Lateral Position Error Research Articles

An integrated longitudinal and lateral control of vehicle platoons

This paper proposes an integrated longitudinal and lateral control scheme for a vehicle platoon with the predecessor-leader communication topology. Both longitudinal and lateral dynamics are established, where the lateral dynamics considers the influence of variance of the longitudinal velocity. The decoupled longitudinal and lateral controllers are designed, respectively. The longitudinal controller adopts the distributed model predictive control algorithm, which regulates the following vehicles to track the longitudinal velocity of the leading vehicle and to keep the inter-vehicle desired spacing. Both the recursive feasibility of optimization problem and the asymptotic consensus of vehicle platoons are analyzed. Furthermore, the lateral controller adopts a feedforward and robust feedback control strategy to track the reference path without offset of the lateral position error. Simulation results in joint of MATLAB/Simulink and TruckSim verify the effectiveness of the proposed scheme.

Research on Intelligent Vehicle Trajectory Tracking Control Based on Improved Adaptive MPC.

Intelligent vehicle trajectory tracking exhibits problems such as low adaptability, low tracking accuracy, and poor robustness in complex driving environments with uncertain road conditions. Therefore, an improved method of adaptive model predictive control (AMPC) for trajectory tracking was designed in this study to increase the corresponding tracking accuracy and driving stability of intelligent vehicles under uncertain and complex working conditions. First, based on the unscented Kalman filter, longitudinal speed, yaw speed, and lateral acceleration were considered as the observed variables of the measurement equation to estimate the lateral force of the front and rear tires accurately in real time. Subsequently, an adaptive correction estimation strategy for tire cornering stiffness was designed, an AMPC method was established, and a dynamic prediction time-domain adaptive model was constructed for optimization according to vehicle speed and road adhesion conditions. The improved AMPC method for trajectory tracking was then realized. Finally, the control effectiveness and trajectory tracking accuracy of the proposed AMPC technique were verified via co-simulation using CarSim and MATLAB/Simulink. From the results, a low lateral position error and heading angle error in trajectory tracking were obtained under different vehicle driving conditions and road adhesion conditions, producing high trajectory-tracking control accuracy. Thus, this work provides an important reference for improving the adaptability, robustness, and optimization of intelligent vehicle tracking control systems.

Navigation system for orchard spraying robot based on 3D LiDAR SLAM with NDT_ICP point cloud registration

Autonomous navigation of orchard spraying robots can effectively improve operational efficiency and reduce workload. The conventional robot navigation solutions based on the Global Navigation Satellite System (GNSS) are susceptible to signal loss in orchards due to interference from the canopy of fruit trees. In this paper, an autonomous navigation scheme for orchard spraying robots based on multi-sensor devices and the three-dimensional (3D) LiDAR Simultaneous Localization and Mapping (SLAM) technology. The initial step of the scheme involved the construction of a 3D point cloud map of the orchard using the 3D LiDAR SLAM algorithm. Subsequently, a designed point cloud map transformation algorithm was used to convert the 3D point cloud map of the orchard into a two-dimensional (2D) grid map containing key feature information of the orchard. To enhance the mapping accuracy of the orchard point cloud, a point cloud registration algorithm combining Iterative Closet Point (ICP) and Normal Distributions Transform (NDT) was proposed. This algorithm initially utilized the NDT algorithm for coarse point cloud registration to obtain transformation parameters. Then, the ICP algorithm was applied in conjunction with the obtained parameters for fine registration. Regarding obstacle avoidance, a cooperative obstacle avoidance algorithm based on the Robot Operating System (ROS) multithreaded communication mechanism was introduced. Experimental trials conducted in a standardized spindle-shaped peach orchard validated the successful completion of the SLAM mapping trajectory, which was achieved through the utilization of the proposed NDT_ICP point cloud registration algorithm. The average absolute pose error between the mapped trajectory and the reference trajectory was 1.173 m, with a standard deviation of 0.498 m. The robot's positioning and navigation errors increased with higher speeds, with the largest positioning deviation observed at 1.2 m/s speed, with a maximum lateral positioning error of 7.04 cm. The robot's average lateral navigation error did not exceed 16 cm, and the average heading deviation was less than 8° at different speeds of 0.4, 0.8 and 1.2 m/s. Both the positioning and navigation accuracies of the robot were sufficient to meet the autonomous operational requirements of orchard spraying robots.

