Vehicle Position Research Articles

Extremity Injuries in Pillion Riders of Fatal 2-Wheeler Road Traffic Accidents: An Autopsy-Based Study.

The extremities are the important anatomical regions that are disproportionately injured during road traffic accidents in poor and medium-income countries. The data regarding extremity injuries, particularly in pillion passengers, are minimal globally. We analyzed the pattern of extremity injuries and their association with various parameters such as collision type, seating position, road type, and type of vehicle. This was a hospital-based cross-sectional study. The pillion riders of fatal motorized 2-wheeler road accident cases that were subjected to autopsy were studied over a period of 2 years. We analyzed 73 cases of pillion riders. Females (60%) were the most commonly affected group. The upper extremities were injured in 55 cases (75.3%) and the lower extremities in 49 cases (67.1%). The shoulder (41%), hand (31.3%), and elbow (23.2%) were the predominantly injured anatomical regions of the upper limb. In the lower extremity, knee (41%) and foot (21.9%) were mostly affected. Forty-nine (67.1%) and 48 (65.8%) cases had abrasions in the upper limbs and lower limbs, respectively, followed by lacerations and contusions. Upper extremity injuries are more common compared with lower extremity. The majority of the pillion riders were seated in a side-saddle position, and self-fall from the bike without any collision with other vehicles was the most common mode of injury. Thus, appropriate seating posture with safety gear can be used to reduce extremity injuries.

Dynamic Event-Triggered Output Feedback Fault-Tolerant Control for Dynamic Positioning of Unmanned Surface Vehicles With Multiple Estimators

Dynamic Event-Triggered Output Feedback Fault-Tolerant Control for Dynamic Positioning of Unmanned Surface Vehicles With Multiple Estimators

Adhesion Coefficient Identification of Wheeled Mobile Robot under Unstructured Pavement.

Because of its uneven and large slope, unstructured pavement presents a great challenge to obtaining the adhesion coefficient of pavement. An estimation method of the peak adhesion coefficient of unstructured pavement on the basis of the extended Kalman filter is proposed in this paper. The identification accuracy of road adhesion coefficients under unstructured pavement is improved by introducing the equivalent suspension model to optimize the calculation of vertical wheel load and modifying vehicle acceleration combined with vehicle posture data. Finally, the multi-condition simulation experiments with Carsim are conducted, the estimation accuracy of the adhesion coefficient is at least improved by 3.6%, and then the precision and effectiveness of the designed algorithm in the article are verified.

Control of the PVTOL with Strong Input Coupling

This paper studies the modeling and control of a Planar Vertical Take-Off and Landing (PVTOL) with steerable thruster. A longitudinal model is obtained using Newton’s second law for the PVTOL which evolves in 3 degrees of freedom and has two control inputs. The aerial vehicle is driven by steerable propulsion controlling its evolution in the vertical plane through the thrust and torque control inputs, which drive the vehicle body and generate a rotation. The obtained model is nonlinear and is significantly different with respect to the well-known PVTOL. For this reason, different control algorithms are presented, and the closed-loop behavior is studied for each of them. The proposed control strategies perform a stationary flight at a desired altitude and control the position of the aerial vehicle. The performance of the proposed control algorithms is tested in numerical simulations.

