Vehicle State Research Articles

Long-range LBL underwater acoustic navigation considering Earth curvature and Doppler effect

The accuracy of underwater acoustic navigation is significantly influenced by geometry configuration and systematic errors coming from geophysical environment. Long baseline (LBL) systems exhibit superior positioning precision than other acoustic navigation systems, primarily attributable to the better Geometric Dilution of Precision (GDOP). However, the geometry configuration tends to degrade when the underwater vehicle is far away from the seafloor beacons. Factors such as acoustic ray bending error, Doppler effect and Earth curvature may seriously affect the navigation accuracy. To address these issues, we present a robust Kalman filter with systematic error compensation for long-range LBL systems. Firstly, a reversed acoustic ray tracking method is proposed for a unique phenomenon in acoustic wave propagation. Following that, we construct an acoustic positioning model with a time bias parameter, to weaken the impact of Doppler effect on acoustic ranging. This additional parameter was estimated with the state of the user vehicle in real time. At last, an observation equation of pressure gauge considering Earth curvature is presented for depth constraint. Long-range LBL experiments conducted in the South China Sea is employed to validate the performance of the proposed methods, taking the positioning results of inverted ultra-short baseline (iUSBL) system as reference. The results show that the proposed method outperform traditional method with a decrease of about 60 % in the three-dimensional root mean square (3DRMS) of navigation errors. With mentioned three improvements, the 3DRMS of the long-range LBL system with the longest distance of 20 km from the beacon center is less than 14 m. These findings can provide theoretical basis for other long-range LBL systems.

Research of the main electromagnetic parameters during the operation of an AC charging station for electric vehicles

Problem. The increasing popularity of electric cars worldwide is also seen in Ukraine, leading to a growing need for more charging stations. Studies show that 80% of electric car charging happens at home. This home charging usually occurs either through the standard AC power grid or through dedicated AC charging stations. This raises concerns about the safety of these charging stations and their potential interference with other electrical and electronic devices nearby. Goal. The goal of this work is to determine the main electromagnetic parameters in the connection cable during the operation of an AC electric vehicle charging station. Methodology. To achieve this goal, it is necessary to study the electromagnetic parameters of the charging station connection cable when AC flows through it and to identify the electromagnetic parameters of the interference that occurs during the operation of the AC charging station. Classical electrophysical methods for calculating electric and magnetic fields and methods for determining the parameters of quadrupoles from the theoretical foundations of electrical engineering are used. Results. The main electromagnetic parameters in the connection cable during the operation of the AC electric vehicle charging station have been identified. Formulas for calculating the current strength and magnetic field strength have been obtained. Originality. New formulas for calculating the electromotive force of interference generated during the operation of the electric vehicle charging station, when AC flows through the charging cable, have been developed. Parameters of capacitive and magnetic coupling between two conductors in a common bundle have been identified. Formulas for determining the current induced by these parasitic connections have been obtained. Practical value. Accurately determining and calculating these parameters allows for the design of a charging station that operates reliably over a long period without causing interference with nearby electrical and electronic systems or devices.

Cost-Effective Strategy for the Deployment of Charging Stations for Electric Vehicles Using Flower Pollination Algorithm

Cost-Effective Strategy for the Deployment of Charging Stations for Electric Vehicles Using Flower Pollination Algorithm

Driving assistant using generative AI pre-generated messages in simulator-based driving assessment: A step towards low-cost simulator-based driving assessment

This paper presents a novel approach for a low-cost simulator-based driving assessment system incorporating a speech-based assistant, using pre-generated messages from Generative AI to achieve real-time interaction during the assessment. Simulator-based assessment is a crucial apparatus in the research toolkit for various fields. Traditional assessment approaches, like on-road evaluation, though reliable, can be risky, costly, and inaccessible. Simulator-based assessment using stationary driving simulators offers a safer evaluation and can be tailored to specific needs. However, these simulators are often only available to research-focused institutions due to their cost. To address this issue, our study proposes a system with the aforementioned properties aiming to enhance drivers' situational awareness, and foster positive emotional states, i.e., high valence and medium arousal, while assessing participants to prevent subpar performers from proceeding to the next stages of assessment and/or rehabilitation. In addition, this study introduces the speech-based assistant which provides timely guidance adaptable to the ever-changing context of the driving environment and vehicle state. The study's preliminary outcomes reveal encouraging progress, highlighting improved driving performance and positive emotional states when participants are engaged with the assistant during the assessment.

