The sensorless control is a key technology of permanent magnet synchronous motor. The core contribution of this work is twofold: the design of an angular-velocity-based proportional-integral observer for rotor position estimation, and the inaugural derivation of the relationship between q-axis current error and angular velocity. This derived relationship allows the method to be applied universally across a wide speed range. Based on the rotation axis voltage model, the current error between the estimated model and the real model is utilized to estimate the angular velocity, which is used to compute the rotor angle through integration. Furthermore, the Lyapunov stability principle is employed to prove the stability of the algorithm. The algorithm is implemented in a motor control system and tested in a motor test bench, which achieves fast and precise speed control of a permanent magnet synchronous motor. Taken together, the experimental findings and statistical evaluations validate the angular-velocity-based proportional-integral observer as a compelling candidate for sensorless algorithmic control in permanent magnet synchronous motors.

In response to the problems of urban traffic congestion and the limited expansion of infrastructure, this paper conducts two core research focusing on the intelligent chassis system of split-type flying vehicle. Firstly, an autonomous navigation strategy for the intelligent chassis module is proposed based on chassis module Navigation 2 architecture, which fuses LIDAR and IMU positioning to plan paths using the A* global planning algorithm on a global cost map, and update the local cost map in real time with sensor data. It is orchestrated by the BT Navigator using a behavior tree, with failures handled by the Recovery Server, to achieve autonomous driving across multiple waypoints. In simulation and closed-field experiments, the system can stably reach the preset target points. The positioning accuracy and trajectory tracking performance can meet the design requirements. Secondly, a mechanical slide rail-type docking structure adapted to the split flying vehicle architecture is designed. Deformation analysis under the representative working conditions are evaluated through finite element software. The test results show that the maximum deformation of this docking structure under typical load is significantly lower than the docking tolerance and positioning repeatability requirements. The structural stiffness and stability meet the design indicators. The above work indicates that the proposed autonomous navigation strategy and the docking structure for the intelligent chassis can effectively support the modular operation of “air trunk & ground terminal” mode, providing a scientific basis for the functional integration and system reliability research of split-type flying vehicles.

Logistics companies typically adopt a mixed fleet of petrol-fueled and electric logistics vehicles to support urban logistics delivery in the era of the logistics green transition. This paper addresses the scheduling problem of a mixed fleet for urban logistics delivery under demand uncertainty. First, we formulate the problem as a chance-constrained vehicle routing problem with time windows, simultaneous loads, and partial charging under demand uncertainty. Then, we propose an adaptive simulated annealing genetic (ASG) algorithm to solve the mixed fleet scheduling model. Finally, the proposed model and algorithm are verified through a set of numerical experiments and an empirical case study. Specifically, benchmark instances generated by the Solomon generator are used to evaluate the effectiveness, solution quality, and computational efficiency of the proposed ASG algorithm. Results from small- and medium-scale instances show that ASG consistently outperforms benchmark algorithms in terms of objective value and computational time, while large-scale instances further confirm its robustness and stability through statistical tests. An empirical case study based on real-world logistics delivery data from Hefei City, China, indicates that increasing the penetration of ELVs significantly reduces total operational costs, with a fully ELV fleet achieving a 34.63% reduction in total operational costs compared to a fully PFV fleet. Our study offers practical insights for logistics companies aiming to balance cost and sustainability during the logistics green transition. It also provides guidance on infrastructure investment and policy-making for urban freight electrification.

With the rapid development of integrated vehicle-road-cloud systems, roadside units (RSUs) have become crucial for communication in vehicular ad hoc networks (VANETs). However, RSU failures disrupt real-time vehicle-to-cloud connectivity, exacerbating traffic congestion and safety risks. Recently, uncrewed aerial vehicles (UAVs) have emerged as a promising solution for temporary mobile base stations due to their rapid deployment and flexibility. Nevertheless, the open and dynamic nature of UAV-vehicle communication networks presents critical challenges in achieving both secure communication and lightweight authentication. Furthermore, the unsupervised deployment environment of UAVs makes them particularly vulnerable to physical attacks. To address these challenges, in this article, we propose a secure and lightweight UAV-assisted vehicle authentication scheme based on a quantum key. The scheme leverages quantum keys, hash functions, and xor to ensure secrecy and traceability of malicious entities. Utilizing the physical unclonable function (PUF) and fuzzy extractor to resist physical attacks. A private blockchain architecture is implemented for secure data storage and management. We verify the scheme's security using the real-or-random (ROR) oracle model and the Scyther tool. The experimental results show that, compared to other schemes, our scheme has a lower overhead, with an average packet loss rate reduction of 11.42%-28.8% and an average communication delay reduction of 14.41%-63%.

