Vehicle Position Information Research Articles

Imaging VOC distribution in cities and tracing VOC emission sources with a novel mobile proton transfer reaction mass spectrometer

Volatile organic compounds (VOCs) are important precursors of ozone (O3) and secondary organic aerosols (SOAs). Tracing VOC pollution sources is important for controlling VOC emissions and reducing O3 and SOAs. We built a novel mobile proton transfer reaction mass spectrometry (M-PTR-MS) instrument to image the distribution of VOCs and trace their emission sources in cities and industrial parks. The M-PTR-MS is composed of a vibration-resistant proton transfer reaction mass spectrometry (PTR-MS) with a global positioning system receiver, modified box vehicle, and geographic information system (GIS) software. The PTR-MS, mounted on a vehicle, sends VOC data and vehicle position information to the GIS software. These data are used to image the space distribution of VOCs in real time while the vehicle platform is in motion and the VOC sources are precisely traced using the GIS. The spatial data resolution of the M-PTR-MS is typically 0.8 m. The limits of detection, sensitivity, and repeatability of the M-PTR-MS are 43.5 ppt, 347 counts ppb−1, and 2.4% (RSD, n = 5), respectively. The intensity of reagent ions is stable over 8 h (RSD = 0.45%). Compared with commercial PTR-MS equipment, the M-PTR-MS demonstrated high consistency, with a correlation coefficient of 92.665%. Several field experiments were conducted in China using the M-PTR-MS. In one field experiment, the VOC distribution along three different routes was surveyed; the navigation monitoring lasted 1.8 h over a distance of 26.7 km at an average speed of 15 km h−1. The VOC sources in an industrial park were identified by analyzing the components near different factories. The main species from a VOC source in an underground garage was related to paint. The M-PTR-MS instrument can be used by environmental protection agencies to trace VOC pollution sources in real time, and by researchers to survey VOC emissions in regions of concern.

RETRACTED ARTICLE: Design of traffic object recognition system based on machine learning

In recent years, researchers have proposed many methods to solve the problem of obstacle detection. However, computer vision-based vehicle detection and recognition technology is still not mature enough. This research combines machine learning technology to construct a traffic object recognition system and applies innovative technology to the computer vision recognition system to construct an automatic identification system suitable for current traffic demand and improve the stability of the traffic system. Moreover, this study uses a combination of a monocular camera and a binocular camera to sense the traffic environment and obtain vehicle position and velocity information. In addition, this study is based on the binocular stereo camera to find the obstacle space and obtain the obstacle relative to the position and speed of the vehicle and combine the obstacle space information to optimize the obstacle frame of the target vehicle. Through experimental research and analysis, it can be seen that the algorithm proposed in this study has certain recognition effect and can be applied to traffic object recognition.

Real-Time Vehicle Detection Framework Based on the Fusion of LiDAR and Camera

Vehicle detection is essential for driverless systems. However, the current single sensor detection mode is no longer sufficient in complex and changing traffic environments. Therefore, this paper combines camera and light detection and ranging (LiDAR) to build a vehicle-detection framework that has the characteristics of multi adaptability, high real-time capacity, and robustness. First, a multi-adaptive high-precision depth-completion method was proposed to convert the 2D LiDAR sparse depth map into a dense depth map, so that the two sensors are aligned with each other at the data level. Then, the You Only Look Once Version 3 (YOLOv3) real-time object detection model was used to detect the color image and the dense depth map. Finally, a decision-level fusion method based on bounding box fusion and improved Dempster–Shafer (D–S) evidence theory was proposed to merge the two results of the previous step and obtain the final vehicle position and distance information, which not only improves the detection accuracy but also improves the robustness of the whole framework. We evaluated our method using the KITTI dataset and the Waymo Open Dataset, and the results show the effectiveness of the proposed depth completion method and multi-sensor fusion strategy.

