Complex Road Conditions Research Articles

Radar-Assisted Multiple Base Station Cooperative mmWave Beam Tracking

In the future vehicular networks with an increased number of transceiver antennas and higher vehicle speeds, more frequent beam switching is required to ensure the quality of communication, which poses challenges to beam tracking speed and resource efficiency. Integrated sensing and communication (ISAC) provide a new solution to cope with this problem since radar echo can help to predict the vehicle’s future location and beam direction. Therefore, we present a radar-assisted beam tracking algorithm based on Extended Kalman filtering (EKF) and multi-road side unit (RSU) cooperation in this article. Each RSU uses EKF and radar echo to predict and track the vehicle position and upload the prediction information to the edge server (ES). By deploying multiple RSUs, the ES uses the uploaded distributed sensing information for joint estimation and thus improves the accuracy of vehicle location prediction, which is used for the beam tracking task at the next moment. Considering the real complex road conditions, we investigate two scenarios where vehicles move linearly or curvilinearly. Simulation results show that the proposed method with multiple base station cooperation improves the spectral efficiency by 34% and 20% over non-cooperative beam tracking in linear and curvilinear mobility, respectively. In addition, compared with traditional beam tracking based on beam scanning and signaling feedback, radar-assisted beam tracking significantly reduces the communication overhead.

Coordinated Lateral Stability Control of Autonomous Vehicles Based on State Estimation and Path Tracking

This paper proposes a coordinated control system of autonomous vehicles’ lateral stability based on state estimation and path tracking, which contributes to enhancing path-tracking capability and at the same time maintaining the stability of autonomous vehicles driving in complex and dynamic road conditions. First, an extended Kalman filter observer is designed to estimate the vehicle parameters. Then, based on the single-point preview theory, an adaptive preview time model is built for the accuracy of path tracking. When designing the direct yaw-moment controller, sideslip angle and yaw rate, two critical parameters for lateral dynamic stability control are considered, and the single-wheel braking method is utilized to reasonably distribute the additional yaw moment to a certain wheel. To make full use of active front steering and direct yaw-moment control, the critical steering angle of the front wheel is determined, and a switching function is formulated. Co-simulation shows that the suggested coordinated control system can effectively strengthen maneuvering stability and simultaneously satisfy tracking accuracy.

Lane Line Detection and Object Scene Segmentation Using Otsu Thresholding and the Fast Hough Transform for Intelligent Vehicles in Complex Road Conditions

An Otsu-threshold- and Canny-edge-detection-based fast Hough transform (FHT) approach to lane detection was proposed to improve the accuracy of lane detection for autonomous vehicle driving. During the last two decades, autonomous vehicles have become very popular, and it is constructive to avoid traffic accidents due to human mistakes. The new generation needs automatic vehicle intelligence. One of the essential functions of a cutting-edge automobile system is lane detection. This study recommended the idea of lane detection through improved (extended) Canny edge detection using a fast Hough transform. The Gaussian blur filter was used to smooth out the image and reduce noise, which could help to improve the edge detection accuracy. An edge detection operator known as the Sobel operator calculated the gradient of the image intensity to identify edges in an image using a convolutional kernel. These techniques were applied in the initial lane detection module to enhance the characteristics of the road lanes, making it easier to detect them in the image. The Hough transform was then used to identify the routes based on the mathematical relationship between the lanes and the vehicle. It did this by converting the image into a polar coordinate system and looking for lines within a specific range of contrasting points. This allowed the algorithm to distinguish between the lanes and other features in the image. After this, the Hough transform was used for lane detection, making it possible to distinguish between left and right lane marking detection extraction; the region of interest (ROI) must be extracted for traditional approaches to work effectively and easily. The proposed methodology was tested on several image sequences. The least-squares fitting in this region was then used to track the lane. The proposed system demonstrated high lane detection in experiments, demonstrating that the identification method performed well regarding reasoning speed and identification accuracy, which considered both accuracy and real-time processing and could satisfy the requirements of lane recognition for lightweight automatic driving systems.

Junction Selection Based on Optimal-Weighted Multiple Attributes for VANETs

Vehicle ad hoc networks (VANETs) is an emerging ad hoc network technology with a promising future but significant limitations, particularly in communication networks. An effective routing system can significantly enhance the performance of VANETs. However, creating an effective routing protocol in an urban context to transport the data packet to the destination is still challenging due to complex road conditions, sporadic connectivity among vehicles, frequent disconnections, and quick changes in network topology. To enhance the accuracy of routing decisions in an urban setting, we propose a novel “multiple attributes decision-making junction selection routing (MADMJSR)” routing protocol based on vehicle position for VANETs. Formerly, most of the research was focused on static weight-based attribute selection for communication rather than optimal values, which led to a drop in junction decision-making accuracy. Our proposed scheme considers the multiple attribute selection and the optimal weight value assigned to each attribute, which significantly improves the routing decision accuracy. The results show a high packet delivery ratio (PDR) gain within minimum latency and less network load. Furthermore, a thorough discussion of the functioning of our proposed protocol is presented along with a detailed performance evaluation in comparison to other protocols.