Special vehicles such as off-road vehicles and planetary rovers frequently operate on complex, unpaved road surfaces with varying mechanical parameters. Inaccurate estimation of these parameters can cause subsidence or rollover. Existing methods either lack proactive perception or high precision. This article proposes a fusion framework integrating a visual classifier and a dynamics observer for stable, accurate estimation of road surface parameters. The visual classifier uses an adaptive segmentation system for unpaved roads, leveraging a large-scale vision model and a lightweight network to classify upcoming road surfaces. The dynamics observer employs an online wheel--ground interaction model using stress approximation, integrating strong tracking theory into an unscented Kalman filter for real-time parameter estimation. The fusion framework performs integration of the classifier and observer outputs at data, feature, and decision levels. An adaptive fading factor and recursive Gaussian process modeling ensure precise estimation of varying parameters. Real-vehicle tests demonstrate that the proposed method reduces the average estimation error by 8.5% and improves convergence speed by 40% during road surface changes, demonstrating potential for integration into off-road vehicle stability control systems.

This paper presents a braking torque control framework with driver intention recognition for the single pedal mode of electric vehicles. Based on the sampled data of the pedal angle and its rate of change, a dual-fuzzy recognition method is designed to determine the braking intention and the required braking intensity. Different braking torque control strategies are allocated according to the braking demand. A vehicle longitudinal dynamics model is established, and based on this model, the model predictive control algorithm is used to calculate the minimum value of the cost function, thereby determining the corresponding optimal motor braking torque. The simulation test results show that under urban driving conditions, the mean square errors of vehicle acceleration, jerk, and slip ratio are approximately $0.08 \mathrm{m} / \mathrm{s}^{2}, 15 \mathrm{m} / \mathrm{s}^{3}$, and 0.0037, respectively, verifying that the proposed control method can achieve precise control of motor torque under different braking intensity demands.

It is particularly challenging to develop a new control theory like human intelligence, as human cognition and decision-making are variable in changing environments. In this article, the idea of variable stability is adopted to design a human-like control algorithm, referred to as variable stability control. A variable model perturbation put into the system dynamics model is computed by model game control, which simulates changes in human cognition. Lyapunov stability control is employed to formulate a backstepping control law that mimics the underlying logic algorithm in human decision-making. Some variable algorithm parameters embedded into the control law are calculated using model predictive control, which imitates dynamic tuning in human decision-making. From another perspective, variable stability control is an algorithm-hybrid control approach validated in a steer-by-wire system for angle tracking. According to the experimental results, variable stability control is a promising candidate for angle tracking in steer-by-wire systems.

In this paper, a real-time prediction of optimal wheel slip ratio with application in electric motor anti-lock braking control is proposed. An initial estimation method of optimal slip ratio is designed according to the sampling data, by which the prediction curve equation is calculated. By comparing the predicted data with the actual sampled data, the confidence is computed to obtain the optimal slip ratio. A wheel dynamics model is established as the state-space equation of model predictive control algorithm, by which the motor braking torque is regulated to follow the optimal slip ratio. Some simulation tests are carried out, from which it can be concluded that the proposed approach has excellent predictive performance of optimal slip ratio and can significantly enhance both efficiency and safety during emergency condition.

Sliding mode control (SMC) algorithms in engineering applications necessitate the design of a sliding manifold to guide system motion. However, finding the optimal sliding manifold parameter (SMP) may be particularly challenging. While much of the research on SMC has focused on establishing a constant parameter for the sliding manifold, comparatively little is known about using a variable approach. In this article, we propose a predictive sliding control (PSC) algorithm that uses model predictive control (MPC) to determine the optimal SMP. The input to MPC is the SMC law with a sliding manifold variable parameter. In addition, the stability and robustness of the control system are analyzed in detail. The designed predictive sliding controller is applied to angle following of a steer-by-wire system installed on a self-driving vehicle. Experimental results and statistical analysis demonstrate the efficacy of the proposed control algorithm.

