Vehicle Dynamics Model Research Articles

A dynamic target tracking framework of UGV for UAV recovery under random disturbances

PurposeDynamically tracking the target by unmanned ground vehicles (UGVs) plays a critical role in mobile drone recovery. This study aims to solve this challenge under diverse random disturbances, proposing a dynamic target tracking framework for UGVs based on target state estimation, trajectory prediction, and UGV control.Design/methodology/approachTo mitigate the adverse effects of noise contamination in target detection, the authors use the extended Kalman filter (EKF) to improve the accuracy of locating unmanned aerial vehicles (UAVs). Furthermore, a robust motion prediction algorithm based on polynomial fitting is developed to reduce the impact of trajectory jitter caused by crosswinds, enhancing the stability of drone trajectory prediction. Regarding UGV control, a dynamic vehicle model featuring independent front and rear wheel steering is derived. Additionally, a linear time-varying model predictive control algorithm is proposed to minimize tracking errors for the UGV.FindingsTo validate the feasibility of the framework, the algorithms were deployed on the designed UGV. Experimental results demonstrate the effectiveness of the proposed dynamic tracking algorithm of UGV under random disturbances.Originality/valueThis paper proposes a tracking framework of UGV based on target state estimation, trajectory prediction and UGV predictive control, enabling the system to achieve dynamic tracking to the UAV under multiple disturbance conditions.

Prediction of frequency response of sub-frame bushing and study of high-order fractional derivative viscoelastic model

This paper presents experimental and dynamic modeling research on the rubber bushings of the rear sub-frame. The Particle Swarm Optimization algorithm was utilized to optimize a Backpropagation (BP) neural network, which was separately trained and tested across two frequency ranges: 1–40 Hz and 41–50 Hz, using wideband frequency sweep dynamic stiffness test data. The testing errors at amplitudes of 0.2 mm, 0.3 mm, and 0.5 mm were found to be 1.03%, 3.05%, and 1.96%, respectively. Subsequently, the trained neural network was employed to predict data within the frequency range of 51–70 Hz. To incorporate the predicted data into simulation software, a dynamic model of the rubber bushing was established, encompassing elastic, friction, and viscoelastic elements. Additionally, a novel model, integrating high-order fractional derivatives, was proposed based on the frequency-dependent model for the viscoelastic element. An enhanced Particle Swarm Optimization algorithm was introduced to identify the model's parameters using the predicted data. In comparison to the frequency-dependent model, the new model exhibited lower fitting errors at various amplitudes, with reductions of 3.84%, 3.61%, and 5.49%, respectively. This research establishes a solid foundation for subsequent vehicle dynamic modeling and simulation.

Research on a Path Tracking Control Strategy for Autonomous Vehicles Based on State Parameter Identification

With the rapid development of autonomous driving technology, estimating and controlling key vehicle state parameters under complex road conditions have become critical challenges. This study combines Unscented Kalman Filtering (UKF) and Sliding Mode Control (SMC) methods to propose an integrated control model for achieving more efficient control. First, a three-degrees-of-freedom vehicle dynamics model based on the Dugoff tire model is constructed to accurately estimate key vehicle state parameters. Next, UKF is used to estimate road friction coefficients and key vehicle state parameters, and its performance is compared with Extended Kalman Filtering (EKF) under various conditions. The results show the superiority of UKF in identifying road friction coefficients. Based on SMC theory, a sliding surface is designed, and the functional relationship between state variables and control variables is derived to establish the corresponding control model. Joint simulations using Carsim and Simulink under different conditions validate the real-time performance and effectiveness of the designed UKF-SMC integrated control strategy in the presence of external disturbances and system uncertainties. Simulation results indicate that this strategy effectively enhances the overall performance and safety of autonomous vehicles, providing an accurate real-time solution capable of handling complex and variable road conditions. The proposed UKF-SMC integrated control strategy not only proves its theoretical superiority but also demonstrates promising practical applications in simulation experiments. This study provides reliable technical support for the development of autonomous driving technology under complex road conditions.

A review of research on tire burst and vehicle stability control.

