Search Results (23)

A vole foraging-inspired dynamic path-planning method considering slippery road conditions is proposed for wheeled humanoid robots. Glazed and oily roads create a high risk of slipping for wheeled humanoid robots and hinder the realization of high-speed movement. But in a dynamic environment, road conditions such as material, texture, and attachments vary uncertainly in both space and time, and cannot be processed as quickly and easily as moving obstacles. Inspired by the process of voles searching for food, to address this challenge, a slip-risk-assessment method based on time–space decoupling is designed and integrated into a grid-based environmental model. On this basis, the dynamic path-planning model is constructed by combining the cost functions and constraints based on the slip-risk information. A two-level non-periodic cyclical dynamic planning mechanism is proposed based on conditional triggering. It adaptively and cyclically calls the global planning algorithm and the local re-planning algorithm according to the characteristics of environmental changes to autonomously avoid high-slip-risk areas and moving obstacles in real time. The experimental results show the effectiveness and practicality of the proposed planning method. Full article

To enhance the path-tracking accuracy of unmanned articulated road roller (UARR) operating on low-adhesion, slippery surfaces, this paper proposes a hierarchical cascaded control (HCC) architecture integrated with real-time ground adhesion coefficient estimation. Addressing the complex nonlinear dynamics between the two rigid bodies of the vehicle and its interaction with the ground, an upper-layer nonlinear model predictive controller (NMPC) is designed. This layer, based on a 4-degree-of-freedom (4-DOF) dynamic model, calculates the required steering torque using position and heading errors. The lower layer employs a second-order sliding mode controller (SOSMC) to precisely track the steering torque and output the corresponding steering wheel angle. To accommodate the anisotropic and time-varying nature of slippery surfaces, a strong-tracking unscented Kalman filter (ST-UKF) observer is introduced for ground adhesion coefficient estimation. By dynamically adjusting the covariance matrix, the observer reduces reliance on historical data while increasing the weight of new data, significantly improving real-time estimation accuracy. The estimated adhesion coefficient is fed back to the upper-layer NMPC, enhancing the control system’s adaptability and robustness under slippery conditions. The HCC is validated through simulation and real-vehicle experiments and compared with LQR and PID controllers. The results demonstrate that HCC achieves the fastest response time and smallest steady-state error on both dry and slippery gravel soil surfaces. Under slippery conditions, while control performance decreases compared to dry surfaces, incorporating ground adhesion coefficient observation reduces steady-state error by 20.62%. Full article

In recent years, the growing number of vehicles on the road have exacerbated issues related to safety and traffic congestion. However, the advent of the Internet of Vehicles (IoV) holds the potential to transform mobility, enhance traffic management and safety, and create smarter, more interconnected road networks. This paper addresses key road safety concerns, focusing on driver condition detection, vehicle monitoring, and traffic and road management. Specifically, various models proposed in the literature for monitoring the driver’s health and detecting anomalies, drowsiness, and impairment due to alcohol consumption are illustrated. The paper describes vehicle condition monitoring architectures, including diagnostic solutions for identifying anomalies, malfunctions, and instability while driving on slippery or wet roads. It also covers systems for classifying driving style, as well as tire and emissions monitoring. Moreover, the paper provides a detailed overview of the proposed traffic monitoring and management solutions, along with systems for monitoring road and environmental conditions, including the sensors used and the Machine Learning (ML) algorithms implemented. Finally, this review also presents an overview of innovative commercial solutions, illustrating advanced devices for driver monitoring, vehicle condition assessment, and traffic and road management. Full article

Safe traffic is an important part of sustainable transportation. Road traffic accidents lead to a large number of casualties and property losses every year. Current research mainly studies some types of traffic accidents and ignores other types of traffic accidents; therefore, taking various types of road traffic accidents as a whole, an overall study of their influencing factors is urgently needed. To improve road traffic safety, taking various types of road traffic accidents as a whole, this paper analyzes the influencing factors and finds out the causative factors of road traffic accidents. A new index system of road traffic accident influencing factors is constructed based on the existing literature and real traffic data, and their subjective weights and objective weights are obtained by the analytic hierarchy process based on the subjective data and the normalization of the actual traffic data for Yizheng City, Yangzhou, China from January 2020 to December 2020, where the subjective weights are the main weights, and comprehensive weights are obtained by the minimum discrimination information principle correcting the subjective weights with the objective weights. Finally, the global weights, their ranks, and their weight differences are obtained. The main findings are as follows: (1) compared with the real traffic data, experts generally overestimate the impact of road factors on traffic accidents and underestimate the impact of human factors on traffic accidents; (2) in the first-level, human factors and road factors are the causative factors; (3) in the second-level, “motor vehicle drivers’ misconduct”, “road condition”, and “road section” are the causative factors; and (4) in the third-level, “slippery road”, “rain and snow weather”, “intersection”, and “untimely braking” are the causative factors. The research results can provide some scientific basis for improving road traffic safety. Full article