Robust controller design of a semi-active quasi-zero stiffness air suspension based on polynomial chaos expansion

Air suspension is one of the heart of electrified chassis, which plays a key role in vehicle ride comfort, driving stability and safety. In order to further improve the suspension performance to meet the demand of complex and changing road conditions, various new suspension structures have emerged. This paper inherits the advantages of electronically controlled air suspension and proposes a quasi-zero stiffness air suspension system combined with pneumatic negative stiffness mechanism. Aiming at the random uncertainty of the structural parameters of the suspension system due to manufacturing, use environment and wear, a robust controller for a semi-active quasi-zero stiffness air suspension based on polynomial chaos expansion (PCE) is designed. The quasi-zero stiffness air suspension alternative model is established by the coefficient state equation of PCE, and the state feedback control law considering the random parameter uncertainty is obtained based on the H2 control theory solution. Simulations and HiL tests are conducted to verify the effectiveness and real-time performance of proposed PCE-H2 controller. Simulation results show that the performance of PCE-H2 control law is better than that of the conventional H2. The HiL results also show that the PCE-H2 control law considering uncertainty is 3.2% less effective than the conventional H2 control law in overall performance of the suspension. Besides, the control effect of the PCE-H2 control law is 38.7% better in the performance maintenance of the semi-active suspension.

Model-Based Condition Monitoring of the Sensors and Actuators of an Electric and Automated Vehicle

Constant monitoring of driving conditions and observation of the surrounding area are essential for achieving reliable, high-quality autonomous driving. This requires more reliable sensors and actuators, as there is always the potential that sensors and actuators will fail under real-world conditions. The sensitive condition-monitoring methods of sensors and actuators should be used to improve the reliability of the sensors and actuators. They should be able to detect and isolate the abnormal situations of faulty sensors and actuators. In this paper, a developed model-based method for condition monitoring of the sensors and actuators in an electric vehicle is presented that can determine whether a sensor has a fault and further reconfigure the sensor signal, as well as detect the abnormal behavior of the actuators with the reconfigured sensor signals. Through the simulation data obtained by the vehicle model in complex road conditions, it is proved that the method is effective for the state detection of sensors and actuators.

A Method of Setting the LiDAR Field of View in NDT Relocation Based on ROI

LiDAR placement and field of view selection play a role in detecting the relative position and pose of vehicles in relocation maps based on high-precision map automatic navigation. When the LiDAR field of view is obscured or the LiDAR position is misplaced, this can easily lead to loss of repositioning or low repositioning accuracy. In this paper, a method of LiDAR layout and field of view selection based on high-precision map normal distribution transformation (NDT) relocation is proposed to solve the problem of large NDT relocation error and position loss when the occlusion field of view is too large. To simulate the real placement environment and the LiDAR obstructed by obstacles, the ROI algorithm is used to cut LiDAR point clouds and to obtain LiDAR point cloud data of different sizes. The cut point cloud data is first downsampled and then relocated. The downsampling points for NDT relocation are recorded as valid matching points. The direction and angle settings of the LiDAR point cloud data are optimized using RMSE values and valid matching points. The results show that in the urban scene with complex road conditions, there are more front and rear matching points than left and right matching points within the unit angle. The more matching points of the NDT relocation algorithm there are, the higher the relocation accuracy. Increasing the front and rear LiDAR field of view prevents the loss of repositioning. The relocation accuracy can be improved by increasing the left and right LiDAR field of view.

Design of an autonomous navigation system for emergency disposal robots at radioactive waste sites under extreme climatic conditions

There are many factors of environmental hazards that endanger the lives of personnel at radioactive waste sites after sudden extreme weather disasters, and the use of robotic substitutes can effectively safeguard the lives of personnel. For the situation where the environmental information of radioactive waste sites is unknown and there are many variables, an autonomous navigation system of emergency disposal robots with strong resilience and stability is designed. First, an algorithm based on the Rao-Blackwellized particle filtering theory is used in the autonomous navigation system to achieve more accurate robot positioning and map building through four processes: prediction, correction, resampling, and map building. Second, the traditional path planning algorithm combined with road information is added to Hopfield neural network to achieve deep analysis of complex road conditions. Finally, the intelligent voice interaction function is added to the navigation system to realize the robot’s interaction and real-time monitoring and feedback. The experimental results show that the emergency disposal robot has high accuracy in positioning, navigation, obstacle avoidance, etc., can respond quickly in unexpected situations and can carry out effective voice interaction.