Vehicle Speed Detection using Haar Cascade Classifier and Correlation Tracking

Objectives: The aim of this study is to develop an efficient and cost-effective solution for predicting vehicle speeds using recorded video data. Methods: The proposed system employs a combination of image processing techniques and computer vision to calibrate cameras for traffic simulation, enabling the extraction of information on average vehicle speeds. It utilizes the Haar Cascade Classifier for object detection, followed by a correlation tracker for vehicle tracking. Speed estimation is achieved through the frame differencing method. The dataset comprises 90 minutes of recorded data from highway cameras, showcasing diverse traffic scenarios with various vehicle types (trucks, trailers, cars, buses, and bikes) at varying speeds. Predicted values are compared with ground truth data obtained from a GPS-equipped car, using Mean Absolute Error (MAE) as the evaluation metric. Findings: The algorithm's performance is evaluated, resulting in an average error rate of 1.72 km/h (2.07%). These findings are compared with state-of-the-art data. Novelty: This study introduces a novel system that combines the Haar Cascade Classifier, correlation tracker, and frame differencing method to track vehicle positions, incorporating bike detection into the analysis, and calculate their moving speeds. A relative analysis underscores the system's performance, emphasizing its effectiveness in real-world applications and demonstrating refinement in accuracy assessment. Keywords: Image processing, Vehicle speed estimation, Haar Cascade Classifier, Correlation tracker, Error rate calculation, Computer vision

Research on YOLOv5 Vehicle Detection and Positioning System Based on Binocular Vision

Vehicle detection and location is one of the key sensing tasks of automatic driving systems. Traditional detection methods are easily affected by illumination, occlusion and scale changes in complex scenes, which limits the accuracy and robustness of detection. In order to solve these problems, this paper proposes a vehicle detection and location method for YOLOv5(You Only Look Once version 5) based on binocular vision. Binocular vision uses two cameras to obtain images from different angles at the same time. By calculating the difference between the two images, more accurate depth information can be obtained. The YOLOv5 algorithm is improved by adding the CBAM attention mechanism and replacing the loss function to improve target detection. Combining these two techniques can achieve accurate detection and localization of vehicles in 3D space. The method utilizes the depth information of binocular images and the improved YOLOv5 target detection algorithm to achieve accurate detection and localization of vehicles in front. Experimental results show that the method has high accuracy and robustness for vehicle detection and localization tasks.

Online calibration method for SINS/LDV integrated navigation system based on left group error definition

The integration of strapdown inertial navigation system (SINS) and laser Doppler velocimeter (LDV) is a reliable technology for land vehicle positioning. To ensure the best positioning performance of the SINS/LDV integrated navigation system, it is necessary to calibrate it accurately. However, the accuracy of the error model of the traditional calibration method is seriously affected by the large misalignment angle, which in turn affects the accuracy and consistency of the filtering, and eventually leads to the decline of the calibration accuracy. Therefore, this paper introduces the Lie group theory for the first time into the calibration study of the SINS/LDV integrated navigation system. Based on the error state vector defined by the left group error definition in the Lie group, the three calibration models of the SINS/LDV integrated navigation system are derived in the Earth-centered Earth-fixed frame, using velocity, displacement increment, and dead reckoning (DR) position, which are the three common observation information. The most significant advantage of these calibration models is their ability to handle large initial misalignment angles. The calibration models proposed in this paper are comprehensively evaluated by two long-distance vehicle experiments. The test results show that under normal conditions (no large attitude misalignment angle and all sensors are working properly), the Lie group-based calibration methods have similar performance to the traditional calibration method, but they have significant advantages in the case of large initial attitude deviation. In addition, using displacement increment and DR position as observations improves calibration performance compared to velocity.

Optimal vehicle position estimation using adaptive unscented Kalman filter based on sensor fusion

Precise position recognition systems are actively used in various automotive technology fields such as autonomous vehicles, intelligent transportation systems, and vehicle driving safety systems. In line with this demand, this paper proposes a new vehicle position estimation algorithm based on sensor fusion between low-cost standalone global positioning system (GPS) and inertial measurement unit (IMU) sensors. In order to estimate accurate vehicle position information using two complementary sensor types, adaptive unscented Kalman filter (AUKF), an optimal state estimation algorithm, is applied to the vehicle kinematic model. Since this AUKF includes an adaptive covariance matrix whose value changes under GPS outage conditions, it has high estimation robustness even if the accuracy of the GPS measurement signal is low. Through comparison of estimation errors with both extended Kalman filter (EKF) and UKF, which are widely used state estimation algorithms, it can be confirmed how improved the estimation performance of the proposed AUKF algorithm in real-vehicle experiments is. The given test course includes roads of various shapes as well as GPS outage sections, so it is suitable for evaluating vehicle position estimation performance.