Tire burst is an accidental occurrence that poses a serious threat to the driving stability and road safety of vehicles. Therefore, it is of great practical significance to investigate early warning systems for tire burst and develop stability and safety control measures after burst incidents. The development of an accurate model that can effectively represent the impact of tire burst on vehicle dynamics is crucial for the design of control systems and the development of stability control strategies. Most of the existing research on tire burst models is based on static tire tests, the effectiveness of these models still needs to be further verified. The main approach to studying the impact of burst tires on vehicle performance is to embed a burst tire model into a vehicle dynamics model. Understanding the impact of tire burst on vehicle performance is essential for identifying burst incidents and developing stability control strategies. The research on burst identification primarily focuses on early warning systems and estimating vehicle state parameters after burst incidents, while the current research on stability control strategies focuses on enabling vehicles to continue running safely after burst incidents through braking, active steering, and collaborative control. Currently, there is no comprehensive review of research on vehicle tire burst stability control. Therefore, this paper primarily reviews five aspects: (a) the causes and prevention of tire burst, (b) the impact of tire burst on vehicle performance, (c) burst identification, (d) stability control strategies for burst incidents, and (e) future prospects for tire burst research.

Study on Mathematical Models for Precise Estimation of Tire–Road Friction Coefficient of Distributed Drive Electric Vehicles Based on Sensorless Control of the Permanent Magnet Synchronous Motor

In order to reduce the use of wheel angular velocity sensors and improve the estimation accuracy and robustness of the tire–road friction coefficient (TRFC) in non-Gaussian noise environments, this paper proposes a sensorless control-based distributed drive electric vehicle TRFC estimation algorithm using a permanent magnet synchronous motor (PMSM). The algorithm replaces the wheel angular velocity signal with the rotor speed signal obtained from the sensorless control of the PMSM. Firstly, a seven-degree-of-freedom vehicle dynamics model and a mathematical model of the PMSM are established, and the maximum correntropy singular value decomposition generalized high-degree cubature Kalman filter algorithm (MCSVDGHCKF) is derived. Secondly, a sensorless control system of a PMSM based on the MCSVDGHCKF algorithm is established to estimate the rotor speed and position of the PMSM, and its effectiveness is verified. Finally, the feasibility of the algorithm for TRFC estimation in non-Gaussian noise is demonstrated through simulation experiments, the Root Mean Square Error (RMSE) of TRFC estimates for the right front wheel and the left rear wheel were reduced by at least 41.36% and 40.63%, respectively. The results show that the MCSVDGHCKF has a higher accuracy and stronger robustness compared to the maximum correntropy high-degree cubature Kalman filter (MCHCKF), singular value decomposition generalized high-degree cubature Kalman filter (SVDGHCKF), and high-degree cubature Kalman filter (HCKF).

Optimal hybrid strategy in adaptive cruise control system for enhanced autonomous vehicle stability and safety

The adaptive cruise control system relies heavily on the vehicle's longitudinal control algorithm. Traditional longitudinal control algorithms typically depend on precise vehicle dynamic modeling or complex controller parameter calibrations. In the research, an optimal hybrid strategy for the control of basic adaptive cruise control system is proposed. The proposed hybrid strategy is the combination of both the Model Predictive Control (MPC) and Coati Optimization Algorithm (COA) and hence it is named as MPCCOA strategy. The fundamental goal of the proposed work is that a vehicle should maintain its current velocity when it approaches another vehicle that is driving at a steady velocity. The inputs for the proposed strategy are vehicle speed and acceleration. The inter-vehicle distance error, velocity error and acceleration are regulated based on the upper and lower limit of acceleration command with the help of proposed strategy. For accomplishing the required steadiness with the limitations in the given input and actual velocity with constant previous vehicle velocity, the proposed controller is utilized. The proposed controller is used to control the traction force of the host vehicle such that the vehicle speed follows the optimal speed trajectory as good as possible while ensuring constraints like distance to a preceding vehicle or speed limits. The performance of the proposed is implemented in the MATLAB platform and compared with existing approaches. The proposed technique demonstrates superior performance metrics, achieving the collision rate, rate of reaching maximum deceleration and ratio of exceeding comfortable deceleration or jerk are 1.27 %, 3.131 % and 9.052 %, respectively.