Utilizing Probabilistic Maps and Unscented-Kalman-Filtering-Based Sensor Fusion for Real-Time Monte Carlo Localization

An autonomous car must know where it is with high precision in order to maneuver safely and reliably in both urban and highway environments. Thus, in this paper, a reliable and relatively precise position estimation (localization) technique for autonomous vehicles is proposed and implemented. In dealing with the obtained sensory data or given knowledge about the vehicle’s surroundings, the proposed method takes a probabilistic approach. In this approach, the involved probability densities are expressed by keeping a collection of samples selected at random from them (Monte Carlo simulation). Consequently, this Monte Carlo sampling allows the resultant position estimates to be represented with any arbitrary distribution, not only a Gaussian one. The selected technique to implement this Monte-Carlo-based localization is Bayesian filtering with particle-based density representations (i.e., particle filters). The employed particle filter receives the surrounding object ranges from a carefully tuned Unscented Kalman Filter (UKF) that is used to fuse radar and lidar sensory readings. The sensory readings are used to detect pole-like static objects in the egocar’s surroundings and compare them to the ones that exist in a supplied detailed reference map that contains pole-like landmarks that are produced offline and extracted from a 3D lidar scan. Comprehensive simulation tests were conducted to evaluate the outcome of the proposed technique in both lateral and longitudinal localization. The results show that the proposed technique outperforms the other techniques in terms of smaller lateral and longitudinal mean position errors.

Joint estimation of position and attitude for intelligent vehicles by fusing delayed visual measurements

Accurate position and attitude information is an important basis for normal driving of intelligent vehicles. In this paper, we investigate the estimation of position and attitude states for intelligent vehicles with low cost scheme. The low cost GNSS, camera, and proprioceptive sensors equipped by mass-produced vehicle are fused to estimate the states. The visual measurements adopted in this paper are based on the lateral distance and deflection angle to road features such as lane lines or curbs, which are generated more frequently than some other semantic features such as traffic lights, and leads to broader application scenario. Moreover, it is easier to implement compared with geometrical feature matching methods, since it only needs a simple prior map while latter needs large maps containing many high precision features. The visual measurements is often with large time delay due to negligible processing time. In order to fuse delayed measurements, a state-augmentation technique is adopted for the estimator design. The performance of the proposed method is evaluated by professional simulation software CarMaker, and shows that the incorporation of road features based visual measurement can effectively improve the position and attitude estimation accuracy by reducing the lateral position and yaw angle estimation error.

Trajectory Planning for an Articulated Tracked Vehicle and Tracking the Trajectory via an Adaptive Model Predictive Control

This paper focuses on the trajectory planning and trajectory tracking control of articulated tracked vehicles (ATVs). It utilizes the path planning method based on the Hybrid A-star and the minimum snap smoothing method to obtain the feasible kinematic trajectory. To overcome the highly non-linearity of ATVs, we proposed a linear-parameter-varying (LPV) kinematic tracking-error model. Then, the kinematic controller was formulated as the adaptive model predictive controller (AMPC). The simulation of the path planning algorithm showed that the proposed planning strategy could provide a feasible trajectory for the ATVs passing through the obstacles. Moreover, we compared the AMPC controller with the developed controller in four scenarios. The comparison showed that the AMPC controller achieved satisfactory tracking errors regarding the lateral position and orientation angle errors. The maximum lateral distance error by the AMPC controller has been reduced by 72.4% compared to the standard-MPC controller. The maximum orientation angle error has been reduced by 55.53%. The simulation results confirmed that the proposed trajectory planning and tracking control system could effectively perform the automated driving behaviors for ATVs.

Robust adaptive sliding mode control for path tracking of unmanned agricultural vehicles

In this paper, a novel adaptive sliding mode control (ASMC) for path tracking of unmanned agricultural vehicles is proposed. It is shown that the designed ASMC is capable of driving the closed-loop lateral position error dynamics to converge to zero asymptotically, where the sliding mode observer (SMO) is designed to estimate the system state so as to adaptively adjust the uncertain bounds of cornering stiffness. Compared with the classical sliding mode control technique, the proposed ASMC not only guarantees strong robustness with respect to parameter uncertainties, disturbance and varying road conditions, but also requires no prior knowledge of the road information. Simulation and experimental results from a typical agricultural wheeled vehicle are provided to verify the excellent performance of the proposed scheme.