Fault diagnosis and analysis of automobile capacitive touch display false triggering

Abstract Nowadays, automobile handling is mainly concentrated in the central control display. The use of high-definition and large-size capacitive touch displays has become the mainstream trend. Driving information and entertainment information are displayed on it, and the operation of the electronic control function of the car is generally realized by touching the touch panel of the central control display. As the integration of the central control display becomes higher, the fault of the display needs to be paid more attention to. The common faults include display faults and touch faults. The display fault will affect the display of vehicle information, and the driver cannot obtain vehicle status information; the touch fault will affect the car operation and can not carry out human-machine interaction. Therefore, once the central control display fails, it will affect the safety driving of the car. This paper studies the false triggering failure which is a kind of touch fault of the vehicle’s 9-inch capacitive touch display. Firstly, we test the parasitic capacitance of faulty parts and repeat the touch failure. After testing and analyzing, it has been found that the mechanical structure design of the display and the air gap bonding process can change the value of parasitic capacitance which leads to false triggering. This paper proposes solutions to solve this failure which are changing the wideness of glue to avoid glue overflow and strengthening size management to improve the spacing between the touch panel and the TFT display screen. After the implementation of the solutions, the false triggering failure no longer occurs, which proves that the solutions are effective and feasible.

A United Architecture For AES/Present Ciphers and Its Usage in a Soc Environment

Abstract: In order to monitor the operating status of vehicles, it is necessary to collect vehicle operating data in real time through IoT devices and analyze these data. However, the collected data has the characteristics of multi-source heterogeneous, network resources are limited and server performance is poor. It is difficult to truly realize data processing in real time. In addition, data needs to be transmitted over the network, it is particularly important to ensure the safety of data transmission. Considering the above problems, it is necessary to structure the data and define a unified data format to facilitate data transmission and analysis. At the same time, improve the server communication program and improve the server's concurrent processing capabilities. In addition, considering that data needs to be transmitted over the network, in order to ensure that the data is not stolen or tampered with, the PRESENT lightweight encryption algorithm is adopted to ensure the safety of data transmission. Compared with encryption algorithms such as AES, this algorithm has much lower hardware requirements. This article combines the characteristics of the project and uses the number of communications between the device and the server to achieve the dynamic key update which is approximately one-time pad, and greatly improves the security of the data.

Development of Ground Control Station for Unmanned Aerial Vehicles

Unmanned aerial vehicles (UAVs) have emerged as important tools in various industries, from military research to civilian applications such as agriculture, disaster management and research. This paper examines the technological development of GCS and current state-of-the-art developments, including advances in hardware, software, and human-machine interfaces. Through a systematic review of recent research, we identify key policy considerations, performance criteria, and challenges associated with GCS development. In addition, we explore autonomy, artificial intelligence integration, cybersecurity, interoperability standards, and other development trends that are changing the UAV industry landscape. Combining insights from academic literature, industry reports, and technology developments, this review provides a comprehensive understanding and new idea of the state of GCS technology and valuable perspectives on future research directions and practical applications in UAV systems.

Comparison of KF-Based Vehicle Sideslip Estimation Logics with Increasing Complexity for a Passenger Car.

Nowadays, control is pervasive in vehicles, and a full and accurate knowledge of vehicle states is crucial to guarantee safety levels and support the development of Advanced Driver-Assistance Systems (ADASs). In this scenario, real-time monitoring of the vehicle sideslip angle becomes fundamental, and various virtual sensing techniques based on both vehicle dynamics models and data-driven methods are widely presented in the literature. Given the need for on-board embedded device solutions in autonomous vehicles, it is mandatory to find the correct balance between estimation accuracy and the computational burden required. This work mainly presents different physical KF-based methodologies and proposes both mathematical and graphical analysis to explore the effectiveness of these solutions, all employing equal tire and vehicle simplified models. For this purpose, results are compared with accurate sensor acquisition provided by the on-track campaign on passenger vehicles; moreover, to truthfully represent the possibility of using such virtual sensing techniques in real-world scenarios, the vehicle is also equipped with low-end sensors that provide information to all the employed observers.