Innovative Design and Kinematic Characteristics Analysis of Floating Mobile Chassis of Inspection Robot

In view of the problems that exist in the working plane of the inspection robot equipped with precision instruments that cannot always maintain a stable state when moving on a complex road surface, a floating mobile chassis was designed based on the Teoriya Resheniya Izobreatatelskikh Zadatch (TRIZ) theory, and the floating suspension device was also optimized based on the substance field. The kinematic model of the floating mobile chassis was established, and the obstacle-surmounting analysis has been carried out on complex road conditions such as the boss and trench. The dynamic model and mobile performance evaluation model of the obstacle crossing wheel are established. The prototype of the non-floating mobile chassis and the prototype of the floating mobile chassis were respectively established in ADAMS, and the motion comparison simulation analysis of boss, trench crossing and complex road conditions were also carried out. The results showed that the floating mobile chassis has strong adaptive performance, and the stability of the working plane can always be maintained when crossing obstacles.

Nonlinear modeling and multi-scale influence characteristics analysis of organic Rankine cycle (ORC) system considering variable driving cycles

The reasonable construction of the organic Rankine cycle (ORC) system model under road conditions is the key to analyze, evaluate, and optimize the performance of the ORC system. However, due to the variability of high-temperature waste heat source and the strong coupling correlation of operating parameters, the operation characteristics of the ORC system show evident time-varying characteristics. Based on the coupling correlation and redundancy characteristics of the ORC system in complex environment, this paper presents a nonlinear modeling framework for the multi-scale influence analysis of the ORC system under road conditions. The nonlinear modeling framework can improve the prediction accuracy of ORC model by at least 68.36% and reduce the time cost by 53.37%. Based on the nonlinear model, the synergistic effects of multiple variables on ORC system performance at different scales are studied. The frequent fluctuation of vehicle speed enhances the coupling correlation of operating parameters, resulting in nonlinear, hysteretic dynamic characteristics of ORC thermal efficiency. The maximum thermal efficiency of ORC is only 3.28%. The nonlinear modeling framework proposed in this paper can provide a practical solution for constructing the intelligent analysis, design, and optimization models of ORC systems under complex road conditions.

Velocity Estimation for Vehicle-Mounted SAR Based on Deep-Learning Framework

With the development of intelligent driving technology, vehicle-mounted synthetic aperture radar (SAR) imaging, which is regarded as one of the most fundamental assists, has become an important and focal topic. As for automotive SAR imaging, the primary task is to estimate the velocity of the moving platform. Due to the volatile and complex road conditions, it does expense tremendous efforts for accurate velocities acquired with additional motion sensors. In this article, based on exploiting the geometrical relations between the moving platform and near-ground echoes, a velocity estimation scheme is deduced with a single radar sensor only. Then, with the proposed geometric model, an accurate velocity can be obtained and quantitatively analyzed. In addition, to further improve the efficiency of velocity estimation, we establish a real-world SAR echo dataset and transform the conventional equipment-dependable problem into a data-driven method based on a deep neural network. The efficiency and effectiveness of the proposed methods are verified by SAR data collected from real-world scenes.

ILL-IDS: An incremental lifetime learning IDS for VANETs

Vehicle Ad Hoc Network (VANET) is one of the most important approaches for intelligent vehicles to communicate under complex road conditions. However, as a VANET is working under wireless and complex conditions, it is under the threat of various malicious attacks. One efficient solution to counter those attacks is the Intrusion Detection System (IDS), which can detect intrusions to the VANET based on the statistical machine learning method. However, most IDS solutions have a problem that they can only detect the attacks which have already known when the IDS is designed/trained. For unknown attacks, most IDSs become inefficient. A blockchain-based Lifetime Learning IDS (LL-IDS) framework has been designed to address this problem. It applies a blockchain to store uncertain data that an IDS cannot decide and is highly likely to be a new type of attacks. With the help of traditional security agencies such as universities, these uncertain data can be labelled to train the IDS model. The incremental learning models are shown to have a great potential in this scenario. In this paper, we introduces a novel IDS named as incremental IDS based on LL-IDS. Numerical experiments show that the computational time consumption and web payload could be decreased by applying the ILL-IDS to a public VANET dataset.