Research and application of Netty based condition monitoring system for forage harvesting machine

In order to realize intelligent monitoring of main components of forage harvesting machine, an intelligent remote monitoring system based on CAN bus technology, Socket communication protocol based on Netty framework and cloud server was designed to solve the problem of low level of informatization of forage harvesting machine in China. system includes data acquisition system, data wireless transmission module and cloud data storage and analysis and processing system, realizing the data synchronization and sharing of working condition information of forage harvesting machine at local, cloud and mobile terminals. We developed the on-board terminal system based on Labview platform, and the web server based on SpringBoot framework. The parameters obtained by the sensor mainly include vehicle position information, engine speed and torque, shredding roller, grain crushing roller, cutting table, feeding, throwing, motor and other core components. Field experiments for 15 consecutive days show that: cloud server system with custom Socket communication protocol receives and stores terminal data in real time; The monitoring platform can realize the functions of data display and curve display of working condition information, data analysis and intelligent information reminder. By analyzing the data curve of the main components, the stability and reliability of the system are verified, which is of great significance to the informationization and intelligent development of the machinery and equipment of the forage harvesting machine.

INS/GNSS integration using recurrent fuzzy wavelet neural networks

In recent years, aided navigation systems through combining inertial navigation system (INS) with global navigation satellite system (GNSS) have been widely applied to enhance the position, velocity, and attitude information of autonomous vehicles. In order to gain the accuracy of the aided INS/GNSS in GNSS gap intervals, a heuristic neural network structure based on the recurrent fuzzy wavelet neural network (RFWNN) is applicable for INS velocity and position error compensation purpose. During frequent access to GNSS data, the RFWNN should be trained as a highly precise prediction model equipped with the Kalman filter algorithm. Therefore, the INS velocity and position error data are obtainable along with the lost intervals of GNSS signals. For performance assessment of the proposed RFWNN-aided INS/GNSS, real flight test data of a small commercial unmanned aerial vehicle (UAV) were conducted. A comparison of test results shows that the proposed NN algorithm could efficiently provide high-accuracy corrections on the INS velocity and position information during GNSS outages.

Location Prediction Model Based on the Internet of Vehicles for Assistance to Medical Vehicles

Along with the Internet of Vehicles, some intelligent systems can help the medical vehicles transport medical supplies and patients. In terms of emergency issues like catastrophic natural disasters or serious accidents, safe and timely transportation for medical vehicles is particularly important. For assistance to medical vehicles on the road, models with position prediction can provide accurate position information of ambient vehicles in the next seconds. However, with the increasing number of vehicles on the road and the changing road environment, it is an important challenge to predict the location of vehicles in the road correctly. Current location prediction models for vehicles usually use the previous trajectory of vehicles, lacking the consideration of vehicle’s state and real-time traffic information, which leads to relatively low accuracy and safety. Based on the deep belief nets (DBN) and long short-term memory (LSTM), this paper presents a location prediction model for assistance to medical vehicles (LPMVs), which fully considers vehicle’s attributes, road information and driving environment as well as the relationship between the factors that influence vehicle driving behaviors and vehicle positions. By experiment, we prove that LPMVs can predict more accurately than current location prediction models and be a good choice for assistance to medical vehicles.

An Improved Authentication Scheme for Internet of Vehicles Based on Blockchain Technology

Thanks to the rapid development in mobile vehicles and wireless technologies, the Internet of Vehicles (IoV) has become an attractive application that can provide a large number of mobile services for drivers. Vehicles can be informed of the mobile position, direction, speed, and other real-time information of nearby vehicles to avoid traffic jams and accidents. However, the environments of IoV could be dangerous in the absence of security protections. Due to the openness and self-organization of IoV, there are enormous malicious attackers. To guarantee the safety of mobile services, we propose an effective decentralized authentication mechanism for IoV on the basis of the consensus algorithm of blockchain technology. The simulation under the veins framework is carried out to verify the feasibility of the scheme in reducing the selfish behavior and malicious attacks in IoV.