Trajectory planning and tracking of dynamic lane change for autonomous buses considering vehicle stability in dynamic traffic scenarios

Trajectory planning and tracking of lane change are critical technologies for autonomous buses. Characteristics of the buses susceptible to stability problems resulting from the high height, large passenger capacity and long lengths, coupling the dynamic traffic with the dynamic changes in the states of adjacent vehicles and road adhesion coefficient, put forward great challenges in lane change for autonomous buses (ABs). To cope with the foregoing challenges, a framework is proposed to achieve the trajectory planning and tracking of dynamic lane change for ABs. For trajectory planning approach, the trajectory planning and replanning is optimized in the safe range of longitudinal length of the lane-changing trajectory to obtain the real-time reference trajectory, combining consideration of vehicle yaw, roll stability and lane-changing efficiency. The minimum longitudinal length of lane-changing trajectory determined by the yaw stability and roll stability of ABs, combined with the maximum length formed by the adjacent vehicles with dynamic states, form the real-time safe range for lane-changing trajectory planning. For trajectory tracking approach, a tracking approach using model predictive control based on multipoint preview is proposed to achieve the real-time planned trajectory tracking considering buses stability. The effectiveness of the proposed strategy is evaluated by simulating an experimentally validated Trucksim model in different dynamic traffic scenarios to demonstrate the capability of the strategy in trajectory planning and tracking, and guaranteeing vehicle stability for dynamic lane change of ABs.

Research on multi-vehicle formation control based on improved artificial potential field method

Multi-vehicle formation can perform various special tasks in unstructured environment. How to take into account the safety of vehicles in avoiding obstacles and the ability to maintain formation has a certain research value. In this paper, the four-circle model of vehicle is established first, and the circle radius is adjusted according to the state of vehicle, so as to describe the safety boundary of vehicle. The improved RRT algorithm is used for the whole route planning, and the discrete path points are used as vehicle guidance. Then the artificial potential field is constructed, and the formation coordination potential field is proposed, so that the vehicles can cooperate with other vehicles to keep the preset formation as far as possible when avoiding obstacles. Then the control quantity of the vehicle is calculated according to the force condition of the vehicle in the potential field by the double exponential sliding mode control method. Finally, the effectiveness of the method is verified by the simulation experiments of triangle formation and circular formation under different working conditions, and the formation error is reduced by about 20%.

Controllable region analysis of active front steering and differential braking system stability control characterized by additional yaw moment

Active Front Steering (AFS) and Differential Braking System (DBS) are commonly used execution systems for vehicle anti-rollover or handling stability control. They regulate the vehicle’s yaw moment by adjusting the lateral tire force and longitudinal tire force, respectively. However, these two systems differ in terms of control capabilities and regulatory sensitivities. In emergency conditions, it becomes crucial to allocate and coordinate the tasks for the execution systems according to their yaw moment control capability and adjustment sensitivity. This paper proposes a method for calculating the controllable region represented by the additional yaw moment. This method allows for the computation of the control capability of AFS and DBS based on the current state. Additionally, the influence of different vehicle states on the controllable regions of the two execution systems is analyzed. Building upon this analysis, the yaw moment adjustment sensitivities of AFS and DBS systems under the current state are further investigated. Finally, a task coordinated strategy between AFS and DBS is developed based on the analysis of controllable regions and sensitivities of different execution systems. Simulink simulations are conducted on yaw stability and roll stability conditions to comprehensively analyze the working process of the proposed task coordination strategy.

The Material and Energy Conversion Potential from Municipal Waste in Padang City: Business Chance and Future Prospective

Padang City, the capital city of West Sumatra Province, is expected to grow and reach a population of 1 million by 2035. However, this growth also means an increase in municipal solid waste, projected to be about 256,000 tons annually. At the same time, there is potential for waste-to-energy conversion in Padang City due to the demand for renewable energy and business opportunities. This study is based on existing literature and data, which were analyzed using a statistical approach and municipal solid waste management calculations. The study conducted scenarios that show potential energy conversions. The first scenario involves converting solid waste to gas and using it directly as biogas. The second scenario involves converting biogas from solid waste to electricity and distributing it for household use. The last scenario consists of using small and medium-sized biodigesters to convert gas into electricity, which will then be used as a charging station for electric vehicles (EVs). The first scenario can generate the most significant profit, while the third scenario suits small and medium businesses. However, each scenario has its pros and cons. Still, it's crucial to consider regulation issues, government support, and public awareness of the environmental problems to implement these waste-to-energy conversion practices on-site successfully.