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.

Assessment of Drivers' Mental Workload by Multimodal Measures during Auditory-Based Dual-Task Driving Scenarios.

Assessing drivers' mental workload is crucial for reducing road accidents. This study examined drivers' mental workload in a simulated auditory-based dual-task driving scenario, with driving tasks as the main task, and auditory-based N-back tasks as the secondary task. A total of three levels of mental workload (i.e., low, medium, high) were manipulated by varying the difficulty levels of the secondary task (i.e., no presence of secondary task, 1-back, 2-back). Multimodal measures, including a set of subjective measures, physiological measures, and behavioral performance measures, were collected during the experiment. The results showed that an increase in task difficulty led to increased subjective ratings of mental workload and a decrease in task performance for the secondary N-back tasks. Significant differences were observed across the different levels of mental workload in multimodal physiological measures, such as delta waves in EEG signals, fixation distance in eye movement signals, time- and frequency-domain measures in ECG signals, and skin conductance in EDA signals. In addition, four driving performance measures related to vehicle velocity and the deviation of pedal input and vehicle position also showed sensitivity to the changes in drivers' mental workload. The findings from this study can contribute to a comprehensive understanding of effective measures for mental workload assessment in driving scenarios and to the development of smart driving systems for the accurate recognition of drivers' mental states.

Improving Vehicle Heading Angle Accuracy Based on Dual-Antenna GNSS/INS/Barometer Integration Using Adaptive Kalman Filter

High-accuracy heading angle is significant for estimating autonomous vehicle attitude. By integrating GNSS (Global Navigation Satellite System) dual antennas, INS (Inertial Navigation System), and a barometer, a GNSS/INS/Barometer fusion method is proposed to improve vehicle heading angle accuracy. An adaptive Kalman filter (AKF) is designed to fuse the INS error and the GNSS measurement. A random sample consensus (RANSAC) method is proposed to improve the initial heading angle accuracy applied to the INS update. The GNSS heading angle obtained by a dual-antenna orientation algorithm is additionally augmented to the measurement variable. Furthermore, the kinematic constraint of zero velocity in the lateral and vertical directions of vehicle movement is used to enhance the accuracy of the measurement model. The heading errors in the open and occluded environment are 0.5418° (RMS) and 0.636° (RMS), which represent reductions of 37.62% and 47.37% compared to the extended Kalman filter (EKF) method, respectively. The experimental results demonstrate that the proposed method effectively improves the vehicle heading angle accuracy.

Design and Implementation of Continuous Bounded Finite-Time Attitude Control of Reusable Launch Vehicle

In this paper, a novel continuous bounded finite-time control algorithm is proposed for reusable launch vehicle (RLV) attitude control system with mismatched disturbances and unknown control coefficients. The remarkable feature of the developed algorithm is to ensure the finite-time stability of RLV attitude system with uniformly continuous bounded control torque signal. Meanwhile, a multivariable finite-time observer is designed to estimate the unknown mismatched disturbances, and the combination of the developed controller and observer could offer better robustness without losing control boundedness. Finally, numerical simulations and experimental results based on three-axis turntable are provided to verify the effectiveness and advantages of the proposed continuous bounded finite-time control method.