Performance Comparison of Three Rival AI-Powered Intelligent Trajectory Tracking Controllers for an Autonomous Delivery Van

Due to the advent of E-commerce businesses, delivery vehicle traffic has grown by 71% over the last two decades. Hence, the automation of vans has received special attention to improve road safety. This study compares the performance of a trajectory tracking controller powered by three rival AI-based techniques namely, Particle Swarm, Genetic Algorithm and Ant Colony. Through scenario-based testing, all three techniques are compared from the tracking performance, algorithm complexity, accuracy and precision, and simplicity in finding the optimal weighting factors of the lateral position error and heading angle error which define the desired lateral controller output. Taken individual performance, ant colony powered fuzzy tracking controller was superior in terms of computational time, accuracy, and precision whereas, poor in ease of programming and explainability.

Hybrid Cost Function Distributed MPC for Vehicle Platoons With Stability and String Stability Properties

Distributed MPC schemes for control of vehicle platoons typically employ a p-norms based cost function to achieve stability and string stability. Quadratic cost functions yield smoother trajectories, but they are not aligned with string stability conditions. Hence, in this paper, we develop distributed MPC controllers for vehicle platoons based on a hybrid cost function, which combines infinity norms and quadratic forms. Sufficient conditions for global platoon stability and leader-follower string stability are derived for the developed hybrid cost function and applied to lateral dynamics control. Simulation results show that the hybrid cost function yields lateral position errors that are 10 times smaller (for the maximum error) compared with an infinity norms based cost function.

A localization method for subsea pipeline based on active magnetization

Accurate location of subsea pipelines is a prerequisite for real-time tracking and detailed inspections by underwater robots. The magnetic anomalies generated by ferromagnetic pipelines can be used to locate both exposed and buried pipelines. However, due to the low signal ratio and model inconsistencies under weak and variable ambient magnetization, there is currently no intuitive and reliable pipeline detection method for pipeline tracking. This paper proposes a method capable of immediately and accurately locating pipelines via active magnetization and vertical magnetic measurements. Finite element simulations show that a magnet array can significantly enhance the magnetic anomaly, and that the vertical magnetic component alone can accurately indicate the pipeline’s position, avoiding the inconvenience of magnetic three-component alignment in the field. It is experimentally demonstrated that the magnetic detection signal-to-noise ratio can be significantly increased by 5 dB–20 dB for a Φ219 mm steel pipe using the magnet array, and the maximum lateral positioning error is 0.03 m and much smaller than that without the magnet array.

Achieving High-Resolution Hard X-ray Microscopy using Monolithic 2D Multilayer Laue Lenses

Abstract: This article introduces the 2D multilayer Laue lens (MLL) nanofocusing optics recently developed for high-resolution hard X-ray microscopy. The new optics utilized a micro-electro-mechanical-system (MEMS)-based template to accommodate two linear MLL optics in a pre-aligned configuration. Angular misalignment between the two lenses was controlled in tens of millidegrees, and the lateral position error was on a micrometer scale. Using the developed 2D MLLs, an astigmatism-free point focus of approximately 14 nm by 13 nm in horizontal and vertical directions, respectively, at 13.6 keV photon energy was obtained. The success of 2D MLL optics with an approaching 10 nm resolution is a significant step forward for the development of high-resolution hard X-ray microscopy and applications of MLL optics in the hard X-ray community.

Lane-level trajectory reconstruction based on data-fusion

While lane-changing movements are performed on the entire motorway network for various reasons, such as overtaking, their intensity is substantially greater near complex segments, such as weaving areas. Aside from mandatory lane changes, some drivers also conduct lateral maneuvers for cooperation or anticipation near network nodes. Unlike longitudinal driver behavior (car-following models), lateral driver behavior (lane-changing movements) has received fewer research efforts. The scarcity of suitable data resources to analyze these behaviors and movements might be a crucial cause for this research gap. This paper presents a four-step approach for reconstructing and correcting lateral bias in trajectories collected by a commercial traffic information application running on everyday smartphones. The resulting lateral position is accurate enough to allow for identification of the driving lane, and thus, the lane changes. The algorithm's core is built on a data fusion method using trajectory and loop detector data. The evaluation and validation of the proposed algorithm using drones and closed-circuit television (CCTV) data demonstrate that the core of the algorithm can correctly match more than 94% of trajectory and loop detector data. Between each pair of successive detector stations, the lateral position error has been significantly corrected and reduced to less than half the width of a standard lane of motorway networks. As a result, more than 90% of processed trajectory sample points are in the correct lane. The algorithm requires just two calibration parameters, so it is relatively simple to apply to other test networks.

