Vehicle Position Data Research Articles

Adaptive Frame Structure Design for Sensing-Assisted Downlink Communication in the Vehicle-to-Infrastructure Scenario.

Vehicle-to-everything (V2X) is considered a key factor in driving the future development of intelligent transport, which requires high-quality communication and fast sensing of vehicle information in high-speed mobile scenarios. However, high-speed mobility makes the wireless channel change rapidly, which requires frequent channel estimation and channel feedback between a vehicle and the roadside unit (RSU), resulting in an increase in communication overhead. At the same time, the high maneuverability of vehicles leads to frequent switching and misalignment of communication beams, so the RSU must have better beam prediction and tracking capabilities. To address this problem, this paper proposes an adaptive frame structure design scheme for sensing-assisted downlink (DL) communication. The basic idea of the scheme involves analyzing the communication model during the vehicle's movement. This analysis aims to establish a theoretical relationship between the Symbol Error Rate (SER) and the following two key factors: the vehicle's starting position and the distance it travels across. Subsequently, the scheme leverages the vehicle's position data, as detected by the RSU, to calculate the real-time SER for DL communication. The SER threshold is set based on the requirements of DL communication. If the real-time SER is below this threshold, channel estimation becomes unnecessary. This decreases the frequency of channel estimation and frees up time and frequency resources that would otherwise be occupied by channel estimation processes within the frame structure. The design of an adaptive frame structure, as detailed in the above scheme, is presented. Furthermore, the performance of the proposed method is analyzed and compared with that of the traditional communication protocol frame structure and the beam prediction-based frame structure. The simulation results indicate that the communication throughput of the proposed method can be improved by up to 6% compared with the traditional communication protocol frame structure while maintaining SER performance.

Side Collision Detection Model for Visually Impaired Using Monocular Object-specific Distance Estimation and Multimodal Real-World Location Calculation

Targeting the potential risk of side-vehicle collisions when the visually impaired crosses roads, this study proposed a side collision detection model, including monocular distance estimation, multimodal real-world location estimation, future location prediction and collision warning strategies tailored for visually impaired pedestrians. The proposed model employs YOLOv8 and DeepSort for vehicle detection and tracking, utilizing shallow neural networks for distance estimation based on image information and vehicle position data. Predicted vehicle distances are combined with magnetic field sensor and GPS data to compute and store real-world vehicle locations, and these location data will be used for linear regression to forecast future locations. A warning strategy is then implemented to alert users. Experimental validation shows that the monocular distance estimation network has an Absolute Relative Error of 0.043 and an ALE (Average Localization Error) of 1.249m. In real-world location estimation, the view angle ALE is 0.019, and the location ALE is 1.778m. Regarding location prediction, the accuracy in distinguishing stationary and moving vehicles reaches 0.962, and the predicted curve, based on ground truth and predicted locations, exhibits good alignment. The proposed warning strategy, evaluated on Kitti Tracking Dataset and a self-created dataset, accurately detects the majority of potential collision risks.

Real-time numerical differentiation of sampled data using adaptive input and state estimation

Real-time numerical differentiation plays a crucial role in many digital control algorithms, such as PID control, which requires numerical differentiation to implement derivative action. This paper proposes an algorithm for estimating the numerical derivative of a signal from noisy sampled data measurements. The method uses adaptive input estimation with adaptive state estimation (AIE/ASE), and thus it requires only minimal prior information about the signal and noise statistics. Furthermore, since the estimates of the derivative at step k provided by AIE/ASE depend only on data available up to step k, AIE/ASE is thus implementable in real time. The accuracy of AIE/ASE is compared numerically to several conventional numerical differentiation methods. Finally, AIE/ASE is applied to simulated vehicle position data, generated in the CarSim simulator software.