A 3D LiDAR Data-Based Dedicated Road Boundary Detection Algorithm for Autonomous Vehicles

Effectively detecting road boundaries in real time is critical to the applications of autonomous vehicles, such as vehicle localization, path planning, and environmental understanding. To precisely extract the irregular road boundaries or those blocked by obstructions on the road from the 3D LiDAR data, a dedicated algorithm consisting of four steps is proposed in this paper. The steps are as follows. First, the 3D LiDAR data is pre-processed, employing the vehicle position and attitude information, and many noise points are deleted. Second, the ground points are quickly separated from the pre-processed point cloud data to reduce the disturbance from the obstacles on the road; this greatly decreases the size of the points cloud to be processed. Third, the candidate points of the road boundaries are searched along the predicted trajectory of the autonomous vehicle and filtered using the unique features of the boundary points. Last, a spline fit model is applied to smoothen the road boundaries. An experiment to test the performance of the proposed algorithm was conducted on the “Xinda” autonomous vehicle under various road scenarios. The experimental results show that the average accuracy of the proposed algorithm exceeds 93%, and its average processing time is approximately 36.5 ms/frame, which outperforms most of the state-of-the-art methods. This indicates that the proposed algorithm can robustly extract the road boundary in real time even if there are many obstacles on the road. This algorithm has been tested on “Xinda” autonomous vehicle for over 1000 kilometers, and its performance was always stable.

Design and Vision Based Autonomous Capture of Sea Organism With Absorptive Type Remotely Operated Vehicle

Robot machine capture protein seafood like sea cucumber and seashell is expected to play a great role in food economy improvement and divers protection. In order to realize robot capture, a sea organism absorptive type remotely operated vehicle (ROV) has been designed with the pilot operation and visionbased autonomous capture modes. A novel region-based fully convolutional network with deformable convolutional networks has been developed to realize organism target recognition. The comparisons in off-line experiments have verified its advantages. In order to realize organism target following and capture control, a novel learning-based type-II fuzzy controller has been developed. Through online fuzzy rule optimization and learning, the controller can realize organism target following and capture control under image coordinate without vehicle horizontal velocity or position information in the complicated submarine environment. Field trials have been made in the Zhangzidao Island of China with the designed absorptive type ROV. The trials manifest that the designed absorptive type ROV can realize online organism target recognition, following and capture in the real submarine environment.

EVP-STC: Emergency Vehicle Priority and Self-Organising Traffic Control at Intersections Using Internet-of-Things Platform

This paper presents an Internet-of-Things-based platform for emergency vehicle priority and self-organised traffic control (EVP-STC) management at intersections. With the increasing number of automobiles, traffic jams in urban areas are becoming a critical issue. Traffic jams, especially those at intersections, not only increase delays for drivers but also increase fuel consumption and air pollution. We propose a novel platform and protocol called EVP-STC that contains three main systems. The first system, called the intersection controller, is installed at traffic lights and collects emergency vehicle position information and vehicle density data at each road segment approaching an intersection. The intersection controller then adjusts the timings of traffic lights based on detected real-time traffic. The second system is installed at each road segment and contains force resistive sensors to detect vehicles. It transmits the detected information to the intersection controller via ZigBee. The third system is installed in emergency vehicles and provides GPS coordinates to the intersection controller to avoid any waiting time for emergency vehicles at intersections. Simulation results demonstrate the effectiveness of the proposed platform, which minimises total delays, lane opening times, and waiting times for emergency vehicles.

A cooperative vehicle-infrastructure based urban driving environment perception method using a D-S theory-based credibility map

Driving environment perception technology is a key issue for driving assistance systems and autonomous vehicles. Driving environment information collection and information fusion and representation are two important contents of this technology. On-board sensors such as camera, radar, lidar, etc., are popularly used to collect relevant information. However, when using these on-board sensors, uncertainty arises from ignorance and errors, where ignorance is usually caused by vehicle occlusion, and errors is caused by imprecise pose estimation and noisy measurements. Increasing sensor number would decrease such uncertainty, but complexity and cost is also increased in this way. In this paper, we present a V2I (vehicle-infrastructure) based urban driving environment collection method achieved only by two sensors, i.e., a Road Side Unit camera (RSU camera) and vehicle GPS. RSU camera is used to extract lane marking information, including position and type, and vehicle position information. Vehicle GPS provide position information of vehicle. A credibility map is proposed as the compact representation of urban environment, and accept those information from RSU camera and vehicle GPS. In order to hand uncertainty caused by sensors, Dempster-Shafer theory is employed to estimate the credibility of each cell, fuse the credibility root in the information provided by RSU camera and vehicle GPS, and map onto the credibility map. The performance of this method has been validated with real-world urban traffic data.

