynamic experiment, compared with the original positioning error, the MAE is reduced by 44.2% while the RMSE is reduced by 24.3%. The results show that the proposed method can improve vehicular positioning accuracy effectively in both static and dynamic environments.

Improving GNSS/INS Tightly Coupled Positioning by Using BDS-3 Four-Frequency Observations in Urban Environments

Vehicular positioning in urban environments has become a research hotspot in recent years, and global navigation satellite system/inertial navigation system (GNSS/INS) tightly coupled positioning is a commonly used method. With the broadcast of BDS-3 and Galileo four-frequency signals, the multi-frequency and multi-system tightly coupled positioning method provides more possibilities for vehicular positioning in urban environments. At present, most of the studies on multi-frequency and multi-system mainly focus on static or baseline solutions, and there are few studies on the urban dynamic environment. In this paper, based on the triple-frequency GPS/BDS-2/INS tightly coupled positioning model, the BDS-3 four-frequency observation is introduced to conduct a preliminary study on the performance of GPS/BDS-2/BDS-3/INS tightly coupled positioning. Taking into account the positioning accuracy and calculation efficiency of the tightly coupled positioning, single epoch extra-wide-lane/wide-lane (EWL/WL) observation is used to participate in the modeling of tightly coupled positioning. The EWL/WL ambiguity is solved by the geometry-free (GF) method, in which triple-frequency carrier ambiguity resolution (TCAR) and four-frequency carrier ambiguity resolution (FCAR) are used for triple-frequency and four-frequency observations, respectively. Finally, the positioning performance of the tightly coupled method in this paper is evaluated through vehicular experiment. The experimental results show that in the urban dynamic environment, due to the higher quality of the linear combination of BDS-3 four-frequency, the positioning accuracy of the BDS-3/INS tightly coupled was slightly better than that of the triple-frequency BDS-2/INS. Compared with GPS/BDS-2/INS, the GPS/BDS-2/BDS-3/INS tightly coupled positioning accuracy increased by 29.1% and 58.7% in horizontal and vertical directions, respectively, which can realize decimeter positioning accuracy in urban environments.

A Review on VANET Research: Perspective of Recent Emerging Technologies

Recent technology has modeled VANET (vehicular adhoc network) communication well in terms of privileges to derive vehicular communication technologically to save time, energy, and money. Due to the increase in powerful technology in modern times, VANETs play a vital role in uplifting daily concerns across vehicles and vehicular identities. Hence, to tune VANETs to become compatible with traditional technologies and increase demand, VANETs require upgrading. The severity and frequency of unwanted occurrences have become a considerable concern for our day-to-day lives relating to vehicular position. Thus, verily updated methodologies or working procedures are needed for the future VANET interplay to eradicate such problems occurring through vehicular identities. This article outlines in technology related to VANETS, future developments, and coping issues by deriving comprehensive frameworks, workflow patterns, upgrading procedures including big data, fog computing, SDN (software defined networking), and SIoT (social Internet of Things). This review article previews an in-depth VANET upgrade possibility to address future problem managements under a range of acceptable scientific themes, indicators, and combinations.

GNSS Outlier Mitigation via Graduated Non-Convexity Factor Graph Optimization

Accurate and globally referenced global navigation satellite system (GNSS) based vehicular positioning can be achieved in outlier-free open areas. However, the performance of GNSS can be significantly degraded by outlier measurements, such as multipath effects and non-line-of-sight (NLOS) receptions arising from signal reflections of buildings. Inspired by the advantage of batch historical data in resisting outlier measurements, in this paper, we propose a graduated non-convexity factor graph optimization (FGO-GNC) to improve the GNSS positioning performance, where the impact of GNSS outliers is mitigated by estimating the optimal weightings of GNSS measurements. Different from the existing local solutions, the proposed FGO-GNC employs the non-convex Geman McClure (GM) function to globally estimate the weightings of GNSS measurements via a coarse-to-fine relaxation. The effectiveness of the proposed method is verified through several challenging datasets collected in urban canyons of Hong Kong using automobile level and low-cost smartphone level GNSS receivers.

Super-random states in vehicular traffic — Detection & explanation

This article deals with specific states of traffic flow on a two-lane freeway, in which statistical fluctuations of microscopic quantities (inter-vehicle gaps) are significantly higher than in systems with absolutely random events (Poisson systems). These anomalous states (super-random) are detected in empirical traffic data, specifically in the fast lane at traffic densities up to 25 vehicles per kilometer. The origin of these states is then explained mathematically (using the theory of balance particle systems and tools of random matrix theory), physically (by means of an one-dimensional particle gas subjected to local perturbations caused by overtaking cars) and empirically (using an analogy with phenomena observed in photon counting experiments). In the article we show that overtaking maneuvers, when vehicles from a slow lane are injected into a fast-lane stream of faster moving vehicles, disrupt a local balance in microstructure of fast-lane stream and cause atypical arrangement of vehicular positions, that is very rare, generally. With help of original numerical model we demonstrate that the anomalous states detected are identical to equilibrium states formed in a stochastic particle gas with a potential containing, in addition to a repulsive component, also an attractive component.