High-resolution global geopositioning system for last-mile delivery

High-resolution monitoring systems are commonly used to calculate mechanical and energy performance, as well as environmental impact resulting from the operation of a fleet of vehicles in a region. These systems require recording operational or vehicle position data, energy consumption in the case of electric or electric-assisted vehicles, and buttons for specific signals in the operation, such as the start of operation, end of operation, or package delivery. Some vehicle tracking devices available on the market do not allow speed and energy consumption data to be recorded and accessed at the required sampling frequency. This study presents the development and validation of a remote sensing device to record energy consumption and operational variables of a vehicle under real operating conditions with a frequency of 1 Hz. The functioning of the device, the integrated elements in its development, and the data analysis process are detailed. Finally, using the monitoring equipment data, a comparison was made between the operation of electric vehicles and internal combustion vehicles, achieving savings of up to 83% in operating costs and a 79% reduction in CO2 emissions.

AI-Empowered Speed Extraction via Port-Like Videos for Vehicular Trajectory Analysis

Automated container terminal (ACT) is considered as port industry development direction, and accurate kinematic data (speed, volume, etc.) is essential for enhancing ACT operation efficiency and safety. Port surveillance videos provide much useful spatial-temporal information with advantages of easy obtainable, large spatial coverage, etc. In that way, it is of great importance to analyze automated guided vehicle (AGV) trajectory movement from port surveillance videos. Motivated by the newly emerging computer vision and artificial intelligence (AI) techniques, we propose an ensemble framework for extracting vehicle speeds from port-like surveillance videos for the purpose of analyzing AGV moving trajectory. Firstly, the framework exploits vehicle position in each image via a feature-enhanced scale-aware descriptor. Secondly, we match vehicle position and trajectory data from the previous step output via Kalman filter and Hungarian algorithm, and thus we obtain the vehicular imaging trajectory in a frame-by-frame manner. Thirdly, we estimate the vehicular moving speed in real-world via the help of perspective projection theory. The experimental results suggest that our proposed framework can obtain accurate vehicle kinematic data under typical port traffic scenarios considering that the average measurement error of root mean square deviation is 0.675 km/h, the mean absolute deviation is 0.542 km/h, and the Pearson correlation coefficient is 0.9349. The research findings suggest that cutting-edge AI and computer vision techniques can accurately extract on-site vehicular trajectory related data from port videos, and thus help port traffic participants make more reasonable management decisions.

Deep ocean long range underwater navigation with ocean circulation model corrections.

An underwater navigation algorithm that provides a "cold start" (CSA) geographic position, geo-position, underwater while submerged using travel times measured from a constellation of acoustic sources is described in Mikhalevsky, Sperry, Woolfe, Dzieciuch, and Worcester [J. Acoust. Soc. Am. 147(4), 2365 - 2382 (2020)]. The CSA geo-position is used as the receive position in the ocean for acoustic modeling runs using an ocean general circulation model (GCM). A different geo-position is calculated using adjusted ranges from the travel time offsets between the data and modeled arrival times for each source. Because the CSA geo-position is close to the true position, the source to CSA position propagation model path and the source to true vehicle position data path of the acoustic arrivals are nearly coincident, enabling accurate measurement of travel time offsets. The cold start with model (CSAM) processing reduced the CSA geo-position errors from a mean of 58 to 25 m. A simulation is developed to estimate CSA and CSAM performances as a function of group speed variability between the source paths. The CSAM geolocation accuracy can be calculated from and is controlled by the accuracy of the GCM.