The modularization design and autonomous motion control of a new baby stroller.

The increasing number of newborns has stimulated the infant market. In particular, the baby stroller, serving as an important life partner for both babies and parents, has attracted more attention from society. Stroller design and functionality are of vital importance to babies' physiological and psychological health as well as brain development. Therefore, in this paper, we propose a modularization design method for the novel four-wheeled baby stroller based on the KANO model to ensure the mechanical safety and involve more functionalities. Manual control of the baby stroller requires the rapid response of human motor systems in a completely controlled manner, which could be a potential risk. To enhance the safety and stability of the stroller motion, especially in situations where manual control is hard to achieve (e.g., sharp turns), we propose an autonomous motion control scheme based on model predictive control. Both the modularization design and the motion controller are verified in the MATLAB simulation environment through path tracking tasks. The feasibility is validated by the satisfactory experimental results with lateral position error in a reasonable range and good trajectory smoothness.

Prediction of Clearance Vibration for Intelligent Vehicles Motion Control

Motion control analysis should consider the system’s uncertainty to ensure the intelligent vehicle’s autonomy. The clearance structure of the transmission shaft is modeled as a cantilever beam with double clearance to predict the clearance vibration for mitigating the nonlinearity. Based on the Kelvin–Voigt collision model, a clearance model was developed using time-varying parameters identified by the wavelet transform. Comparing the frequency response functions (FRF) of the initial model with constant parameters and the updated model with time-varying parameters, the experimental results from the updated model indicate that the modal assurance criterion (MAC) is increased by 42.92%, 31.08%, 38.97%, and 50.74% in the first-four order. Cross-signature assurance criteria (CSAC) and cross-signature scale factor (CSF) have been increased by 6.55% and 12.37%. The control method based on the clearance model has been verified. In the case of 120 km/h, compared with model-predictive control (MPC) and sliding mode control (SMC), the peak of the lateral position error was reduced by 35.7% and 14.3%, and the peak of the heading error was reduced by 50% and 15.6%.

ESO-based lateral control for electrical vehicles with unmodeled tire dynamics on uneven road

In this paper, lateral control is investigated for an uneven road driving electrical vehicle subject to unmodeled tire dynamics by an extended state observer (ESO). A bicycle model with unmodeled tire dynamics is used as a lateral system of the vehicle for the lateral control. The ESO based on a generalized super-twisting algorithm is designed to estimate the unmodeled tire dynamics. A nonlinear controller is utilized to track a reference trajectory with lateral position errors and heading errors. Stability on the ESO and the nonlinear controller are analyzed for the lateral system by Lyapunov methods. Experimental results are given to show effectiveness of the proposed control method for electrical vehicles.

Vision-Based Uncertainty-Aware Lane Keeping Strategy Using Deep Reinforcement Learning

Abstract Recent deep learning techniques promise high hopes for self-driving cars while there are still many issues to be addressed such as uncertainties (e.g., extreme weather conditions) in learned models. In this work, for the uncertainty-aware lane keeping, we first propose a convolutional mixture density network (CMDN) model that estimates the lateral position error, the yaw angle error, and their corresponding uncertainties from the camera vision. We then establish a vision-based uncertainty-aware lane keeping strategy in which a high-level reinforcement learning policy hierarchically modulates the reference longitudinal speed as well as the low-level lateral control. Finally, we evaluate the robustness of our strategy against the uncertainties of the learned CMDN model coming from unseen or noisy situations, as compared to the conventional lane keeping strategy without taking into account such uncertainties. Our uncertainty-aware strategy outperformed the conventional lane keeping strategy, without a lane departure in our test scenario during high-uncertainty periods with random occurrences of fog and rain situations on the road. The successfully trained deep reinforcement learning agent slows down the vehicle speed and tries to minimize the lateral error during high uncertainty situations similarly to what human drivers would do in such situations.

