Vehicle Position Research Articles

Dynamic event-triggered prescribed performance disturbance rejection fault-tolerant trajectory tracking for surface vehicles

In this article, a dynamic event-triggered prescribed performance disturbance rejection fault-tolerant scheme is designed for the trajectory tracking of surface vehicles. The external time-varying disturbances and loss-of-effectiveness thruster faults are addressed separately. By constructing the separate observers, the external time-varying disturbances and the loss-of-effectiveness thruster faults are on-line estimated, respectively. The prescribed performance and error transformation functions enable the transient and steady-state performance to settle within pre-specified function values. The dynamic event-triggering is introduced so that commanded control signals are temporarily kept under predefined conditions to reduce wear and tear on the thrusters especially in harsh environmental condition. With the aid of the vectorial backstepping technique, the finial control law is derived such that the position and heading of vehicles can be forced to track the reference trajectories in disturbance and fault conditions. Application example on a 1:70 scaled model vessel in the different cases clarify the scheme.

Real-time localization with probabilistic maps and unscented kalman filtering: a dynamic sensor fusion approach

This paper presents a robust position estimation technique for autonomous vehicles navigating urban and highway environments. It employs a probabilistic framework, integrating Monte Carlo simulation and Bayesian filtering via particle filters to represent position estimates beyond Gaussian distributions. To improve accuracy, Unscented Kalman Filtering (UKF) is used to fuse radar and lidar data, facilitating the detection of static, pole-like objects. These objects are matched with landmarks from a detailed reference map generated through 3D lidar scans. The proposed method addresses both lateral and longitudinal localisation challenges, ensuring precise vehicle positioning. Comprehensive simulation tests validate the system's effectiveness, demonstrating reliable performance across various driving conditions.

Extracting terrain elevation information in front of the vehicle based on vehicle-mounted LiDAR in dynamic environments

Abstract Point cloud maps constructed using 3D LiDAR, are widely used for robot navigation and localization. Few studies have utilized point cloud maps to extract terrain elevationinformation in front of a vehicle, which can be used as active suspension inputs to reduce vehicle bumps. In addition, the trajectories of dynamic objects in point cloud maps and global navigation satellite system (GNSS) data loss can affect the extraction of elevation information. To solve these problems, this paper proposes a framework for extracting terrain elevation information in front of the vehicle based on vehicle-mounted LiDAR in dynamic environments. The framework consists of two modules: point cloud map construction and vehicle front terrain elevation information extraction. In the point cloud map construction module, a system for simultaneous localization and mapping (SLAM) is proposed, which is capable of building point cloud maps without GNSS. Furthermore, a dynamic descriptor-based dynamic object filtering algorithm is proposed which is applied to SLAM. Therefore, the SLAM system overcomes the influence of dynamic objects on vehicle position and attitude estimation, and there are no trajectories of dynamic objects in the point cloud maps built by the system. In the vehicle front terrain elevation information extraction module, the unscented Kalman filter is utilized to predict the vehicle position at the next moment. Based on the geometric features of the tire-ground contact area, the terrain elevation information of the tire contact area at the predicted position on the point cloud map is extracted. Experiments show that the algorithm in this paper overcomes the effect of dynamic objects and builds a vehicle point cloud map without dynamic objects under GNSS data loss, which improves the accuracy of the extraction of terrain elevation information in front of the vehicle.

Probabilistic Risk Assessment for Data Rate Maximization in Unmanned Aerial Vehicle-Assisted Wireless Networks

The evolution of beyond fifth generation (B5G) wireless networks poses significant technical and economic challenges across urban, suburban, and rural areas, demanding increased capacity for users whose positions continually change. This study investigated the dynamic positioning of an unmanned aerial vehicle (UAV), acting as a mobile base station (MoBS) to enhance network efficiency and meet ground terminals (GTs) expectations for data rates, particularly in emergency scenarios or temporary events. While UAVs show great promise, existing research often assumes certainty in network architecture, overlooking the complexities of unpredictable user movements. We introduce a decision-making framework utilizing the ordered weighted averaging (OWA) operator to address uncertainties in GT locations, enabling the optimization of UAV trajectories to maximize the overall network data rate. An optimization problem is formulated by modeling GT dynamics through a Markov process and discretizing UAV movements while accounting for communication thresholds and movement constraints. Extensive simulations reveal that our approach significantly improves expected data rates by up to 48% compared to traditional fixed base stations (BSs) and predefined UAV movement patterns. This research underscores the potential of UAV-assisted networks to bolster communication reliability while effectively managing dynamic user movements to maintain optimal quality of service (QoS).