Infrastructure camera calibration with GNSS for vehicle localisation

Intelligent transportation and smart city applications are currently on the rise. In many applications, diverse and accurate sensor perception of vehicles is crucial. Relevant information could be conveniently acquired with traffic cameras, as there is an abundance of cameras in cities. However, cameras have to be calibrated in order to acquire position data of vehicles. This paper proposes a novel automated calibration approach for partially connected vehicle environments. The approach utilises Global Navigation Satellite System positioning information shared by connected vehicles. Corresponding vehicle Global Navigation Satellite System locations and image coordinates are utilised to fit a direct transformation between image and ground plane coordinates. The proposed approach was validated with a research vehicle equipped with a Real-Time Kinematic-corrected Global Navigation Satellite System receiver driving past three different cameras. On average, the camera estimates contained errors ranging from 1.5 to 2.0 m, when compared to the Global Navigation Satellite System positions of the vehicle. Considering the vast lengths of the overlooked road sections, up to 140 m, the accuracy of the camera-based localisation should be adequate for a number of intelligent transportation applications. In future, the calibration approach should be evaluated with fusion of stand-alone Global Navigation Satellite System positioning and inertial measurements, to validate the calibration methodology with more common vehicle sensor equipment.

Analysis on the Effect of Intelligentization and Improvement of Tourist Bus Loops Based on Markov Chain Decoupling

Based on vehicle positioning and remote data transmission technology, a quantitative analysis method for the impact of intelligent tour bus loops on the choice of transportation means between tourist attractions is developed. Aiming at the problem of undirected graph path planning where the influence of time series is known and the edge weights are known, Markov chains are introduced on the basis of the “0-1-like planning under time series” model, and a Markov chain-based algorithm is established. In this article, we proposed a model for route planning of a tourist bus. When solving, it is assumed that the weight of the edge changes with time, and random variables are introduced, but the past state will not affect the current state, and other conditions remain unchanged. After the constraint model is established, it is simulated by computer and solved using the stochastic gradient descent algorithm. The model makes the tour bus loop intelligent and has good interpretability and robustness.

Space Division Multiple Access for Cellular V2X Communications

Vehicular communication is the backbone of future Intelligent Transportation Systems (ITS). It offers a network-based solution for vehicle safety, cooperative awareness, and traffic management applications. For safety applications, Basic Safety Messages (BSM) containing mobility information is shared by the vehicles in their neighborhood to continuously monitor other nearby vehicles and prepare a local traffic map. BSMs are shared using mode 4 of Cellular V2X (C-V2X) communications in which resources are allocated in an ad hoc manner. However, the strict packet transmission requirements of BSM and hidden node problem causes packet collisions in a vehicular network, thus reducing the reliability of safety applications. Moreover, as vehicles choose the transmission resources in a distributed manner in mode 4 of C-V2X, the packet collision problem is further aggravated. This paper presents a novel solution in the form of a Space Division Multiple Access (SDMA) protocol that intelligently schedules BSM transmissions using vehicle position data to reduce concurrent transmissions from hidden node interferers. The proposed protocol works by dividing road segments into clusters and sub-clusters. Several sub-frames are allocated to a cluster and these sub-frames are reused after a certain distance. Within a cluster, sub-channels are allocated to sub-clusters. We implement the proposed SDMA protocol and evaluate its performance in a highway vehicular network. Simulation results show that the proposed SDMA protocol outperforms standard Sensing-Based Semi Persistent Scheduling (SB-SPS) in terms of safety range and packet delay.

Big data analytics in smart cities’ transportation infrastructure modernization

Using big data in supply chain management (SCM) has the potential to have a significant impact on the industry in general and international transportation in particular. Big data have a direct influence on transportation capacity in future cities. There has been a significant increase in urbanization over the last decade in which one in three people will live in an urban area by 2050. An updated transportation infrastructure is essential to keep up with the present flow of goods, while also limiting its impact on the environment and human health and this is likely to be achieved using big data analytics technique.To overcome this problem, smart cities are becoming more popular. With the use of information and communication technology (ICT), a smart city aims to address public concerns in an inclusive, municipally-based partnership. A big data transportations system may be built using the superstructure of a smart city. A good way to define it is the modeling and analysis of urban transportation and distribution networks using enormous data sets created by GPS, mobile phones, and transactional data from company activities.Big Data analytics may be used in public transportation to better understand how people go about the city. A better understanding of passengers’ travel patterns might help transportation providers make better judgments regarding service quality. People who travel by automobile on a regular basis may now be predicted based on the triangulation of mobile phone data from millions of anonymous users. Local and national polls may demonstrate the paradigm’s applicability. To compute the time it takes for passengers to board and exit trains, Metro and iBus vehicle position data may be combined with information from smart cards. Big Data analytics for traffic management may benefit from these findings.