Road Friction Coefficient Estimation Via Weakly Supervised Semantic Segmentation and Uncertainty Estimation

Vision-based road friction coefficient estimation received extensive attention in the field of road maintenance and autonomous driving. However, the current mainstream coarse-grained friction estimation methods are basically based on image classification tasks. This makes it difficult to deal with complex road conditions in changing weathers. Many models can correctly predict the friction coefficients of the road as a whole in consistent and simple road conditions, but perform poorly otherwise. The existing image benchmarks in this field rarely consider the above problems as well, which limits the comparable evaluations of different models. Therefore, in this paper, we first construct a challenging pixel-level friction coefficient estimation dataset WRF-P to evaluate model performances under mixed road conditions. Then, we propose a friction coefficient estimation method based on weakly supervised learning and uncertainty estimation to realize pixel-level road friction prediction with low annotation cost. The model outperforms existing weakly supervised methods and reaches 39.63% mIOU on the WRF-P dataset. The WRF-P dataset will be made publicly available at https://github.com/blackholeLFL/The-WRF-dataset soon.

Dynamic response assessment and multi-objective optimization of organic Rankine cycle (ORC) under vehicle driving cycle conditions

Organic Rankine cycle (ORC) can effectively utilize the waste heat energy of internal combustion (IC) engine. The ORC system has obvious large time-varying characteristic, strong fluctuation, and hysteresis under the synergistic influence of frequent fluctuation of vehicle speed and operating parameters in the system. The power system, waste heat recovery system, transmission system, and other subsystem models are constructed in this study. The key components are verified. An integrated system model of IC engine–ORC for driving cycle is constructed on the basis of the subsystem model. The dynamic response of ORC system is evaluated under different road conditions. The synergistic influence of multiple variables on system performance is analyzed. Obvious strong coupling and nonlinear characteristics are observed among different performances of ORC system under road conditions. Therefore, a multi-objective cooperative optimization framework of ORC system considering actual road conditions is proposed on the basis of multidimensional data. In the process of collaborative optimization, thermal efficiency and emissions of CO2 equivalent show a clear trade-off under different road conditions. The limit value of thermal efficiency under new European driving cycle is also 6.48% higher than that under worldwide harmonized light vehicles test cycle. Frequent fluctuation of vehicle speed is not conducive to obtaining the limit value of thermal efficiency. The evaluation and optimization of the comprehensive performance of ORC system under complex road conditions can provide a reference for the practical application of vehicle ORC system.

LaneFormer: Real-Time Lane Exaction and Detection via Transformer

In intelligent driving, lane line detection is a basic but challenging task, especially in complex road conditions. The current detection algorithms based on convolutional neural networks perform well for simple scenes with plenty of light, and the lane lines are clean and unobstructed. Still, they do not perform well for complex scenes such as damaged, blocked, and lack-of-light scenes. In this article, we have exceeded the above restrictions and propose an attractive network: LaneFormer; We use an end-to-end network for up and down sampling three times each, then fuse them in their respective channels to extract the slender lane line structure. At the same time, a correction module is designed to adjust the dimensions of the extracted features using MLP, judging whether the feature is completely extracted through the loss function. Finally, we send the feature into the transformer network, detect the lane line points through the attention mechanism, and design a road and camera model to fit the identified lane line feature points. Our proposed method has been validated in the TuSimple benchmark test, showing the most advanced accuracy with the lightest model and fastest speed.

Toward Collaborative Intelligence in IoV Systems: Recent Advances and Open Issues.

Internet of Vehicles (IoV) technology has been attracting great interest from both academia and industry due to its huge potential impact on improving driving experiences and enabling better transportation systems. While a large number of interesting IoV applications are expected, it is more challenging to design an efficient IoV system compared with conventional Internet of Things (IoT) applications due to the mobility of vehicles and complex road conditions. We discuss existing studies about enabling collaborative intelligence in IoV systems by focusing on collaborative communications, collaborative computing, and collaborative machine learning approaches. Based on comparison and discussion about the advantages and disadvantages of recent studies, we point out open research issues and future research directions.

Optimization Analysis of Emergency Material Distribution Methods Based on Complex Network Model and Cluster Analysis Model

In order to ensure people's normal life, it is especially important to deliver emergency supplies in a timely and accurate manner. Along with the improvement of technology and the gradual popularization of 5G network, the application of drones is becoming more and more widespread, and the delivery mode of "delivery vehicle + drone" has gradually become a new and effective delivery method. The delivery mode of "delivery vehicle + drone" refers to the following: in the process of material delivery, the delivery vehicle delivers to a certain location at the same time, the drone can also deliver to the surrounding feasible locations, and return to the delivery vehicle to reload the materials and replace the battery after the delivery is completed. This distribution mode can greatly improve the efficiency of emergency supplies distribution, and can also solve the distribution of supplies under complex road conditions and avoid secondary damage to people from secondary disasters. This paper is motivated to study the distribution mode of emergency supplies based on complex network model and cluster analysis model.