Utilizing Photovoltaic Solar Panels for Real‐Time Localization and Speed Detection of Approaching Illuminated Objects in Humanoid Robots

ABSTRACTIn the field of humanoid robotics, this paper showcases a promising method of integrating the photovoltaic (PV) solar panels into the “GUCnoid 1.0” humanoid robot model. Instead of the conventional power generation, solar cells are used to supply electrical energy to various sensors to create a system capable of real‐time sensing and perception. The main focus of the study will be the use of PV panels as receptors that are capable of detecting the light, enabling an adaptive system which perceives environmental changes. The behavior of the PV cell is superbly studied when various light sources are approached or depart. They finally reveal unique patterns in the voltage output signal amplitude. Interestingly, these patterns figure out the same symmetric structure, which reflects on a vertical axis by their mirroring. Using this simplicity, the method involves using an artificial neural network that is able to distinguish the light sources coming towards the detector and the ones running away and the rate at which they approach/recede. Outdoor experiment was organized for verification of methods. GUCnoid 1.0— humanoid robot was placed in front of a moving vehicle with different speeds of approach. To be able to identify the vehicle's position and velocity, a PV sensing technique has to be applied. This innovative technology will have wide applications, with much attention paid to improving the speed of finding nearby objects or vehicles in the scenarios where quick detection is a serious life safety issue. Through the process of PV solar panels' smart sensing, we directly connect the areas of high‐tech robotics and renewable energy. This discovery creates an opportunity for companies to build more responsive and flexible humanoid robots that can effectively collaborate with humans to achieve greater outcomes through more secure and efficient interactions between humans and robots.

A nonlinear model predictive control for air suspension of hub motor electric vehicle based on hybrid H2/H∞ state estimation

To alleviate the vibration from the road irregularities and the unbalanced electromagnetic forces (UEFs), deteriorating the dynamic performance of hub motor electric vehicles (HM-EVs), a novel nonlinear model predictive control (NMPC) is proposed based on a hybrid H2/H ∞ filtering algorithm for the HM-EVs with air suspension (HM-AS). The dynamic HM-AS model involves the electric vehicle’s longitudinal, lateral, and vertical motion. The air suspension system between the body and the hub motor enhances ride quality and insulates the oscillation delivered to the vehicle body. Then, the dynamic HM-AS model is validated by the actual vehicle test, and the proposed filter is developed to obtain the vehicle states. Besides, the NMPC controller guarantees the dynamic performance of HM-AS by intervening in vehicle attitude, road-holding stability, motor safety, and ride comfort. Finally, compared with the application of PID and MPC, the efficiency of the proposed method is demonstrated based on several random road scenarios. The results indicate the proposed algorithm can alleviate the dynamic performance of HM-AS and reduce motor vibration.

Characteristics analysis and safety evaluation of vehicles turning over long distances

In order to explore the motion and safety characteristics of the U-turn vehicles at long-range U-turn intersections, based on the measured data of the U-turn vehicles at typical locations, we analyzed the characteristics of the U-turn vehicle's trajectories and traffic parameters with the help of probability theory and statistical knowledge, and established the U-turn trajectory model. We used Traffic Conflict Technology (TCT) and take Post Encroachment Time (PET) as the indicator to study the feature of vehicles security of - turn vehicles. Three conclusions are drawn from this study. First, there is a significant difference in the headway between long-range -turn vehicles and those at intersections without opening: the stability of the headway of long-range -turn vehicles is weak, and the release of vehicles is seriously disturbed by the turning flow; Secondly, when turning around at a long interval u-turn intersection, the starting position of vehicle turning is dispersed and the distribution of U-turn trajectories is quadratic distribution. Third, the setting of long interval turning lanes will increase the interference between traffic flows at intersections, thus increasing the number of conflicts with vehicles in the opposite direction. Cross-conflict accounts for the majority of conflicts in long interval U-turn intersections, and rear-end collision is the main conflict type in U-turn intersections without opening. The above research conclusions are helpful to improve the safety of long interval U-turn intersections.