The Cooperative Maximal Covering Location Problem with ordered partial attractions

The Maximal Covering Location Problem (MCLP) is a classical location problem where a company maximizes the demand covered by placing a given number of facilities, and each demand node is covered if the closest facility is within a predetermined radius. In the cooperative version of the problem (CMCLP), it is assumed that the facilities of the decision maker act cooperatively to increase the customers’ attraction towards the company. In this sense, a demand node is covered if the aggregated partial attractions (or partial coverings) of open facilities exceed a threshold. In this work, we generalize the CMCLP introducing an Ordered Median function (OMf), a function that assigns importance weights to the sorted partial attractions of each customer and then aggregates the weighted attractions to provide the total level of attraction. We name this problem the Ordered Cooperative Maximum Covering Location Problem (OCMCLP). The OMf serves as a means to compute the total attraction of each customer to the company as an aggregation of ordered partial attractions and constitutes a unifying framework for CMCLP models.We introduce a multiperiod stochastic non-linear formulation for the CMCLP with an embedded assignment problem characterizing the ordered cooperative covering. For this model, two exact solution approaches are presented: a MILP reformulation with valid inequalities and an effective approach based on Generalized Benders’ cuts. Extensive computational experiments are provided to test our results with randomly generated data and the problem is illustrated with a case study of locating charging stations for electric vehicles in the city of Trois-Rivières, Québec (Canada).

Twin-in-the-loop state estimation for vehicle dynamics control: Theory and experiments

In vehicle dynamics control, many variables of interest cannot be directly measured, as sensors might be costly, fragile or even not available. Therefore, real-time estimation techniques need to be used. The previous approach suffers from two main drawbacks: (i) the approximations due to model mismatch might jeopardize the performance of the final estimation-based control; (ii) each new estimator requires the calibration from scratch of a dedicated model. In this paper, we propose a twin-in-the-loop scheme, where the ad-hoc model is replaced by an accurate full-fledged simulator of the vehicle, typically available to vehicles manufacturers and suitable for the estimation of any on-board variable, coupled with a compensator within a closed-loop observer scheme. Given the black-box nature of the digital twin, a data-driven methodology for observer tuning is developed, based on Bayesian optimization. The effectiveness of the proposed estimation method for the estimation of vehicle states and forces, as compared to traditional model-based Kalman filtering, is experimentally shown on a dataset collected with a sport car. In summary, the proposed approach achieves, in the most aggressive driving scenarios, an average improvement of 0.5° for the side-slip angle estimation, and of more than 500 N for the front lateral forces estimation.

A method based on Vision Transformer and multiple image information for vehicle lane-changing recognition in mixed traffic and connected environment

ABSTRACT To enhance the safety of autonomous vehicles in mixed traffic and connected environment, it is crucial to recognize the lane-changing intentions (LCIs) of human-driven vehicles for autonomous vehicles. This paper presents a novel method for LCI recognition, which extracts features from the driving state and relative motion of the target vehicle and its neighbors. The method applies short-time Fourier transform, Gramian angular summation field, and Gramian angular difference field to the time-series data, and generates three grayscale images, which are merged into one information fusion image (IFI) by image processing techniques. The IFIs are then classified into three categories: lane keeping, lane-changing left, and lane-changing right, using the Vision Transformer model with transfer learning to speed up convergence and reduce training cost. The experimental results demonstrate that the proposed method outperforms the traditional methods, achieving an accuracy of 95.65% for recognizing LCI 3s before the lane change point.

A preliminary study to identify critical factors for evaluating the effect of car-lock sounds on drivers

Car-lock sounds are designed to inform the lock status of vehicles. However, drivers often experience a lack of confidence regarding whether the car is locked, and car thefts persistently occur, frequently attributed to unlocked doors. Without identification of critical factors for evaluating effects of car-lock sounds on drivers, a strategy to car-lock sound design with increased locking efficiency remains implicit. This study proposes a method to identify critical factors influencing drivers’ perceived certainty of car-lock status and behaviours during car-locking. An experiment was conducted to simulate the locking process and verbal protocol analysis was employed to comprehend participants’ cognitive processes and behaviours. The results show that mechanical sound yielded high certainty and few hesitations, while tonal and crisp sound elicited low certainty and frequent hesitations. Seven critical factors on participants’ behaviours and cognitive processes were identified, which provides a data-driven approach for future research in car-lock sounds evaluation and design.