Pseudolite-Based Lane-Level Vehicle Positioning in Highway Tunnel

Pseudolite-Based Lane-Level Vehicle Positioning in Highway Tunnel

Enhanced Land Vehicle Positioning in Challenging GNSS Urban Environments Utilizing Automotive Radars

Enhanced Land Vehicle Positioning in Challenging GNSS Urban Environments Utilizing Automotive Radars

Cooperative Vehicle Localization in Multi-Sensor Multi-Vehicle Systems Based on an Interval Split Covariance Intersection Filter with Fault Detection and Exclusion

In the cooperative multi-sensor multi-vehicle (MSMV) localization domain, the data incest problem yields inconsistent data fusion results, thereby reducing the accuracy of vehicle localization. In order to address this problem, we propose the interval split covariance intersection filter (ISCIF). At first, the proposed ISCIF method is applied to the absolute positioning step. Then, we combine the interval constraint propagation (ICP) method and the proposed ISCIF method to realize relative positioning. Additionally, in order to enhance the robustness of the MSMV localization system, a Kullback–Leibler divergence (KLD)-based fault detection and exclusion (FDE) method is implemented in our system. Three simulations were carried out: Simulation scenarios 1 and 2 aimed to assess the accuracy of the proposed ISCIF with various capabilities of absolute vehicle positioning, while simulation scenario 3 was designed to evaluate the localization performance when faults were present. The simulation results of scenarios 1 and 2 demonstrated that our proposed vehicle localization method reduced the root mean square error (RMSE) by 8.9% and 15.5%, respectively, compared to the conventional split covariance intersection filter (SCIF) method. The simulation results of scenario 3 indicated that the implemented FDE method could effectively reduce the RMSE of vehicles (by about 55%) when faults were present in the system.

Computer Vision-Based Real-Time Identification of Vehicle Loads for Structural Health Monitoring of Bridges

Coupling effects of various loading conditions can cause deflections, settlements and even failure of in-service bridges. Although it is one of the most critical loads, unfortunately, loading conditions of moving vehicles are difficult to capture in real time by bridge monitoring systems currently in place for sustainable operation. To fully understand the status of a bridge, it is essential to obtain instantaneous vehicle load distributions in a dynamic traffic environment. Although there are some methods that can identify overweight vehicles, the captured vehicle-related information is scattered and incomplete and thus cannot support effective bridge structural health monitoring (BSHM). This study proposes a noncontact, vision-based approach to identification of vehicle loads for real-time monitoring of bridge structural health. The proposed method consists of four major steps: (1) establish a dual-object detection model for vehicles using YOLOv7, (2) develop a hybrid coordinate transformation model on a bridge desk, (3) develop a multiobject tracking model for real-time trajectory monitoring of moving vehicles, and (4) establish a decision-level fusion model for fusing data on vehicle loads and positions. The proposed method effectively visualizes the 3D spatiotemporal vehicular-load distribution with low delay at a speed of over 30FPS. The results show that the hybrid coordinate transformation ensures that the vehicle position error is within 1 m, a 5-fold reduction compared with the traditional method. Wheelbase is calculated through dual-object detection and transformation and is as the primary reference for vehicle position correction. The trajectory and real-time speed of vehicles are preserved, and the smoothed speed error is under 5.7%, compared with the speed measured by sensors. The authors envision that the proposed method could constitute a new approach for conducting real-time SHM of in-service bridges.

Information Acquisition and Seismic Damage Prediction of Masonry Structures in Rural Areas Based on UAV Inclined Photogrammetry

Using a novel methodology that integrates incremental dynamic analysis (IDA) and unmanned aerial vehicle positioning (POS) analysis, this study aims to assess the seismic risk of brick structures in rural China. This method can collect a lot of data and accurately anticipate seismic damage by combining UAV oblique photography with IDA analysis. Because rural China has many masonry structures, the project will design unique seismic risk mitigation strategies. High-resolution cameras on Unmanned Aerial Vehicles capture realistic photographs of rural brick buildings. The collected data is carefully examined to reveal architectural and structural elements. The project uses dynamic post-processing software from the CHC Geomatics Office to improve UAV-reference station position accuracy. This program analyzes UAV POS data disparities. The findings allow rural Chinese brick buildings to be assessed for seismic sensitivity during unexpected ground shaking occurrences. UAV tilt-photography reduces manpower and expenditures, improving inquiry efficiency. This combination improves seismic risk response. The IDA and UAV POS analysis are essential for earthquake preparedness and risk mitigation. This data-driven method informs lawmakers, urban planners, and disaster management authorities worldwide, improving earthquake engineering and catastrophe resilience programs. This work improves seismic threat assessment and masonry structure fortification, making earthquake-prone buildings safer. Thus, rural communities benefit from it.