Vehicle posture wheelbase preview control of in-wheel motors drive intelligent vehicle on potholed road

The in-wheel motors drive intelligent vehicle (IMDIV) has a strong ability of dynamic control, which makes it be a major development path of intelligent vehicles in the future. However, its stability is easily deteriorated by the impact of the road when driving on potholed road. To tackle this issue, a vehicle posture control method based on road feedback and elevation recognition is proposed. Firstly, an elevation recognition algorithm of adaptive Kalman filter (AKF) based on vehicle dynamic response is proposed after considering the flooded road and the system noise uncertainty. Secondly, a vehicle posture controller of fuzzy active disturbance rejection control (FADRC) is designed, with the function of dynamically adjusting the active disturbance rejection control (ADRC) parameters. It is designed to implement front suspension feedback control and rear suspension wheelbase preview control. At last, the road elevation recognition algorithm and vehicle posture control method are proved by Simulink simulation and off-line hardware in the loop test. The results revealed that the AKF can effectively recognize both white noise pavement and potholed road, and the recognition accuracy is greater than 90%. The FADRC controller realizes accurate front suspension feedback control and rear suspension wheelbase preview control by adjusting the real-time online parameters of ADRC. The proposed vehicle posture control method can effectively control the pitch and roll motion, which significantly improves the stability of IMDIV on potholed road. This study can serve as a reference for the posture stability control of IMDIV on potholed road.

High precision DSRC and LiDAR data integration positioning method for autonomous vehicles based on CNN

In order to improve the safe driving and automatic positioning capability of autonomous vehicles, a high-precision DSRC and LiDAR data integration positioning technology for autonomous vehicles based on CNN is proposed. Import the data of Dedicated Short Range Communications and Light Detection and Ranging for automatic driving vehicle positioning, carry out kinematic analysis of autonomous driving vehicles under multi-sensor fusion, and transform the data of DSRC and LiDAR sensors into tightly coupled coordinate systems; The CNN depth learning method is used to compensate the position and attitude tracking estimation error under the overall time stamp synchronization of the sensor through adaptive information tracking; The first and second order feedforward compensation is made for the positioning parameters of the autonomous driving vehicle using the PID model, and the point cloud feature matching model is fused to complete the estimation of the positioning attitude parameters of the autonomous driving vehicle. In order to eliminate the noise interference under the DSRC communication mechanism, the Kalman filter function is used to automatically optimize the constraint parameters in the point cloud feature detection model, and the positioning error parameters are dynamically filtered and adjusted; Kinematics analysis is carried out for the driving state of the vehicle, and the positioning error in the vehicle movement is controlled through the difference technology to achieve high-precision DSRC and LiDAR data integration positioning. The simulation results show that this method can integrate and locate the high-precision DSRC and LiDAR data of the autonomous vehicle, and the attitude estimation and positioning accuracy of the vehicle is good, while the error of the attitude parameter estimation of the autonomous vehicle is low.

MMD-TSC: An Adaptive Multi-Objective Traffic Signal Control for Energy Saving with Traffic Efficiency