A prediction method of missing vehicle position information based on least square support vector machine

The continuous development of VANET has accelerated the development of V2X communication. In the DSRC communication mode of VANET, the location information of the vehicles is interfered by factors such as high-density broadcasting and electromagnetic radiation, which can lead to the loss of the original vehicle information data collected by GPS easily. To solve it, this paper proposed the Least Squared SVM based Beacon Data Complete Algorithm. Unlike previous studies that historical trends of vehicle operation were mainly used to predict vehicle location., this method attempts to find a function, which is used to establish the relationship between the lost value and the past value of the vehicle. On this basis, a nonlinear function approximation strategy is used to predict the position of the missing vehicle. Part of the original data was lost artificially to complete checking calculation and to verify the effectiveness of it. The results show that the average relative error between the complemented vehicle position data and the real data is 0.45% and the maximum absolute relative error is 8.25%. This method has the advantage of not needing to extract historical trend data and high calculation accuracy compared with the methods such as PWHOG algorithm, difference matrix, and moving average data preprocessing. It is suitable for real-time acquisition of vehicle position of VANET and can reduce the complexity of detection time.

Intersection and Stop Bar Position Extraction From Vehicle Positioning Data

Intersection and Stop Bar Position Extraction From Vehicle Positioning Data

Implementasi Aplikasi Berbasis Teknologi IoT pada Perangkat Tracking dan Kendali Kendaraan Bermotor

The increasing use of vehicles, especially motorbikes, has the potential to increase motor vehicle theft cases. The increase in motorcycle theft cases can occur due to the weakness of the owner in installing a motorcycle security system, so that it has the potential to experience losses due to theft. An indication of the problem is how to develop an application that can be used to carry out tracking that can display the position of the vehicle with integrated maps and vehicle control remotely in the event of a vehicle theft. The development of Internet of Things (IoT) technology as well as information and communication technology can now be used to develop an application for wireless vehicle monitoring and control. So that it can make it easier for vehicle owners to track and control the vehicle to minimize vehicle loss. In this study using MIT App Inventor as an application development platform and using ThingSpeak as an IoT database server. Furthermore, the application will be integrated with the Tracking Device device consisting of a microcontroller, Global Positioning System (GPS) module, and a DC relay installed on the vehicle, utilizing an internet connection as a link between the application and the tracking device. The result is that vehicle position data can be displayed through a mobile application with map visualization integrated with google maps and the user can turn off or turn on the vehicle through the application with the farthest distance that has been successfully tested of 141 km. The time for the tracking device to respond to commands from the application varies from 5 to 20 seconds depending on the strength of the internet connection used.
 Keywords : Internet of Things (IoT), Tracking Device, Global Positioning System (GPS), mikrokontroler.

MOVING OBJECT CLASSIFICATION USING MULTILAYER LASER SCANNING WITH SPACE SUBDIVISION FRAMEWORK

Abstract. In this paper, we focus on the development of intelligent construction vehicles to improve the safety of workers in construction sites. Generally, global navigation satellite system positioning is utilized to obtain the position data of workers and construction vehicles. However, construction fields in urban areas have poor satellite positioning environments. Therefore, we have developed a 3D sensing unit mounted on a construction vehicle for worker position data acquisition. The unit mainly consists of a multilayer laser scanner. We propose a real-time object measurement, classification and tracking methodology with the multilayer laser scanner. We also propose a methodology to estimate and visualize object behaviors with a spatial model based on a space subdivision framework consisting of agents, activities, resources, and modifiers. We applied the space subdivision framework with a geofencing approach using real-time object classification and tracking results estimated from temporal point clouds. Our methodology was evaluated using temporal point clouds acquired from a construction vehicle in drilling works.