To break through the limitations associated with over-reliance on high-cost sensors and complex tire models in the current research, a wheel anti-lock control approach for regenerative braking process of battery electric vehicle based on optimal slip ratio recognition is proposed. By deriving the rate of change of the utilization adhesion coefficient with the slip ratio, the moment corresponding to the optimal braking condition is determined, thereby the optimal slip ratio is recognized. A road adhesion variation monitor is designed to ensure the adaptability under challenging road conditions, offering enhanced flexibility and practicality. A wheel dynamics model is developed to serve as the basis for a model predictive controller, which is designed for efficient optimal slip ratio tracking. Simulation test demonstrate that the effectiveness of the regenerative braking stability control for battery electric vehicle can be significantly improved.

To address the impact of sparsity and disorder of point clouds on object detection accuracy, this paper proposes a multi-modal fusion network VPC-VoxelNet based on virtual point clouds. Firstly, virtual point clouds are constructed using image detection object information to increase the density of point clouds, thus improving the performance of object features; Secondly, increasing the dimensionality of point cloud features, distinguishing virtual point clouds and avoiding the accumulation of multi model errors; Finally, an optimized loss function such as the scale factor of the virtual point cloud is used to improve the training efficiency of the multi-modal network. The object detection network, VPC-VoxelNet, was tested on the KITTI dataset, and the detection accuracy was better than that of the classical 3D point cloud detection network and certain multi-modal information fusion networks, with a vehicle detection accuracy of 86.9

To enhance the accuracy of path tracking in autonomous vehicles, we propose a method of path optimization based on tire trajectory. The real road is abstracted into a centroid path parametric equation, from which a new centroid path parametric equation is derived based on the tire trajectory. A two-degree-of-freedom dynamic model is employed to design a predicting model for the MPC controller. Co-simulation experiments using Simulink and Carsim are conducted under various road conditions to analyze the offset of each wheel. The results indicate that in continuous small curvature curves, after optimization, the error of the front wheels decreased by more than 24.6%, and the error of the rear wheels decreased by 30%. In large curvature curves, after optimization, the errors of both the front and rear wheels have been reduced by over 42%, significantly enhancing the accuracy of path tracking.

In vehicular ad hoc networks (VANETs), efficient anonymous authentication and group key update schemes have always been a focal point of research. However, there remains significant room for improvement in ensuring privacy protection as well as the security and efficiency of key distribution. Therefore, this article proposes an efficient anonymous authentication and group key agreement scheme for vehicle-fog-cloud systems based on quantum random numbers. In this scheme: 1) the anonymous authentication of vehicles is generated by combining random numbers from the vehicle and the trusted authority (TA), thereby achieving privacy protection for vehicles during the registration and authentication process and 2) a method for generating and updating a group key is designed. By sharing Chebyshev chaotic mapping parameters, the vehicle and the road side unit (RSU) independently compute session keys, and the tasks of the group key calculation and updating are offloaded to the RSU, enabling the group key to update rapidly. This scheme ensures one-time encryption while achieving forward and backward security. Through security analysis and real-vehicle testing, the security and feasibility of the proposed scheme are demonstrated. Furthermore, compared to other schemes, as the number of vehicles increases, the computational overhead at the vehicle and TA remain almost unchanged, while the signaling overhead is reduced by nearly half.

The automotive industry is evolving with advances in electrification, vehicle intelligence, and connectivity. This study assesses the strategic position and growth prospects of Anhui's intelligent new energy vehicle (INEV) industry during the global automotive transition. In this paper, the Delphi method is employed to evaluate Anhui’s industrial strengths, policy support, and talent base. Based on these evaluations, this study proposes a regional development framework, “One Core, Two Wings, One Base.” Within this framework, the Analytic Hierarchy Process (AHP) is applied to derive indicator weights. To convert the diagnosis into actionable strategies, a SWOT analysis is conducted to link internal factors to external conditions. The results indicate that Anhui maintains a competitive position in the INEV industry but encounters gaps in core technology and assurance functions. Accordingly, targeted development strategies and policy measures are proposed. These measures target gaps in key technologies while strengthening supply-chain resilience in Anhui’s INEV industry.

