Vehicle Position Error Research Articles

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.

Event-Based Communication Strategies for Collaborative Inertial Radio SLAM

Event-based communication strategies for vehicles navigating by aiding their inertial navigation systems (INSs) with terrestrial signals of opportunity (SOPs) are developed. The following problem is considered. Multiple navigating vehicles with access to global navigation satellite system (GNSS) signals are aiding their on-board INSs with GNSS pseudoranges. While navigating, vehicle-mounted receivers draw pseudorange measurements from terrestrial SOPs with unknown emitter positions and unknown and unsynchronized clocks. The vehicles share INS data and SOP pseudoranges to collaboratively estimate the SOPs' states through an extended Kalman filter. After some time, GNSS signals become unavailable, at which point the navigating vehicles use shared INS and SOP information to continue navigating in a collaborative inertial radio simultaneous localization and mapping (CIRSLAM) framework. Two eventbased communication strategies to share this information are developed, where instead of sharing information at a fixedrate when measurements become available, information is only shared whenever any vehicle's position error could violate a userspecified position error threshold with some desired probability. Simulation results are presented demonstrating the tradeoff between localization performance and the accumulated transmitted data when the event-based transmission scheme is employed versus sharing data at the fixed-rate. Experimental results are presented demonstrating two unmanned aerial vehicles (UAVs) navigating in a CIRSLAM framework with SOP pseudoranges drawn from terrestrial cellular towers. The event-based communication scheme reduced the cumulative communicated data by 86.6% compared to a fixed-rate scheme, while maintaining the specified error constraint.

Parameter Estimation of Radar Noise Model for Terrain Referenced Navigation Using a New EM Initialization Method

In this paper, we present an expectation-maximization (EM) initialization method for estimation of unknown parameters in a radar noise model. We deal with radar based terrain referenced navigation (TRN) over vegetated areas, where the radar noise was modeled as a double-mode Gaussian mixture with two unknowns. The known parameters are used for EM initialization along with innovation samples. Through TRN simulation, it is shown that the proposed EM initialization method outperforms random or k-means initialization methods in terms of both the parameter estimation error and the vehicle position error.

A Novel Vehicle-Based GNSS Integrity Augmentation System for Autonomous Airport Surface Operations

Autonomous vehicles equipped with integrity augmentation systems offer the potential to increase safety, efficiency and sustainability of airport ground operations. The model predictive behavior of these systems supports a timely detection of any deviations from the Required Navigation Performance (RNP), producing useful alerts for onboard mission management. Firstly, the system architecture of a Navigation and Guidance System (NGS) for autonomous airport surface vehicle operations based on Global Navigation Satellite System (GNSS) measurements is described. Subsequently, an integrity augmentation module is implemented in the NGS by modeling the key GNSS signal degradation phenomena including masking, multipath and signal attenuation. The GNSS integrity augmentation system is capable of monitoring the RNP and alerting the remote operator of the airport surface vehicle. The uniqueness of the presented system is that both caution and warning flags are produced based on prediction-avoidance and reaction-correction capabilities respectively. Additionally, the system is capable of issuing suitable steering commands to the onboard mission management system/remote ground base station operator in the event of GNSS signal degradations or losses. Multipath is modelled in detail using a ray tracing algorithm and the vehicle position error is computed as a function of relative geometry between the satellites, receiver antenna and reflectors in realistic airport operation scenarios. Additionally, the surface vehicle dynamics and reflective surfaces of buildings are modelled in order to simulate a vehicle trajectory through a typical airport airside/aprons environment. Simulation case studies are performed to validate the mathematical models developed for the integrity augmentation system and the results corroborate the suitability of the proposed system to generate useful and timely integrity flags when GNSS is used as the primary means of navigation.

3402 最適軌道に対するスキッドステア式小型無人車両の追従制御

This paper investigates an optimal control maneuver of a skid-steer small unmanned ground vehicle (UGV). The optimal control problem is prescribed as a minimum-time maneuver of the UGV, and it is solved using pseudospectral (PS) optimal control theory. The optimal trajectory which includes the pivot-turn maneuver is provided to the real UGV system as reference input commands, and the vehicle tracks it by the gain-scheduled feedback law on the vehicle position error. The results provide nominal tracking performance and validate the feasibility of the approach.

Adaptive vehicle skid control

In this paper, adaptive vehicle skid control, for stability and tracking of a vehicle during slippage of its wheels without braking, is addressed. Two adaptive control algorithms are developed: one for the case when no road condition information is available, and one for the case when certain information is known only about the instant type of road surface on which the vehicle is moving. The vehicle control system with an adaptive control law keeps the speed of the vehicle as desired by applying more power to the drive wheels where the additional driving force at the non-skidding wheel will compensate for the loss of the driving force at the skidding wheel, and also arranges the direction of the vehicle motion by changing the steering angle of the two front steering wheels. Stability analysis proves that the vehicle position and velocity errors are both bounded. With additional road surface information available, the adaptive control system guarantees that the vehicle position error and velocity error converge to zero asymptotically even if the road surface parameters are unknown.

Lunar Surface Navigation

The adverse environment of the lunar surface requires the implementation of unique land vehicle navigation techniques, concepts, and components to perform postulated future lunar surface navigation functions. Subject to the lunar environmental constraints, three generalized lunar surface navigation concepts are modeled and analyzed. The generalized hybrid concepts, consisting of both position fix and dead reckoning subconcepts, are a passive non-gyro, and inertial, and rf system. A generalized error model of the lunar surface navigation function, is applied to assess navigation system accuracy for each of the three systems. The analytical approach to model derivation is basically the covariance technique where 3 sigma error parameters of equipment errors and 3 sigma error measures of lunar physical uncertainties are related to a 3 sigma vehicle position error ellipsoid about a true point on the lunar surface. The error model digital computer program is applied to the navigation concepts and results are shown which indicate representative component and total concept accuracy requirements for lunar surface navigation in the 1972 to 1985 era.

Probable Accuracy in Acquisition Aids for an Orbiting Vehicle

Efficient operation of a tracking system requires that certain acquisition aids be available to each station prior to vehicle approach. One method of deriving such aids is based on extrapolation of vehicle position from a set of orbital parameters. Since in general, extrapolation errors tend to increase with time, orbital parameters must be updated periodically to provide for automatic acquisition. A statistical analysis is presented for assessing extrapolation errors in vehicle position. Starting with a set of orbital elements and a prescribed distribution of errors in the latter, an upper bound is obtained for a statistical parameter associated with the magnitude of the vehicle position error vector. This upper bound is time-dependent and is applied to estimate the related errors in acquisition aids. Thus, all estimates depend upon the time lapse which has expired since the orbital parameters were updated. The method of analysis is general and may be applied to a wide class of tracking systems. An example is included to indicate the magnitude of errors for a particular hypothetical vehicle.