Asphalt pavement characterization by GPR using an air-coupled antenna array

Ground penetrating radar (GPR) has become an effective tool for asphalt pavement inspection. However, a ground-coupled GPR system cannot facilitate a high-speed survey due to the complex road environment and traffic condition. In this paper, an air-coupled antenna array is designed for measurement of common midpoint (CMP) GPR data. From the CMP data, an improved velocity spectrum algorithm considering the refraction on the pavement surface is proposed to simultaneously estimate the thickness and dielectric permittivity of the pavement layer. The results of a laboratory experiment demonstrate that the improved velocity spectrum method can greatly enhance the accuracy of the velocity and thickness estimations, compared with the traditional inversion method. A field measurement conducted on a highway pavement shows that the maximum error of the thickness estimation of the asphalt layer is less than 10 mm (7.1%). It is concluded that the developed CMP technique can be used for quantitative characterization of asphalt pavement.

Research on Dynamic Path Planning of Multi-AGVs Based on Reinforcement Learning

Automatic guided vehicles have become an important part of transporting goods in dynamic environments, and how to design an efficient path planning method for multiple AGVs is a current research hotspot. Due to the complex road conditions in dynamic environments, there may be dynamic obstacles and situations in which only the target point is known but a complete map is lacking, which leads to poor path planning and long planning time for multiple automatic guided vehicles (AGVs). In this paper, a two-level path planning method (referred to as GA-KL, genetic KL method) for multi-AGVs is proposed by integrating the scheduling policy into global path planning and combining the global path planning algorithm and local path planning algorithm. First, for local path planning, we propose an improved Q-learning path optimization algorithm (K-L, Kohonen Q-learning algorithm) based on a Kohonen network, which can avoid dynamic obstacles and complete autonomous path finding using the autonomous learning function of the Q-learning algorithm. Then, we adopt the idea of combining global and local planning by combining the K-L algorithm with the improved genetic algorithm; in addition, we integrate the scheduling policy into global path planning, which can continuously adjust the scheduling policy of multi-AGVs according to changes in the dynamic environment. Finally, through simulation and field experiments, we verified that the K-L algorithm can accomplish autonomous path finding; compared with the traditional path planning algorithm, the algorithm achieved improves results in path length and convergence time with various maps; the convergence time of the algorithm was reduced by about 6.3%, on average, and the path length was reduced by about 4.6%, on average. The experiments also show that the GA-KL method has satisfactory global search capability and can effectively avoid dynamic obstacles. The final experiments also demonstrated that the GA-KL method reduced the total path completion time by an average of 12.6% and the total path length by an average of 8.4% in narrow working environments or highly congested situations, which considerably improved the efficiency of the multi-AGVs.

Revised adaptive active disturbance rejection sliding mode control strategy for vertical stability of active hydro-pneumatic suspension

The unmanned ground vehicle (UGV) travels in complex and uncertain terrain. Its vertical stability is a key factor affecting the working state and service life of high-sensitivity on-board sensors and mechanical structures. With the development of unmanned platform, a six-wheel independent drive UGV (6WID UGV) came into being. Its complex operating conditions and the unique configuration of active hydro-pneumatic suspension (AHPS) put forward higher requirements for vertical stability control. Based on the AHPS of 6WID UGV, a revised active disturbance rejection sliding mode controller (R-ADRSMC) is designed to improve the vertical stability of UGV. Firstly, the dynamic model of AHPS was established, and a test platform was built to verify the accuracy of the nonlinear characteristics of stiffness and damping. Secondly, an extended state observer (ESO) is used to estimate the disturbance caused by the model’s high nonlinearity and uncertainty. The known disturbance is fed back to ESO to form feedforward compensation, which improves the accuracy of disturbance estimation and compensation. Thirdly, the output of ESO is incorporated into the control law of the sliding mode controller (SMC), giving the control law real-time adaptive capability to the state of suspension system. Finally, the effectiveness of R-ADRSMC and its strong robustness to the uncertainty of road and load parameters are verified by simulation. The results show that compared with passive suspension (PS), active disturbance rejection control (ADRC), and SMC, the proposed R-ADRSMC can effectively improve the vertical stability of UGV under complex road conditions and has better control characteristics.