Map Matching-Based Driving Lane Recognition for Low-Cost Precise Vehicle Positioning on Highways

This paper proposes a map matching-based driving lane recognition system for low-cost precise vehicle positioning on highways. The proposed method finds the position where the road boundaries detected by a LIDAR sensor are best matched with the road boundaries of the precise digital map and then recognizes the driving lane based on the position. To improve the limitations of the existing Chamfer matching method, this paper proposes the following two methods. The first is a method of generating one candidate position for each lane by using the lane information of the map and the left and right lane offsets obtained from the front camera. Second is a simple matching method that converts the LIDAR and map data into arrays and compares only the lateral distances to measure the matching score. In addition, this paper proposes a method for maintaining sufficient road boundary information in every frame by applying a temporal accumulation to the LIDAR data. In the experiment, the proposed method was quantitatively evaluated using a database acquired from sensors mounted on a real vehicle on highways. The experimental results show that the driving lane recognition rate is 99.56%, the lateral position error is 0.49m, and the processing time is 6.4ms. These results prove that the proposed system provides sufficient accuracy and reliability even in the presence of GPS position errors in various highway environments.

Lateral Control of an Autonomous and Connected Following Vehicle With Limited Preview Information

Lateral control of an autonomous and connected vehicle (ACV), especially in emergency situations, is important from the safety viewpoint. In these situations, the trajectory to be followed by an ACV must either be planned in real-time (e.g., for a possible evasion maneuver if the obstacle to be avoided is detected) or be communicated from its preceding vehicle. Typically, the trajectory information is available to the following ACV in the form of GPS time samples. From the viewpoint of lateral control, the lateral velocity information is not readily available and the feedback structure must reflect this reality. In this paper, we develop a methodology to synthesize a lateral control algorithm for a following ACV in a two-vehicle platoon in two steps: 1) From the limited preview information of the trajectory to be tracked via samples of GPS way points, we estimate the radius of curvature of the trajectory using “least-square” estimation and 2) develop a fixed-structure feedback control scheme for following the predecessor by synthesizing the set of stabilizing gains corresponding to lateral position error, heading error and heading rate error. Numerical simulation and experimental results corroborate the effectiveness of the proposed schemes.

Gaussian Process Online Learning With a Sparse Data Stream

Gaussian processes (GPs) have been exploited for various applications even including online learning. To learn time-varying hyperparameters from an information-limited sparse data stream, we consider the infinite-horizon Gaussian process (IHGP) with a low computational complexity. For example, the IHGP framework could provide efficient GP online learning with a sparse data stream in mobile devices. However, we show that the originally proposed IHGP has difficulty in learning time-varying hyperparameters online from the sparse data stream due to the numerically approximated gradient of the marginal likelihood function. In this paper, we show how to extend the IHGP in order to learn time-varying hyperparameters using a sparse and non-stationary data stream. In particular, our solution approach offers the exact gradient as the solution of a Lyapunov equation. Therefore, our approach achieves better performance with a sparse data stream while still keeping the computational complexity low. Finally, we present the comparison results to demonstrate that our approach outperforms the originally proposed IHGP on practical applications with sparse data streams. To demonstrate the effectiveness of our approach, we consider a multi-rate sensor fusion or an interpolation problem where slow vision systems need to be combined with other fast sensory units for feedback control in the field of autonomous driving. In particular, we apply our approach to vehicle lateral position error estimation together with a deep learning model for autonomous driving using non-stationary lateral position error signals in a model-free and data-driven fashion.

A robust lateral tracking control strategy for autonomous driving vehicles

A robust steering torque control strategy for lateral tracking functionality of autonomous driving vehicles that perform active steering, active accelerating and active braking is researched in this paper. The main target of this research is to track references of lateral position and heading angle, which are provided by upstream motion planning module, via torque that robotic arm applies to steering hand wheel. Firstly, a system with tracking errors is generated and analyzed. To keep control system’s robustness against time varying parameters, such as speed, center of mass, etc., gain-scheduling approach is utilized to obtain proper feedback gain. To achieve performance robustness, methods of linear matrix inequalities are used in design to maintain a performance function. Control performance is validated in Hardware in the loop environment built by ETAS labcar and Matlab/Simulink for a lane changing scenario and two typical parking scenarios. Results show that the proposed control strategy can regulate vehicle to follow target trajectory precisely even when speed varies. In lane changing scenario, steady state error is close to zero and maximum lateral position error is about ±30 cm. In parking scenarios, tracking error of lateral position is about ±3 cm, and of heading angle is about ±0.1 rad at end points. In addition, in comparison with linear quadratic regulator (LQR) and model predictive control (MPC), the proposed control outperformances in the three test scenarios.