Synthetic Optimization of Trafficability and Roll Stability for Off-Road Vehicles Based on Wheel-Hub Drive Motors and Semi-Active Suspension

Considering the requirements pertaining to the trafficability of off-road vehicles on rough roads, and since their roll stability deteriorates rapidly when turning violently or passing slant roads due to a high center of gravity (CG), an efficient anti-slip control (ASC) method with superior instantaneity and robustness, in conjunction with a rollover prevention algorithm, was proposed in this study. A nonlinear 14 DOF vehicle model was initially constructed in order to explain the dynamic coupling mechanism among the lateral motion, yaw motion and roll motion of vehicles. To acquire physical state changes and friction forces of the tires in real time, corrected LuGre tire models were utilized with the aid of resolvers and inertial sensors, and an adaptive sliding mode controller (ASMC) was designed to suppress each wheel’s slip ratio. In addition, a model predictive controller (MPC) was established to forecast rollover risk and roll moment in reaction to the change in the lateral forces as well as the different ground heights of the opposite wheels. During experimentation, the mutations of tire adhesion capacity were quickly discerned and the wheel-hub drive motors (WHDM) and ASC maintained the drive efficiency under different adhesion conditions. Finally, a hardware-in-the-loop (HIL) platform made up of the vehicle dynamic model in the dSPACE software, semi-active suspension (SAS), a vehicle control unit (VCU) and driver simulator was constructed, where the prediction and moving optimization of MPC was found to enhance roll stability effectively by reducing the length of roll arm when necessary.

Carbody hunting behaviour of high speed vehicles in low effective conicity of wheel-rail contact

In the past, bogie hunting instability garnered more attention due to its potential for causing serious safety issues. However, with the rise in high-speed vehicle speeds and operational mileage, the concerns arising from carbody hunting can no longer be overlooked. This article investigates the causes and solutions for low-frequency carbody swaying under low effective conicity in wheel-rail contact, employing a combination of experimental and numerical techniques. Initially, the study involves an in-depth analysis of time-domain signals from lateral and vertical accelerations of the carbody and bogie frame during a high-speed train field-test. The carbody vibration mode resulting from carbody hunting displays a swaying motion at a frequency of 1.5 Hz. Subsequently, a dynamic system model of the high-speed vehicle was established and the suspension mode of the model were verified. The frequency of the carbody upper sway motion is determined to be 1.39 Hz. Low effective conicity is induced when grinding wheel match with new rail or new wheel match with grinding rail. Under this low effective conicity, the hunting frequency of the vehicle is around 1.5 Hz, close to the frequency of the carbody upper sway motion. The coincidence of the hunting frequency and the upper sway motion frequency leads to the low-frequency swaying of the carbody. At last, the effects of wheel profiles, rail profiles and suspension parameters on the carbody hunting have been studied. The study demonstrates that addressing low-frequency carbody swaying can be achieved through targeted rail grinding. This process involves modifying the rail profile while ensuring careful grinding of the rail inner corner. Furthermore, it has been confirmed in the article that replacing suitable yaw dampers is also effective.

Improved Model Predictive Control Path Tracking Approach Based on Online Updated Algorithm with Fuzzy Control and Variable Prediction Time Domain for Autonomous Vehicles

The design of trajectory tracking controllers for smart driving cars still faces problems, such as uncertain parameters and it being time-consuming. To improve the tracking performance of the trajectory tracking controller and reduce the computation of the controller, this paper proposes an improved model predictive control (MPC) method based on fuzzy control and an online update algorithm. First, a vehicle dynamics model is constructed and a feedforward MPC controller is designed; second, a real-time updating method of the time domain parameters is proposed to replace the previous method of empirically selecting the time domain parameters; lastly, a fuzzy controller is proposed for the real-time adjustment of the weight coefficient matrix of the model predictive controller according to the lateral and heading errors of the vehicle, and a state matrix-based cosine similarity updating mechanism is developed for determining the updating nodes of the state matrix to reduce the controller computation caused by the continuous updating of the state matrix when the longitudinal vehicle speed changes. Finally, the controller is compared with the traditional model prediction controller through the co-simulation of CARSIM and MATLAB/Simulink, and the results show that the controller has great improvement in terms of tracking accuracy and controller computational load.