Reducing traffic energy consumption is crucial for smart cities, and vehicle carbon emissions are a key energy indicator. Traffic signal control (TSC) is a useful method because it can affect the energy consumption of vehicles on the road by controlling the stop-and-go of vehicles at traffic intersections. However, setting traffic signals to reduce energy consumption will affect traffic efficiency and this is not in line with traffic management objectives. Current studies adopt multi-objective optimization methods with high traffic efficiency and low carbon emissions to solve this problem. However, most methods use static weights, which cannot adapt to complex and dynamic traffic states, resulting in non-optimal performance. Current energy indicators for urban transportation often fail to consider passenger fairness. This fairness is significant because the purpose of urban transportation is to serve people’s mobility needs not vehicles. Therefore, this paper proposes Multi-objective Adaptive Meta-DQN TSC (MMD-TSC), which introduces a dynamic weight adaptation mechanism to simultaneously optimize traffic efficiency and energy saving, and incorporates the per capita carbon emissions as the energy indicator. Firstly, this paper integrates traffic state data such as vehicle positions, velocities, vehicle types, and the number of passengers and incorporates fairness into the energy indicators, using per capita carbon emissions as the target for reducing energy consumption. Then, it proposes MMD-TSC with dynamic weights between energy consumption and traffic efficiency as reward functions. The MMD-TSC model includes two agents, the TSC agent and the weight agent, which are responsible for traffic signal adjustment and weight calculation, respectively. The weights are calculated by a function of traffic states. Finally, the paper describes the design of the MMD-TSC model learning algorithm and uses a SUMO (Simulation of Urban Mobility) v.1.20.0 for traffic simulation. The results show that in non-highly congested traffic states, the MMD-TSC model has higher traffic efficiency and lower energy consumption compared to static multi-objective TSC models and single-objective TSC models, and can adaptively achieve traffic management objectives. Compared with using vehicle average carbon emissions as the energy consumption indicator, using per capita carbon emissions achieves Pareto improvements in traffic efficiency and energy consumption indicators. The energy utilization efficiency of the MMD-TSC model is improved by 35% compared to the fixed-time TSC.

An Aerial Robot Achieving Bimodal Flight and Independent Attitude Control through Passive Morphing

Enhancing the adaptability and versatility of unmanned micro aerial vehicles (MAVs) is crucial for expanding their application range. In this article, a bimodal reconfigurable robot capable of operating in both regular quadcopter flight mode and a unique revolving flight mode is presented, which allows independent control of the vehicle's position and roll‐pitch attitude. This design incorporates passive revolute joints that enable seamless mid‐air transitions between the two modes, facilitated by centrifugal forces, without the need for additional actuators. In the regular mode, the robot operates with four vertical propellers, similar to conventional designs. In the revolving mode, the configuration shifts to two horizontal and two vertical propellers, achieving five degrees of freedom control with four actuators. This underactuated flight capability is made possible by exploiting the robot's fast revolving motion and the resulting cycle‐averaged forces and torques. Experimental validations confirm the feasibility and enhance the maneuverability of this design. The revolving flight mode provides significant advantages in applications such as mapping and confined space navigation.

Adaptive IMU error correction algorithm for dual-antenna GNSS/IMU integrated vehicle attitude determination

Abstract The attitude of a vehicle can be largely determined by integrating the global navigation satellite system (GNSS) and inertial measurement unit (IMU) in land-based applications. However, traditional dual-antenna GNSS/IMU integrated systems are susceptible to signal reflection, diffraction, and even interruption, leading to reduced accuracy and reliability in challenging environments. To address this issue, this paper proposes an adaptive IMU error correction algorithm for dual-antenna GNSS/IMU integrated vehicle attitude determination. First, we propose an IMU-aided baseline length constraint model to support the ambiguity resolution (AR) process. This model incorporates accurate prior information from the IMU, improving the success rate of AR within the dual-antenna GNSS/IMU integrated system. Second, we present an adaptive IMU error correction mechanism based on long short-term memory (LSTM) and particle swarm optimization (PSO) to assist in vehicle attitude determination and mitigate attitude error drift of the lMU during GNSS outages. Field test results verified that, compared to two other candidate algorithms, the proposed algorithm improves accuracy in roll, pitch, and yaw by 19.23%, 30.56%, and 67.12%, respectively, and by 12.50%, 10.71%, and 38.39% respectively. Moreover, in two distinct scenarios where GNSS is blocked for 120 seconds, the proposed algorithm still provides accurate and stable attitude information for vehicle navigation, with the accuracy of roll, pitch, and yaw kept within 0.08 degrees.