The working environment of mining electric locomotives is wet and muddy coal mine roadway. Due to low friction between the wheel and rail and insufficient utilization of creep rate, there may be idling or slipping between the wheels and rails of mining electric locomotives. Therefore, it is necessary to control the creep rate within a reasonable range. In this paper, the autonomous control algorithm for mining electric locomotives based on improved $\epsilon$-greedy is theoretically proven to be convergent and effective firstly. Secondly, after analyzing the contact state between the wheel and rail under wet and slippery road conditions, it is concluded that the value of creep rate is an important factor affecting the autonomous driving of mining electric locomotives. Therefore, the autonomous control method for mining electric locomotives based on creep control is proposed in this paper. Finally, the effectiveness of the proposed method is verified through simulation. The problem of wheel slipping and idling caused by insufficient friction of mining electric locomotives in coal mining environments is effectively suppressed. Autonomous operation of vehicles with optimal driving efficiency can be achieved through quantitative control and utilization of the creep rate between wheels and rails. Full article

Distributed-drive vehicles utilize independent drive motors on the four-wheel hubs. The working conditions of the wheel-hub motors are so harsh that the motors are prone to failing under different driving conditions. This study addresses the impact of drive motor faults on vehicle performance, particularly on slippery roads where sudden faults can lead to accidents. A fault-tolerant control system integrating motor fault diagnosis and a direct yaw moment control (DYC) based fault-tolerant controller are proposed to ensure the stability of the vehicle during various motor faults. Due to the difficulty of identifying the parameters of the popular permanent magnet synchronous wheel hub motors (PMSMs), the system employs a model-free backpropagation neural network (BPNN)-based fault detector. Turn-to-turn short circuits, open-phase faults, and diamagnetic faults are considered in this research. The fault detector is trained offline and utilizes rotor speed and phase currents for online fault detection. The system assigns the torque outputs from both healthy and faulted motors based on fault categories using sliding mode control (SMC)-based DYC. Simulations with four-wheel electric vehicle models demonstrate the accuracy of the fault detector and the effectiveness of the fault-tolerant controller. The proposed system is prospective and has potential for the development of distributed electric vehicles. Full article

This article addresses the braking controls for an electric vehicle with DC motors such that the voltage in the motors is used for controlling the wheel angular velocity. Other papers on the anti-lock braking system (ABS) handled how to derive the braking torque (or braking pressure) for controlling the wheel angular velocity. However, heavy or prolonged braking can cause brake fade or wear. According to EURO 7 regulations, brake fade or wear is not desirable, since the regulations refer to the reduction in particles emitted from brake pads. For avoiding heavy or prolonged braking, this paper does not use a brake unit, such as electro-mechanical brake units or hydraulic brake units, for vehicle stop. Instead, the motor voltage is used for controlling the wheel angular velocity. While a vehicle moves, the goal of this paper is to provide automatic braking controls in real time, so that the vehicle stops safely and smoothly without slippage before colliding with an obstacle. In practice, road conditions can change depending on weather conditions, such as rain or snow. Moreover, road slope can have an effect on the braking distance for the vehicle. Thus, this article introduces automatic braking controls, while considering both road slope and road conditions. This article is unique in presenting automatic braking controls for the smooth stop of electric vehicles with DC motors, while considering both road slope and road conditions. In addition, this article is unique in controlling the motor voltage for controlling the wheel angular velocity, while not requiring any brake units. Full article