Comprehensive Multidisciplinary Electric Vehicle Modeling: Investigating the Effect of Vehicle Design on Energy Consumption and Efficiency

In this study, an electric vehicle (EV) dynamic model is devised that amalgamates mechanical design aspects—such as aerodynamic effects, tire friction, and vehicle frontal area—with crucial components of the electrical infrastructure, including the electric motor, power converters, and battery systems. Verification of the model is executed through a comprehensive multidisciplinary analysis utilizing CATIA, ANSYS Electromagnetics, ANSYS Fluent, and MATLAB–Simulink tools, which are applied to evaluate two alternative lightweight EV prototypes. The process involves initial computations of critical inputs for the dynamic model, including aerodynamic lift (C1), drag coefficients (Cd), and frontal area (Af). Subsequent stages entail the detailed design and analysis of a 2 kW brushless permanent magnet electric motor in ANSYS Electromagnetics to map efficiency contours across various speed–torque values. Integration of these parameters into a MATLAB–Simulink dynamic model, connected with motor drive inverter and battery models, allows for simulation-based energy consumption analysis under race track slope profiles. Remarkably, the findings underscore the considerable impact of neglected parameters on energy consumption, often exceeding fifty percent of the total. Consequently, an energy-efficient EV prototype is manufactured and rigorously tested under specified drive conditions, affirming the validation of the comprehensive multidisciplinary EV dynamic model.

Decelerating catalyst aging of natural gas engines using organic Rankine cycle under road conditions

High exhaust temperature is an intrinsic nature of natural gas engines which underlies power de-rating and thermal aging of after-treatment system; therefore, this study integrates an organic Rankine cycle (ORC) system between engine and it's three-way catalyst (TWC) to address these challenges. ORC facilitates power output enhancement through exhaust energy recovery and alleviates thermal aging by reducing exhaust temperature. To estimate the effectiveness of this hypothesized system, a simulation-based investigation is performed. First, simulation models, including engine, TWC, and vehicle dynamic models, are built and validated by experimental data. According to the temperature characteristics of different TWCs, three scenarios, representing old, current, and prospective TWC technology, are formulated to estimate the ORC performance under Worldwide Harmonized Light Vehicles Test Cycle. Results show that ORC system can substantially alleviate the thermal damage caused by high exhaust temperature and extend TWC lifespan. It is estimated that over 98.5 % of thermal damage can be decreased by proper ORC setting, and the average TWC lifespan extension can be at least 55.4, making a reduced noble metal usage and cost of TWC. Meanwhile, with the decrease of the working temperature of TWC, ORC can recover exhaust energy under more road conditions, further improving the net power and shortening the payback period of extra ORC hardware costs. A reduction in the working temperature of TWC from 770.5 K to 618 K yields a 109 % enhancement in maximum power, coupled with a 62.30 % reduction in the payback period. These findings fully reflect the advantage of ORC-TWC coupling and indicate that ORC is supposed to be used more for the TWC with a low working temperature to maximize economic effectiveness. This study provides a novel pathway for thermal aging alleviation of TWC and a valuable reference for prospective studies on matching ORC with TWC under road conditions.

Deep Dynamics: Vehicle Dynamics Modeling With a Physics-Constrained Neural Network for Autonomous Racing

Deep Dynamics: Vehicle Dynamics Modeling With a Physics-Constrained Neural Network for Autonomous Racing

Slope estimation based on multi-source information fusion

Abstract This study presents a novel methodology for slope estimation, addressing limitations in current unidirectional estimation research. A comprehensive vehicle kinematics and dynamics model is developed using Inertial Measurement Unit (IMU) data, enabling a thorough assessment of vehicle slope. The proposal advocates employing the Interactive Multiple Model Unscented Kalman Filter algorithm to enhance algorithmic stability and prevent divergence from singular model errors. This approach ensures precise computation of dynamic slopes. The algorithm’s efficacy is demonstrated through collaborative simulations on the CarSim/Simulink platform. Introducing innovative concepts and methods for slope estimation, this research has significant practical and theoretical implications.

An improved gain-scheduling robust MPC for path following of autonomous independent-drive electric vehicles with time-varying and uncertainties

This paper proposes a gain-scheduling robust model predictive control (GS-RMPC) algorithm for the path-following problem of autonomous independent-drive electric vehicles (AIDEVs) with consideration of time-varying and uncertainties. Firstly, the polytopic uncertainty method and norm-bounded uncertainty method are introduced to characterise the vehicle dynamics model. Secondly, the infinite predict horizon optimisation process of online GS-RMPC is transformed into a series of linear matrix inequalities (LMIs) by minimising the worst-case objective function while considering all scheduling states in the polytope. Thirdly, an offline solution is also proposed to reduce the computational burden based on asymptotically stable invariant ellipse sets. Then, a hierarchical control structure is proposed to distribute the additional yaw moment, and a multi-step predictor is designed to compensate for the actuator time delay. Finally, the hardware-in-the-loop (HIL) testing is conducted to verify the efficacy of the proposed strategy.