Numerical study of vehicle motion during water exit under combined lifting force and wave action

During the retrieval process of the deep-sea mining vehicle (DSMV), the stability of the retrieval system is strongly influenced by the interaction between the vehicle body and the surrounding seawater due to the vehicle's complex shape and wave motion. Naturally, the negative side effects of significant changes in the vehicle's attitude and the water exit position can only increase retrieval's challenge. To investigate the characteristic of the flow field of the DSMV, this study employs the computational fluid dynamics method based on the Reynolds-averaged Navier–Stokes equations integrating the volume-of-fluid multiphase flow model with a fifth-order Stokes-wave model to explore the attitude and displacement changes of the vehicle during the water exit process in the ocean wave environment. The results indicate that the wave phase and lifting force are the major effect factors in the DSMV's water exit process. An appropriate lifting force under a specific wave phase can effectively reduce attitude changes and positional drift of the DSMV during water exit, thereby enhancing recovery efficiency and stability.

Experimental modal identification of a pedestrian bridge through drive-by monitoring integrated with shared-mobility vehicles

In the context of improving resilience in transport infrastructure, an emerging approach of indirect structural health monitoring is gaining attention, known as drive-by monitoring, instrumenting a vehicle with sensors to evaluate the bridges it crosses. However, their effectiveness has predominantly been investigated in ideal scenarios, with actual real-world applications being quite scarce. The research presented in this paper explores the feasibility of combining two routes: (1) fleet emissions reduction and (2) transport resilience enhancement, through drive-by monitoring integrated with shared mobility, including electric mobility scooter and bicycle. The information from drive-by data collected from shared mobility can give valuable information on critical transport infrastructure (e.g., bridges) and such a drive-by database has the potential for network level infrastructure condition assessment. In this paper, two different drive-by roadmaps are investigated subject to the flexibility of the bridges, namely partially or fully drive-by monitoring. To validate the proposed roadmaps, a full-scale pedestrian bridge was chosen for drive-by testing, where smartphone sensors and specialised accelerometers are mounted on shared mobility for data acquisition. Experimental results demonstrate that (i) smartphone sensing can provide data with similar accuracy compared to specialised accelerometers, (ii) bridge frequencies can be easily obtained from temporarily parked shared mobility, with a maximum relative error of 1.05%, (iii) both the bridge frequencies and operational deflection shapes are successfully extracted from the moving shared mobility by using variational mode decomposition and filtering techniques, and shared mobility’s GPS data along with moving speeds are collected for potential vehicle positioning and drive-by database updating.

Practical three-dimensional path following and control for unmanned combat aerial vehicles based on expansion of L1+ guidance logic and disturbance rejection

An effective virtual-target-based three-dimensional path-following algorithm is proposed for unmanned combat aerial vehicles, which extends from the planar [Formula: see text] guidance. The separated implementation of the planar guidance law in two perpendicular planes provides the longitudinally-laterally decoupled acceleration commands. In order to obtain a non-unique virtual-target-point, a numerical iterative method using auxiliary path-related variables is proposed. Moreover, two protection improvements are discussed for real-word implementation: one involves the modification of adaptive [Formula: see text] length for vehicle position far away from the desired path; the other involves a switching strategy for multi-segment paths. The improved algorithm eliminates complex coordinate transformation and easily connects different types of path segments. For the control loop, technology enhanced with the active disturbance rejection control was used to facilitate the design of a bank-to-turn autopilot. For the pitch and roll channels, a rate-damping augmentation loop was used first to transform the original system into a generalized plant. Then the second-order active disturbance rejection control was structured to estimate the total disturbance, including time-varying dynamics, model mismatch, and coupling of channels, making the system an ideal integral-series type. The non-smooth optimization technique in the H-infinity methodology was used to facilitate tuning of the fixed-structure active disturbance rejection control.

A privacy-preserving Self-Supervised Learning-based intrusion detection system for 5G-V2X networks