A Redundant Configuration of Four Low-Cost GNSS-RTK Receivers for Reliable Estimation of Vehicular Position and Posture.

This paper proposes a low-cost sensor system composed of four GNSS-RTK receivers to obtain accurate position and posture estimations for a vehicle in real-time. The four antennas of the receivers are placed so that every three-antennas combination is optimal to get the most precise 3D coordinates with respect to a global reference system. The redundancy provided by the fourth receiver allows to improve estimations even more and to maintain accuracy when one of the receivers fails. A mini computer with the Robotic Operating System is responsible for merging all the available measurements reliably. Successful experiments have been carried out with a ground rover on irregular terrain. Angular estimates similar to those of a high-performance IMU have been achieved in dynamic tests.

3D Point Clouds Data Super Resolution-Aided LiDAR Odometry for Vehicular Positioning in Urban Canyons

LiDAR odometry algorithms are extensively studied for vehicular positioning. However, achieving high-precision positioning using low-cost 16-channel LiDAR in urban canyons remains a challenging problem due to the limited point cloud density from low-cost LiDAR and excessive dynamic surrounding objects. To fill this gap, this paper proposes enriching sparse 3D point clouds to denser clouds via a novel deep learning-based superresolution (SR) algorithm before its utilization in 3D LiDAR odometry. We validate the effectiveness of the proposed method using the KITTI dataset and a challenging dataset collected in an urban canyon (with complex environmental structures and dynamic objects) of Hong Kong. We conclude that significantly denser point clouds are achieved with considerable accuracy. In addition, significantly improved performance of 3D LiDAR odometry is obtained in the evaluated dataset collected in an urban canyon of Hong Kong.

V2X-Based Vehicular Positioning: Opportunities, Challenges, and Future Directions

Vehicle-to-Everything (V2X) creates many new opportunities in the area of wireless communications, while its feasibility on enabling vehicular positioning (VP) has not been fully explored despite its importance for autonomous driving. This article aims at investigating whether V2X can help VP from different perspectives. We first explain V2X's critical advantages over other technologies (e.g., GPS, RADAR, LIDAR, and camera) and suggest new scenarios of V2X-based VP. Then, we review the state-of-the-art positioning techniques discussed in different standardizations and point out their limitations. Lastly, some promising research directions for V2X-based VP are presented, which contribute to realizing fully autonomous driving by overcoming the current obstacles.

A Highly Accurate Positioning Solution for C-V2X Systems.

Cellular vehicle-to-everything (C-V2X) is essential in enabling safe, reliable, and efficient transportation services. It serves as serve as the foundation for vehicles to communicate with each other and everything around them. One fundamental element in C-V2X is positioning, namely extracting the vehicle’s absolute and relative positions concerning other objects such as buildings, pedestrians, traffic signs, and other vehicles. However, its feasibility in enabling vehicular positioning has not been fully explored yet. In this paper, key performance indicators (KPIs) for C-V2X positioning have been described firstly. Then positioning challenges and conventional positioning methods for C-V2X are reviewed. Afterward, two user equipment (UE)-based and UE-assisted C-V2X positioning architectures are proposed, and key technologies are also described. Lastly, testing and typical application cases are provided.

A Tightly Coupled BDS/INS Integrated Positioning Algorithm Based on Triple-Frequency Single-Epoch Observations

Vehicular dynamic positioning based on tightly coupled (TC) Global Navigation Satellite System (GNSS)/Inertial Navigation System (INS) integration in urban areas is due to either low accuracy of pseudorange or poor continuity of carrier phase, resulting in insufficient positioning performance. To enhance the stability while ensuring positioning accuracy, this paper proposed a tightly coupled Beidou Navigation Satellite System (BDS)/INS integration scheme by improving measurement modelling with triple-frequency observations: first, a stepwise single-epoch ambiguity resolution of extra-wide-lane (EWL)/wide-lane (WL) combined observations and then modelling the measurement equation with fixed WL observation instead of conventional pseudorange or carrier phase. Experiments were carried out for verification with data collected in real traffic by a measurement vehicle. The proposed method achieved single-epoch output with an RMS statistical accuracy of decimetre level of 0.152 m horizontally and 0.196 m vertically. The signal outage experiment verified that the proposed algorithm is restoring high-accuracy positioning output in single-epoch once the signal is recaptured. The proposed method obtained a positioning accuracy improvement of 43.6% horizontally and 6.2% vertically in signal outage sections compared to the conventional method. This avoids the multiepoch ambiguity searching to fix with conventional carrier-phase processing, thereby improving the positioning stability.