Predictor-based neural dynamic surface control for distributed formation tracking of multiple marine surface vehicles with improved transient performance

In this paper, we investigate the distributed formation tracking problem of multiple marine surface vehicles with model uncertainty and time-varying ocean disturbances induced by wind, waves, and ocean currents. The objective is to achieve a collective tracking with a time-varying trajectory, which can only be accessed by a fraction of follower vehicles. A novel predictor-based neural dynamic surface control design approach is proposed to develop the distributed adaptive formation controllers. We use prediction errors, rather than tracking errors, to construct the neural adaptive laws, which enable the fast identification of the vehicle dynamics without incurring high-frequency oscillations in control signals. We establish the stability properties of the closed-loop network via Lyapunov analysis, and quantify the transient performance by deriving the truncated $L_2$ norms of the derivatives of neural weights, which we demonstrate to be smaller than the classical neural dynamic surface control design approach. We also extend the above result to the distributed formation tracking using the relative position information of vehicles, and the advantage is that the velocity information of neighbors and leader are required. Finally, we give the comparative studies to illustrate the performance improvement of the proposed method.

Context-aware GPS integrity monitoring for intelligent transport systems

The integrity of positioning systems has become an increasingly important requirement for location-based intelligent transport systems (ITS), for example electronic toll collection (ETC), public transport operations and traffic control services. In ITS, satellite navigation systems, such as global positioning system (GPS), are used to provide real-time vehicle positioning information including details of longitude, latitude, direction and speed. Map matching algorithms are used to integrate the positioning information into the digital road map. However, the navigation systems used in ITS cannot provide the high quality positioning information required by most services, due to the various types of errors made in the map matching process and experienced by GPS sensors such as signal outage, and errors due to atmospheric effects and receiver measurement errors, all of which are difficult to measure. An error in the positioning information or map matching process might lead to the inaccurate determination of a vehicle location. This could have legal or economic consequences for ITS applications such as traffic law enforcement systems (e.g., speed fining). Such applications require integrity when measuring the vehicle position and speed information and in the map matching process when locating the vehicle on the correct road segment to avoid errors when charging drivers. Consequently, the integrity algorithm for the navigation system should include a guarantee that the systems do not produce misleading or faulty information as this may lead to significant errors in the ITS services provided. In this paper, a high integrity GPS monitoring algorithm based on the concept of context-awareness that can be applied with real time ITS services to integrate changes in the integrity status of the navigation system was developed. Results suggest that the new integrity algorithm can support various types of location-based ITS services (e.g., route guidance).

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.

공심형 선형동기전동기 기반의 궤도열차 추진제어에 관한 연구

Abstract To accelerate a heavy roller coaster train with over 1G force, a lot of thrust is required and linear synchronous motor(LSM) as propulsion method is suitable for this kind of system. To increase the propulsion efficiency of LSM, precise and real-time position information of vehicle is required for accurate phase control. However, the discontinuous position information with relatively long time interval is usually transmitted from the hall-sensors on the track every magnet length. In this paper, the basic motor model based on traditional dq-axis equations is described and the motor dynamic modelis produced by considering the cogging force and friction loss. To improve the position accuracy, the position estimator is also proposed for LSM control system. Simulations were performed to check the characteristics of thetorque control system which includes the position estimator based on the motor model. Simulation results based on the linearized model show that this control system has an enough bandwidth and phase margin and the executed algorithm achieves an ideal effect to follow the real-time position signal. Therefore, the feasibility of position estimator is also confirmed.