Modelling the travel time of transit vehicles in real‐time through a GTFS‐based road network using GPS vehicle locations

SummaryPredicting the arrival time of a transit vehicle involves not only knowledge of its current position and schedule adherence, but also traffic conditions along the remainder of the route. Road networks are dynamic and can quickly change from free‐flowing to highly congested, which impacts the arrival time of transit vehicles, particularly buses which often share the road with other vehicles, so reliable predictions need to account for real‐time and future traffic conditions. The first step in this process is to construct a framework with which road state (traffic conditions) can be estimated using real‐time transit vehicle position data. Our proposed framework implements a vehicle model using a particle filter to estimate road travel times, which are used in a second model to estimate real‐time traffic conditions. Although development and testing took place in Auckland, New Zealand, we generalised each component to make the framework compatible with other public transport systems around the world. We demonstrate the real‐time feasibility and performance of our approach in real‐time, where a combination of R and C++ was used to obtain the necessary performance results. Future work will use these estimated traffic conditions in combination with historical data to obtain reliable arrival time predictions of transit vehicles.

DESIGN AND DEVELOPMENT OF MONITORING SYSTEM APPLICATIONS LYN SEPARATION OF STTAL PERSONNEL BASED ON ANDROID SYSTEM

The STTAL personnel pickup lyn mechanism and system is a means of pick-up transportation that helps STTAL personnel to transport departures from the pickup location to the work unit (satker). With the STTAL personnel pickup lyn facility can improve the performance of personnel at each work unit, travel time to the work unit is faster than using public transportation. Updated information related to vehicles in support of personnel pick-up cannot yet be fully accessed by personnel, so misinformation often occurs. Therefore, it is necessary to design an Android-based personnel pickup lyn monitoring system application. Here we will design a device for monitoring the pickup lyn by using a transmitter device in the form of a cellphone carried by a man or driver. This system can provide information through an application that is embedded on Android by looking at an integrated application in Open Street Map, which is able to send vehicle position data through an application that is planted on the Android user so that STTAL personnel can monitor the position of pickup service vehicles and updated information related to the vehicle. worn. Users can access the application and see the pickup lyn position on the android mobile. After this system is built, it makes it easy for all STTAL personnel who use the pickup service vehicles to monitor the position of the vehicle through applications that are planted on Android.
 Keywords : Android, Lyn pickup personnel, Open Street Map

Spatio-Temporal Synchronization of Cross Section Based Sensors for High Precision Microscopic Traffic Data Reconstruction

The next generation of Intelligent Transportation Systems (ITS) will strongly rely on a high level of detail and coverage in traffic data acquisition. Beyond aggregated traffic parameters like the flux, mean speed, and density used in macroscopic traffic analysis, a continuous location estimation of individual vehicles on a microscopic scale will be required. On the infrastructure side, several sensor techniques exist today that are able to record the data of individual vehicles at a cross-section, such as static radar detectors, laser scanners, or computer vision systems. In order to record the position data of individual vehicles over longer sections, the use of multiple sensors along the road with suitable synchronization and data fusion methods could be adopted. This paper presents appropriate methods considering realistic scale and accuracy conditions of the original data acquisition. Datasets consisting of a timestamp and a speed for each individual vehicle are used as input data. As a first step, a closed formulation for a sensor offset estimation algorithm with simultaneous vehicle registration is presented. Based on this initial step, the datasets are fused to reconstruct microscopic traffic data using quintic Beziér curves. With the derived trajectories, the dependency of the results on the accuracy of the individual sensors is thoroughly investigated. This method enhances the usability of common cross-section-based sensors by enabling the deriving of non-linear vehicle trajectories without the necessity of precise prior synchronization.