Estimation of States and Cornering Stiffness of a Vehicle by LPV‐MHE

In this paper, we consider an estimation problem of sideslip angle and cornering stiffness of a vehicle from measurements of yaw rate, lateral acceleration and steering angle. Firstly, a vehicle dynamical model is expressed as a linear parameter‐varying system with cornering stiffness as uncertain parameters. Then, we show a method to estimate the parameters and the state based on moving horizon estimation. We provide a simple algorithm to solve the estimation problem. The effectiveness of the estimation method is evaluated through numerical simulations. © 2024 Institute of Electrical Engineers of Japan and Wiley Periodicals LLC.

Research on the influence of flexible wheelset rotation effect on wheel rail contact force

PurposeUnder the high-speed operating conditions, the effects of wheelset elastic deformation on the wheel rail dynamic forces will become more notable compared to the low-speed condition. In order to meet different analysis requirements and selecting appropriate models to analyzing the wheel rail interaction, it is crucial to understand the influence of wheelset flexibility on the wheel-rail dynamics under different speeds and track excitations condition.Design/methodology/approachThe wheel rail contact points solving method and vehicle dynamics equations considering wheelset flexibility in the trajectory body coordinate system were investigated in this paper. As for the wheel-rail contact forces, which is a particular force element in vehicle multibody system, a method for calculating the Jacobian matrix of the wheel-rail contact force is proposed to better couple the wheel-rail contact force calculation with the vehicle dynamics response calculation. Based on the flexible wheelset modeling approach in this paper, two vehicle dynamic models considering the wheelset as both elastic and rigid bodies are established, two kinds of track excitations, namely normal measured track irregularities and short-wave irregularities are used, wheel-rail geometric contact characteristic and wheel-rail contact forces in both time and frequency domains are compared with the two models in order to study the influence of flexible wheelset rotation effect on wheel rail contact force.FindingsUnder normal track irregularity excitations, the amplitudes of vertical, longitudinal and lateral forces computed by the flexible wheelset model are smaller than those of the rigid wheelset model, and the virtual penetration and equivalent contact patch are also slightly smaller. For the flexible wheelset model, the wheel rail longitudinal and lateral creepages will also decrease. The higher the vehicle speed, the larger the differences in wheel-rail forces computed by the flexible and rigid wheelset model. Under track short-wave irregularity excitations, the vertical force amplitude computed by the flexible wheelset is also smaller than that of the rigid wheelset. However, unlike the excitation case of measured track irregularity, under short-wave excitations, for the speed within the range of 200 to 350 km/h, the difference in the amplitude of the vertical force between the flexible and rigid wheelset models gradually decreases as the speed increase. This is partly due to the contribution of wheelset’s elastic vibration under short-wave excitations. For low-frequency wheel-rail force analysis problems at speeds of 350 km/h and above, as well as high-frequency wheel-rail interaction analysis problems under various speed conditions, the flexible wheelset model will give results agrees better with the reality.Originality/valueThis study provides reference for the modeling method of the flexible wheelset and the coupling method of wheel-rail contact force to the vehicle multibody dynamics system. Furthermore, by comparative research, the influence of wheelset flexibility and rotation on wheel-rail dynamic behavior are obtained, which is useful to the application scope of rigid and flexible wheelset models.

Steering control for handling and stability and wear resistance of multi-axle vehicles

The steering performance of a vehicle directly affects its handling and stability, and tire wear during steering. For multi-axle vehicles equipped with a mechanical hydraulic steering system (MHS), this paper describes a steering-by-wire hydraulic system (SHS) for a third axle that can reduce the steering wear of third-axle tires while improving the handling and stability. A hydraulic steering system based on a reliable self-locking alignment cylinder was designed in this study, and a nonlinear dynamics model of a multi-axle vehicle was developed. In terms of the control strategy, the upper controller was a decision selector with a lateral acceleration threshold of 0.3 g , at which point the system entered the nonlinear kinematic state as a criterion for decision-making. The suboptimal linear time-varying model predictive control (S-LTV-MPC) method or Ackermann steering theory was used to calculate the target steering angle of the third axle. The lower controller was a fuzzy proportional–integral–derivative controller for tracking the steering angle of the third axle. The simulation and test results showed that the S-LTV-MPC method can meet the requirements of real-time control and accuracy. The designed SHS exhibited good handling and stability, and anti-wear properties during steering.