Geographic scheduling of directional transmission for periodic safety messages in IEEE WAVE

It is typically assumed that safety communication will use omnidirectional broadcast in vehicle-to-vehicle environments. However, in the IEEE Wireless Access in Vehicular Environment standard, some safety application messages will have directional semantics. For example, messages in the cooperative adaptive cruise control application are relevant to vehicles following the transmitter. In this paper, we investigate the impacts of using directional communication that is tailored to such applications. In particular, we show that the directional transmission indeed improves the message delivery probability by reducing message spillover in unnecessary directions. However, we also find that such benefit is not automatically obtained. Analysis reveals that it is crucial that the temporal ordering of the transmission is aligned with the vehicles’ spatial ordering to minimize the hidden terminal losses and fully garner the benefits of directional communication. For a method to ensure that the alignment takes place, we propose an application-level message scheduling solution that utilizes vehicle position information, and demonstrate that it leads to both lower channel utilization and higher message delivery rates than omnidirectional transmission.

A novel fusion methodology to bridge GPS outages for land vehicle positioning

Many intelligent transportation system applications require accurate, reliable, and continuous vehicle position information whether in open-sky environments or in Global Positioning System (GPS) denied environments. However, there remains a challenging task for land vehicles to achieve such positioning performance using low-cost sensors, especially microelectromechanical system (MEMS) sensors. In this paper, a novel and cost-effective fusion methodology to bridge GPS outages is proposed and applied in the Inertial Navigation System (INS)/GPS/ compass integrated positioning system. In the implementation of the proposed methodology, a key data preprocessing algorithm is first developed to eliminate the noise in inertial sensors in order to provide more accurate information for subsequent modeling. Then, a novel hybrid strategy incorporating the designed autoregressive model (AR model)-based forward estimator (ARFE) with Kalman filter (KF) is presented to predict the INS position errors during GPS outages. To verify the feasibility and effectiveness of the proposed methodology, real road tests with various scenarios were performed. The proposed methodology illustrates significant improvement in positioning accuracy during GPS outages.

Relative Position Dynamic Map for Driver Assistance Systems in Vehicular Networks

Many traffic accidents happen because the driver misjudges the relative distance between vehicles. Active safety technologies and passive warning functions have been proposed for driver assistance systems in vehicular networks. Radar and cameras are sensors commonly used in active safety technologies. The global positioning system and dedicated short-range communication are suggested for the passive warning functions. However, the accuracy of position information is a challenge which can decrease the effectiveness of safety applications in driver assistance systems. This paper proposes a relative position dynamic map with a radar sensor for driver assistance systems in vehicular networks. The proposed method relies on the relative distance measured by the radar sensor to determine the position information of neighbouring vehicles by means of dedicated short-range communication and constructs a relative position dynamic map for the driver. The experimental results show that the proposed method should provide better performance than traditional global positioning system information.

An Improved Path Tracking Algorithm for Intelligent Vehicle in Automatic Parking Conditions

A feedforward and feedback path tracking algorithm appropriate for automatic parking is proposed in this paper. This algorithm is composed of feedforward control and feedback control. In the path tracking, vehicle position and reference path information are used to calculate the lateral and heading error of the vehicle as well as the curvature of reference path, and put these three as the system inputs, the steering angel of the vehicle as the output. The system parameters are settled by multiple tests. Experiments show that, this method has a high performance in path tracking and meets the requirements of vehicle parking in both low speed situation and narrow spaces.

Design of Communication Module Based on GPS/GPRS

With the number of the city vehicle increasing, traffic congestion has become a big problem. Thus the demand for vehicle accurate monitoring and scheduling is increasingly urgent. A wireless communication module based on GPS/GPRS has been designed in this paper, which is used to transmit the vehicle position information and other related data to the server wirelessly by GPRS. The STM8S207 microcontroller produced by the STMicroelectronics is used as the controller which is adopted as the communication module. The NEO-6M module and the wireless module MG323 with embedded TCP/UDP protocol are combined to acquire and monitor the vehicle data. The communication module designed in this paper can ensure the accuracy of GPS data and the real-time of GPRS transmission.