Spatio-Temporal Profiling of Public Transport Delays Based on Large-Scale Vehicle Positioning Data From GPS in Wrocław

In recent years, many studies on urban mobility based on large data sets have been published: most of them are based on crowdsourced GPS data or smart-card data. We present, what is to the best of our knowledge, the first exploration of public transport delay data harvested from a large-scale, official public transport positioning system, provided by the Wrocław municipality. We introduce the methodology to analyze the distribution of delays in public transport, enabling the improvement of timetables by making them more realistic, and thus improve passenger comfort. We evaluate the method considering the characteristics of delays between stops in relation to the direction, time, and delay variance of 1648 stop pairs from 16-mln delay reports. We construct a normalized feature matrix of likelihood of a given delay change happening at a given hour on the edge between two stops. We then calculate the distances between such matrices using the earth mover's distance and cluster them using hierarchical agglomerative clustering with Vor Hees's linkage method. As a result, we obtained six profiles of delay changes in Wrocław: edges nearly not impacting the delay at all, these not impacting the delay significantly, likely to cause strong increase of delay, these causing increase of delay, edges likely to cause strong decrease of delay, and finally these likely to cause decrease of delay (i.e., when a public transport vehicle is speeding). We analyze the spatial and mode of transport properties of each cluster and provide insights into reasons of delay change patterns in each of the detected profiles. Such insights can be successfully utilized in traffic structure optimization and transport model split.

Driving manoeuvre during lane maintenance in older adults: Associations with neuropsychological scores

Older drivers experience difficulties in lane maintenance under challenging driving sections due to age-related cognitive declines, yet there is little comprehensive evidence on associations between cognitive functions and the lane maintenance in this population. In this study, fifty older drivers completed an on-road driving assessment and a battery of standard neuropsychological tests. Mean Lane Position (MLP), Standard Deviation of Lane Position (SDLP) and manoeuvre time calculated from precise vehicle movement trajectories were used as the lane maintenance parameters. The GNSS tracking vehicle movement presents comprehensive and reliable vehicle position data, which is more sensitive for detecting subtle variations of lane maintenance in older drivers. Statistical analysis results showed that lower visual attention (selective and divided attention) was associated with higher MLP and SDLP; MLP was also correlated to spatial abilities, executive function, and motor speed; manoeuvre time was negatively correlated with drivers’ risk-taking personality (all p < .01). Selective attention was found to be the best predictor of MLP in lane maintenance. A combined eight variables from three neuropsychological tests, UFOV 2 and 3, BD and BJLO, D-KEFS TMT 1, 2, 3, and 4, correctly classified 80.4% of participants with good versus low-performing lane maintenance.

Comparing apples and oranges: Off‐road pedestrian detection on the National Robotics Engineering Center agricultural person‐detection dataset

AbstractPerson detection from vehicles has made rapid progress recently with the advent of multiple high‐quality datasets of urban and highway driving, yet no large‐scale benchmark is available for the same problem in off‐road or agricultural environments. Here we present the National Robotics Engineering Center (NREC) Agricultural Person‐Detection Dataset to spur research in these environments. It consists of labeled stereo video of people in orange and apple orchards taken from two perception platforms (a tractor and a pickup truck), along with vehicle position data from Real Time Kinetic (RTK) GPS. We define a benchmark on part of the dataset that combines a total of 76k labeled person images and 19k sampled person‐free images. The dataset highlights several key challenges of the domain, including varying environment, substantial occlusion by vegetation, people in motion and in nonstandard poses, and people seen from a variety of distances; metadata are included to allow targeted evaluation of each of these effects. Finally, we present baseline detection performance results for three leading approaches from urban pedestrian detection and our own convolutional neural network approach that benefits from the incorporation of additional image context. We show that the success of existing approaches on urban data does not transfer directly to this domain.