To enhance the safety and stability of lane change maneuvers for autonomous vehicles in adverse weather conditions, this paper proposes a quadratic programming−based trajectory planning algorithm for lane changing in rainy weather. Initially, in order to mitigate the risk of potential collisions on wet and slippery road surfaces, we incorporate the concept of road adhesion coefficients and delayed reaction time to refine the establishment of the minimum safety distance. This augmentation establishes constraints on lane change safety distances and delineates the boundaries of viable lane change domains within inclement weather contexts. Subsequently, adopting a hierarchical trajectory planning framework, we incorporate visibility cost functions and safety distance constraints during dynamic programming sampling to ensure the safety of vehicle operation. Furthermore, the vehicle lane change sideslip phenomenon is considered, and the optimal lane change trajectory is obtained based on the quadratic programming algorithm by introducing the maneuverability objective function. In conclusion, to verify the effectiveness of the algorithm, lateral linear quadratic regulator (LQR) and longitudinal double proportional−integral−derivative (DPID) controllers are designed for trajectory tracking. The results demonstrate the algorithm’s capability to produce continuous, stable, and collision−free trajectories. Moreover, the lateral acceleration varies within the range of ±1.5 m/s², the center of mass lateral deflection angle varies within the range of ±0.15°, and the yaw rate remains within the ±0.1°/s range. Full article

Anti-slip control, as a fundamental technique of vehicle stability control, prevents loss of control of vehicles, especially under extreme driving conditions. However, current control methods fail to suppress vehicle slippage when steering. Therefore, a new anti-slip control approach for four-wheel independent-drive electric vehicles (EVs) based on the energy method is proposed. This approach makes full use of the distribution of motor energy between the body and the wheels during vehicle turning, being able to adjust the driving torque of each wheel. Simulation results validate that the proposed approach can prevent wheel slip when the vehicle steers on slippery roads. Furthermore, simulations also show that the proposed control strategy can maintain high control performance when the motor flux linkage varies. Full article

Snow and ice forming on the entrance and exit driveways of underground car parks of buildings brings serious difficulties and risks in safe parking for vehicles in winter. Even though traditional methods such as chemical salt and snow plowing reduce slippery conditions on driveways, they also result in infrastructure- and environment-related damages. Hydronic heating is an alternative way to prevent snow and ice forming; thereby, the hydronic heating driveway (HHD) is a promising technique for energy-efficient and environment-friendly solutions. This study presents a time-dependent three-dimensional numerical heat transfer model for HHD applications with realistic boundary conditions and meteorological data in the MATLAB environment. After developing the numerical heat transfer model, the model is applied to a case study in Istanbul, Turkey and followed by an economic comparison with the commercial electrically-heated driveways (EHD) method that is applied in two different ways; applying the electric cables in (i) whole driveway and (ii) only tire tracks. Different escalation rates in natural gas and electricity, hot fluid inlet temperature, air temperature, and the number of parallel pipes are the main parameters in the case study. Results show that the decrease in pipe spacing drops the investment cost term but it needs a higher supplied fluid temperature for anti-icing, and therefore the operating cost term increases. Among other cases was the number of parallel pipes, with 50 being the most economically feasible solution for all air temperatures ranging from 0 °C to −10 °C. The economic comparison shows that the EHD with only tire tracks has the minimum total cost as it significantly decreased both the operating and investment cost terms. In case of an anti-icing requirement on the whole road surface, the HHD system was found to be preferable to the EHD whole driveway scenario at air temperatures of 0 °C and −5 °C, while it is more beneficial only for the high electricity escalation rates at the ambient temperature of −10 °C. Full article

Tire slip control is one of the most critical topics in vehicle dynamics control, being the basis of systems such the Anti-lock Braking System (ABS), Traction Control System (TCS) or Electronic Stability Program (ESP). The highly nonlinear behavior of tire–road contact makes it challenging to design robust controllers able to find a dynamic stable solution in different working conditions. Furthermore, road conditions greatly affect the braking performance of vehicles, being lower on slippery roads than on roads with a high tire friction coefficient. For this reason, by knowing the value of this coefficient, it is possible to change the slip ratio tracking reference of the tires in order to obtain the optimal braking performance. In this paper, an $H_{\infty }$ controller is proposed to deal with the tire slip control problem and maximize the braking forces depending on the road condition. Simulations are carried out in the vehicular dynamics simulator software CarSim. The proposed controller is able to make the tire slip follow a given reference based on the friction coefficient for the different tested road conditions, resulting in a small reference error and good transient response. Full article