Mechanism and performance of a passive auxiliary steering device for low-floor light rail vehicle

For low-floor light rail vehicles, a passive auxiliary steering device (ASD) is developed aiming at relieving the independently rotating wheelset (IRW) excessive wear and improving IRW bogie system running safety on curve line. The ASD can be briefly seen as some longitudinal springs arranged between a carbody with IRW and a carbody ahead or behind it to increase the vehicle horizontal flexural stiffness. Consequently, the vehicle will possess the characteristics of both single-type vehicle and traditional vehicle. When passing curved track, the yaw angle between the adjacent carbodies deforms the springs to generate moment which will reduce the unbalanced centrifugal force of the carbody with IRW. As a result, the centering ability of the IRW bogie is improved. The vehicle-track coupled dynamics model for a 6-module single-type low-floor light rail vehicle is developed and the simulations of vehicle passing through tangent and curve lines with and without ASD are carried out. The results indicate that the ASD can improve the IRW restoring capability on tangent track and the IRW negotiation performance on medium and large curved tracks. Besides, larger spring preload, larger steering stiffness and smaller steering clearance are preferred to guarantee the higher performance of this ASD.

Multiple model fault diagnosis and fault tolerant control for the launch vehicle’s attitude control system

Abstract For the launch vehicle attitude control problem, traditional methods can seldom accurately identify the fault types, making the control method lack of pertinence, which largely affects the effect of attitude control. This paper proposes an active fault tolerant control strategy, which mainly includes fault diagnosis and fault tolerant control. In the fault diagnosis part, a small deviation attitude dynamics model of the launch vehicle is established, Kalman filters with different structures are designed to detect and isolate faults through residual changes, and the fault quantity of the actuator is further estimated. In the fault tolerant control part, the following control scheme is adopted according to the above diagnostic information: when the sensor fault is detected, the sensor measurement data is reconstructed; when the actuator fault is identified, the control allocation matrix is reconstructed. Simulation results show that the proposed method can effectively diagnose sensor fault and actuator faults, and significantly improve attitude tracking accuracy and control adjustment time.

FGO-MFI: factor graph optimization-based multi-sensor fusion and integration for reliable localization

Reliable and precise information pertaining to the position, velocity, and attitude is essential for automated driving. This paper proposes FGO-MFI, a cost-effective and robust multi-sensor fusion and integration localization framework that utilizes factor graph optimization. Firstly, a tightly coupled Global Navigation Satellite Systems (GNSS)/on-board sensor fusion localization framework is established to estimate vehicle states, including position, velocity, and attitude. To address the large drift rate of the Inertial Measurement Unit (IMU), this study introduces a novel IMU/Dynamics pre-integration method based on the vehicle dynamics model. We establish a two-degree-of-freedom vehicle dynamics model utilizing measurements from the wheel speed sensor and steering wheel angle sensor. The IMU/Dynamics factor is devised through a close integration of the model output and IMU pre-integration, enabling the construction of precise odometry with low-cost on-board sensors. Then, to address the issue of the non-Gaussian distribution of GNSS pseudorange error, this paper employs a Gaussian Mixture Model (GMM) to characterize the pseudorange noise, which is then applied to further sensor fusion. Given the time-varying nature of pseudorange noise, the expectation maximization algorithm is utilized to estimate GMM parameters online, leveraging the pseudorange residuals within a sliding window. Comprehensive experiments, inclusive of challenging scenarios such as urban canyons, tunnels, and wooded areas, have been carried out. They affirm the superior performance of the proposed method. Experiments have shown that our method demonstrates reliable localization across different statuses of GNSS signal, exhibiting a 39.4% improvement in the root mean square error of position error when compared to the state-of-the-art. Additionally, this FGO-MFI is a general sensor data fusion framework and is able to incorporate diverse sensor measurements, for example, from cameras and LiDARs to provide more reliable and accurate localization information.