The Control of Handling Stability for Four-Wheel Steering Distributed Drive Electric Vehicles Based on a Phase Plane Analysis

For the sake of enhancing the handling and stability of distributed drive electric vehicles (DDEVs) under four-wheel steering (4WS) conditions, this study proposes a novel hierarchical control strategy based on a phase plane analysis. This approach involves a meticulous comparison of the stable region in the phase plane to thoroughly analyze the intricate influence of the front wheel angle, rear wheel angle, road adhesion coefficient, and longitudinal speed on the complex dynamic performances of DDEVs and to accurately determine the critical stable-state parameter. Subsequently, a hierarchical control strategy is presented as an integrated solution to achieve the coordinated control of maneuverability and stability. On the upper control level, a model predictive control (MPC) motion controller is developed, wherein the real-time adjustment of the control weight matrix is ingeniously achieved by incorporating the crucial vehicle stable-state parameter. The lower control level is responsible for the optimal torque allocation among the four wheel motors to minimize the tire load rate, thereby ensuring a sufficient tire grip margin. The optimal torque distribution for the four wheel motors is achieved using a sophisticated two-level allocation algorithm, wherein the friction ellipse is employed as a judgement condition. Finally, this developed control strategy is thoroughly validated through co-simulation utilizing the CarSim 2019 and Simulink 2020b commercial software, demonstrating the validity of the developed control strategy. The comparative results indicate that the presented controller ensures a better tracking capability to the desired vehicle state while exhibiting improved handling stability under both the double lane shifting condition and the serpentine working condition.

Leveraging Machine Learning to Improve Adaptive Primitive-Based Motion Planning

This paper introduces a new approach for adding replanning capabilities to the maneuver automaton. We call this approach “maneuver interruption.” Maneuver interruption enables replanning by identifying maneuver segments that are dynamically similar to the current vehicle state. As a result, the vehicle can exit a maneuver if new information emerges or the environment changes. We use machine learning to enhance the performance of maneuver interruption. Specifically, we examine how supervised learning can predict dynamic similarity and utilize the learned network to enable maneuver interruption. A variety of models are compared for their ability to quantify the feasibility of a maneuver-to-maneuver transition. The multilayer perceptron is found to be the most effective at this task and was therefore selected for generating maneuver-to-maneuver transitions for replanning. Additionally, we use Monte Carlo methods and pruning to reduce the transition library size by an order of magnitude with minimal loss in performance. We test learning-enhanced maneuver interruption on obstacle evasion tasks with a medium-fidelity ZOHD Drift flight dynamics model. On randomly generated obstacle fields, maneuver interruption is demonstrated to enable longer collision-free flights at a minor cost to control performance.

Toward just and equitable mobility: Socioeconomic and perceptual barriers for electric vehicles and charging infrastructure in the United States

Based on a large-scale public survey, we identify and quantify the significance of key factors associated with the deployment of electric vehicles and charging infrastructure. Our results indicate that individual characteristics, such as income, age, region, and single vs. multi-family housing type can significantly affect electric vehicle purchase preferences, especially those concerning overnight charging and perceptions of benefits and barriers. Moreover, our results challenge earlier findings in the literature by showing how certain elements, such as expected electric driving range, certain travel behaviors (e.g., driving distance, destination types), the most common perceived benefits (e.g., cleaner air) or barriers (e.g., reliability concerns), and preferred location for public charging seem to not vary much or at all with the socioeconomic, demographic, and geographical variables examined in this study. We conclude with the implications for policies to advance equitable vehicle electrification. Our findings underscore the importance of lower-cost models of electric vehicles, home and public charging access, charging infrastructure planning, more integrated analysis of interlinked housing and transportation needs and solutions, the availability of alternative transportation modes, and the potential role of gas stations for electric vehicles. We encourage others to build on these results and have shared our complete survey instrument as an added contribution.

Comprehensive Chassis Control Strategy Based on Coordination of Traction/Braking Distribution and Active Roll Control

A coordinated control of front/rear traction, four-wheel independent braking, and an active roll control system is proposed in this paper to improve overall vehicle cornering performance. The control algorithm is structured in a hierarchical form: the supervisory controller is used to calculate the desired yaw rate and roll angle based on the driver input and vehicle states; the upper-level controller is utilized to decide the target longitudinal tire forces, yaw moment, and anti-roll moment based on the sliding mode control method; and the control allocator is used to optimally map the virtual control input to the control commands of specific actuators. Co-simulations of MATLAB/Simulink and CarSim were conducted to verify the performance enhancement of the proposed controller. The results suggest that the proposed controller can effectively enhance the driving performance in limit cornering while maintaining lateral stability, and it reduces tire usage, as indicated by the index of tire dissipation energy when compared to control methods such as AWD, AWD-ESC, and ICC.