Road friction has long been recognized as one of the most effective winter road maintenance (WRM) performance measures. It allows WRM personnel to make more informed decisions to improve their services and helps road users make trip-related decisions. In this paper, a machine-learning-based methodological framework was developed to model road friction using inputs from mobile road weather information systems (RWIS) that collect spatially continuous road weather data and road grip. This study also attempts to estimate friction using data from stationary RWIS that are installed far from each other, thereby leaving large areas unmonitored. To fill in the spatial gaps, a kriging interpolator was developed to create a continuous friction map. Slippery road risk levels were classified to provide an overview of road conditions via a risk warning map. The proposed method was evaluated with a selected highway segment in Alberta, Canada. Results show that the models developed herein are highly accurate (93.3%) in estimating friction and identifying dangerous road segments via a color-coded risk map. Given its high performance, the developed model has the potential for large-scale implementation to facilitate more efficient WRM services while also improving the safety and mobility of the traveling public. Full article

Because the road surfaces of the underground roadways in coal mines are slippery, uneven, with dust and water mist, and the noise and light illumination effects are significant, global positioning system (GPS) signals cannot be received, which seriously affects the ability of the odometer, optical camera and ultrasonic camera to collect data. Therefore, the underground positioning of coal mines is a difficult issue that restricts the intellectualization of underground transportation, especially for automatic robots and automatic driving vehicles. Ultra-wide band (UWB) positioning technology has low power consumption, high performance and good positioning effects in non-visual environments. It is widely used in coal mine underground equipment positioning and information transmission. In view of the above problems, this research uses the WLR-5A mining unmanned wheeled chassis experimental platform; uses two UWB receivers to infer the position and yaw information of the vehicle in the underground roadway through the method of differential mapping; and tests the vehicle on the double shift line and quarter turn line in the GAZEBO simulation environment and on the ground simulation roadway to simulate the vehicle meeting conditions and quarter turning conditions in the underground roadway. The positioning ability of the method in these two cases is tested. The simulation and test results show that the vehicle position and attitude information deduced by two UWB receivers through the differential mapping method can basically meet the requirements of underground environments when the vehicle is traveling at low speeds. Full article

Black ice on the road can be dangerous, as it renders the road slippery and is difficult to identify, owing to its transparency. Although studies on black ice detection using cameras, optical sensors, and infrared sensors have been conducted, these sensors have limitations, as they are affected by low light conditions and sunlight. To detect black ice regardless of low light conditions or sunlight, in this study, we incorporate a mmWave sensor that is consistent with varying light conditions. In the proposed method, a frequency modulated continuous wave is transmitted to the surface by the mmWave sensor, and the mmWave sensor backscattering is modulated by the surface medium and roughness. The proposed method also includes preprocessing to calculate the Range-FFT result of the mmWave sensor backscattering and a classification based on a 1-dimensional convolutional neural network to precisely detect the presence of black ice from the Range-FFT result. As a result of the indoor experiment, the proposed black ice detection method achieves an accuracy of 98.2% on dry, wet, and black ice surfaces. Additionally, under low light conditions and in an outdoor environment with sunlight, the proposed method achieves accuracies of 95.6% and 98.5%, respectively. Full article

This study aims to develop a human-centric, intelligent lighting control system using adaptive LED lights in roadway lighting, integrated with an imaging luminance meter that uses an IoT sensor driver to detect the brightness of road surfaces. AI image data are collected for luminance and vehicle conditions analyses to adjust the output of the photometric curve. Type-A lenses are designed for R3 dry roads, while Type-B lenses are designed for W1 wet roads, to solve hazards caused by slippery roads, for optimizing safety and for visual clarity for road users. Data are collected for establishing formulae to optimize road lighting. First, the research uses zonal flux analysis to design secondary optical components of LED roadway lighting. Based on the distribution of LED lights and the target photometric curve, the freeform surface calculation model and formula are established, and control points of each curved surface are calculated using an iterative method. The reflection coefficient of a roadway is used to design optical lenses that take into account the illuminance and luminance uniformity to produce photometric curves accordingly. This system monitors roadway luminance in real time, which simulates drivers’ visual experiences and uses the ZigBee protocol to transmit control commands. This optimizes the output of light according to weather and produces quality roadway lighting, providing a safer driving environment. Full article