Air and Structural Loads Analysis of a 5-Ton Class Rotorcraft in a Pull-Up Maneuver Using CFD/CSD Coupled Approach

The air and structural loads of a 5-ton class light helicopter (LH) rotor in a 2.24 g pull-up maneuver are investigated using a coupling between the computational structural dynamics (CSD) and computational fluid dynamics (CFD) methods. The LH rotor is characterized by a five-bladed system with elastomeric bearings and inter-bladed dampers. The periodic trim solution along with the converged CFD/CSD delta airloads obtained in steady-level flight (advance ratio of 0.287) are used to perform the transient CSD maneuver analysis. The resulting vehicle attitude angles and velocity profiles of the aircraft are then prescribed in the quasi-static (QS) CFD maneuver analysis. It is demonstrated that the present QS approach provides an effective means for the maneuver loads’ analysis. The important flow behaviors such as BVI (blade–vortex interaction)-induced oscillations and the negative pitching moment peaks met in maneuver flight are captured nicely with the proposed method. Either the vortex trajectories or the surface pressure distributions are examined to identify the sources of the oscillations. A loose CFD/CSD coupling (LC) is used to predict the blade elastic motions, structural moments, and pitch link loads at the specified maneuver revolution of the rotor and also to correlate these with the transient CSD-based predictions. A reasonable correlation is obtained. The LC results show more pronounced 5P (five per revolution) oscillations on the structural response than those of the CSD-based methods.

An Optimal Hierarchical Control Strategy for 4WS-4WD Vehicles Using Nonlinear Model Predictive Control

Advanced driving algorithms, control strategies, and their optimization in self-driving vehicles in various scenarios are hotspots in current research; 4WS-4WD (four-wheel steering and four-wheel drive) is another hotspot in the study of new concept models; and the nonlinear dynamic characteristics of self-driving vehicles (AVs) are prominent in the fast cornering mode, which leads to a significant reduction in the accuracy and stability of trajectory tracking. Based on these research backgrounds, this paper proposes a control strategy optimization idea based on the 4WS4WD vehicle and its optimization model. The main content includes the establishment of a 3D vehicle model that takes into account vehicle load transfer and position change, and the establishment of a hierarchical control strategy based on the optimized NMPC and 4WS4WD models. The controller consists of two parts: an upper tracking controller based on the new vehicle model and NMPC, and a lower decoupled controller. The tracking control effect of the algorithmic control strategy based on the model and controller is validated in the high-speed serpentine motion mode and double-shift linear motion mode on the joint simulation platform of Car Sim and Simulink.

A nonlinear model predictive control for air suspension in hub motor electric vehicle

The hub-motor electric vehicle (HM-EV) is considered as an ideal configuration for electric vehicles (EVs). However, the electromechanical coupling effect deteriorates HM-EV ride comfort, which limits its widespread application in EVs. In this study, the HM-EV dynamic system with air springs is proposed to intervene in vehicle attitude and ride comfort. The HM-EV dynamic model with air spring, considering the electromechanical coupling effect, is established and the test validation is investigated. Then quasi-infinite horizon nonlinear model predictive control (QIH NMPC) is designed to improve the longitudinal and vertical dynamic performance. The dynamic performance of passive suspension, air suspension based on QIH NMPC, air suspension based on MPC, and PID control receptively, are compared under several random road scenarios. Finally, the results indicated that the proposed control algorithm can improve ride comfort, reduce motor vibration, and improve longitudinal and vertical dynamic performance.