In light of the ongoing transformation in the automotive industry, driven by the adoption of 5G and the proliferation of connected vehicles, network security has emerged as a critical concern. This is particularly true for the implementation of cutting-edge 5G services such as Network Slicing (NS), Software Defined Networking (SDN), and Multi-access Edge Computing (MEC). As these advanced services become more prevalent, they introduce new vulnerabilities that can be exploited by cyber attackers. Consequently, Network Intrusion Detection Systems (NIDSs) are pivotal in safeguarding vehicular networks against cyber threats. Still, their efficacy hinges on extensive data, which often contains sensitive and confidential information such as vehicle positions and owner’s behaviors, raising privacy concerns. To address this issue, we propose a Privacy-Preserving Self-Supervised Learning (SSL) based Intrusion Detection System for 5G-V2X networks. The majority of works in the literature relying on Federated Learning (FL) and often overlook data labeling on the end devices. Our methodology leverages SSL to pre-train NIDSs using unlabeled data. Post-training is then performed with a minimal amount of labeled data, which can be carefully crafted by an expert. This novel technique allows the training of NIDSs with huge datasets without compromising privacy, consequently enhancing the efficacy of cyber-attack protection. Our innovative SSL pre-training methodology has yielded remarkable results, demonstrating a substantial improvement of up to 9% in accuracy across a diverse range of training dataset sizes, including scenarios with as few as 200 data samples. Our approach highlights the potential to enhance automotive network security significantly, showcasing groundbreaking achievements that set a new standard in the field of automotive cybersecurity.

Bus arrival and departure time updates in the Greater Sydney Area

Public Transport Authorities generate large quantities of data as part of their daily operations, including vehicle positions, arrival times, and dynamic routing options. This information is essential feedback into the system for planning routes and timetables, modelling passenger demand growth and evaluating operational performance. The General Transit Feed Specification (GTFS) is a data format that allows public transport data to be consumed by a wide variety of software applications. There are barriers to widespread consumption of GTFS data related to location-specific data extensions, non-human-readable formats and error cleaning. This paper describes a flexible dataset of actual bus arrivals and departure times created with a pipeline for GTFS realtime feeds designed to address these challenges. The paper describes the pipeline, verifies the quality of the data and presents an output of 25 months of actual bus arrival and departure times for Sydney, Australia. We conclude by discussing relevance to researchers and practitioners of the pipeline outputs in general and the sample data specifically.

Integrated Sensing and New Radio Communications for Air Vehicle Positioning

Integrated Sensing and New Radio Communications for Air Vehicle Positioning

Incremental Sliding Mode Control for Predefined-Time Stability of a Fixed-Wing Electric Vertical Takeoff and Landing Vehicle Attitude Control System

Incremental Sliding Mode Control for Predefined-Time Stability of a Fixed-Wing Electric Vertical Takeoff and Landing Vehicle Attitude Control System

Dynamic Measurement Method for Steering Wheel Angle of Autonomous Agricultural Vehicles

Steering wheel angle is an important and essential parameter of the navigation control of autonomous wheeled vehicles. At present, the combination of rotary angle sensors and four-link mechanisms is the main sensing approach for steering wheel angle with high measurement accuracy, which is widely adopted in autonomous agriculture vehicles. However, in a complex and challenging farmland environment, there are a series of prominent problems such as complicated installation and debugging, spattered mud blocking the parallel four-bar mechanism, breakage of the sensor wire during operation, and separate calibrations for different vehicles. To avoid the above problems, a novel dynamic measurement method for steering wheel angle is presented based on vehicle attitude information and a non-contact attitude sensor. First, the working principle of the proposed measurement method and the effect of zero position error on measurement accuracy and path tracking are analyzed. Then, an optimization algorithm for zero position error of steering wheel angle is proposed. The experimental platform is assembled based on a 2ZG-6DM rice transplanter by software design and hardware modification. Finally, comparative tests are conducted to demonstrate the effectiveness and priority of the proposed dynamic sensing method. Experimental results show that the average absolute error of the straight path is 0.057° and the corresponding standard deviation of the error is 0.483°. The average absolute error of the turning path is 0.686° and the standard deviation of the error is 0.931°. This implies the proposed dynamic sensing method can accurately realize the collection of the steering wheel angle. Compared to the traditional measurement method, the proposed dynamic sensing method greatly improves the measurement reliability of the steering wheel angle and avoids complicated installation and debugging of different vehicles. The separate calibrations for different vehicles are not needed since the proposed measurement method is not dependent on the kinematic models of the vehicles. Given that the attitude sensor can be installed at a higher position on the wheel, sensor damage from mud blocking and the sensor wire breaking is also avoided.