In this study, we propose a novel International Multimodal Transport Connectivity (IMTC) index and assess the IMTC of multimodal transport from mainland China to Europe. We first present the definition of IMTC, which represents the ease of transporting export cargoes from a domestic origin to an overseas destination by an international multimodal transport network. Second, we propose two kinds of IMTC indexes and then give detailed measurement methods by considering the main modes (i.e., railway express and maritime shipping) in the international multimodal transport process. One is the monetary-based IMTC measured by the monetary cost shippers must pay for transporting export cargo to an overseas region. The other is the time-based IMTC, which is measured by the time cost of the international multimodal transport process and incorporates the time value of different export cargo categories and transport time uncertainty. Third, we assess the IMTC of the international multimodal transport from mainland China to Europe and we mainly compare the monetary-based and the time-based IMTCs via the CR Express or maritime shipping in the pre- and post-COVID years. The results indicate that monetary-based IMTCs from China to Europe via CR Express are significantly lower than those via maritime shipping in the pre-COVID year, while the trend reverses in the post-COVID year, and the monetary-based IMTCs via CR Express are much higher than those via maritime shipping. Moreover, in both the pre-and post-COVID years, the time-based IMTCs from China to Europe via CR Express were significantly higher than those via maritime shipping for all nine cargo categories. Our study offers a new perspective for assessing the convenience of international multimodal transport with the novel IMTC index and provides policy and managerial implications (e.g., real-time cargo tracking technology with blockchain, unified price control mechanism and subsidy strategy, and expansion of the CR Express capacity) to guide policymakers in improving the resilience of international multimodal transport.

The construction of scenario library for autonomous driving algorithm testing has always been a difficult problem in intelligent connected vehicles. Aiming at the problem that the proportion of key high-risk test scenarios that can effectively test the automatic driving algorithm in the natural driving scenario library is too low, this paper proposes a method for generating key high-risk test scenario library of intelligent connected vehicles based on the driving safety field to improve the proportion of key high-risk scenarios in the test scenario library. Firstly, based on the theory of the driving safety field, the Scenario Potential Risk-Driving Safety Field (SPR-DSI) is taken as the risk evaluation index representing the potential risk of automatic driving scenarios and verified. Then, the threshold method is used to extract the scenario elements, and the correlation analysis between the scenario elements and SPR-DSI is carried out to determine the key scenario elements. After that, based on the Genetic Algorithm (GA), SPR-DSI is used as the risk evaluation index for generating scenarios, and the key scenario elements are taken as chromosomes to generate a database of key high-risk test scenarios for autonomous vehicles. Finally, compared with the scenario library generated by Markov Chain Monte Carlo (MCMC) and Automatic Efficient Test Generator (AETG), the results show that, the high-risk test scenario library generated based on driving safety field effectively increases the proportion of key high-risk test scenarios, which helps to fully verify the safety and robustness of automatic driving algorithm.

At present,the unsupervised monocular infrared image depth estimation method is difficult to deal with low texture and low contrast areas,resulting in poor estimation effect,so an unsupervised monocular infra-red image depth estimation algorithm based on local plane guide layer is proposed in this paper.The algorithm con-sists of continuous video frame input,multi-scale feature extraction,ASPP and local planar guidance layer,compu-tational loss,joint training,and output image module.Firstly,by using multiple small-resolution grayscale blocks and multi-scale feature fusion,the problems of blurring edges and occluding objects in infrared images are solved.Secondly,by using the local plane guidance layer to introduce a plane constraint on the depth image,the noise and discontinuity in the depth image are reduced,and the problem of lack of clear processing of low texture areas of the traditional algorithm is solved.The experimental results show that the proposed depth estimation algorithm effective-ly improves the accuracy of monocular depth estimation and reduces the error,and the Abs Rel,Sq Rel,RMS,RMS(log)on the Iray dataset is 0.262,3.621,9.473 and 0.332,respectively,and the accuracy reaches 60.5％,85.2％ and 94.5％ when the threshold indicators are less than 1.25,1.252 and 1.253.

Brake-by-wire system for self-driving vehicles requires hydraulic pressure response of the master cylinder to be quick in the braking process, making its long-term precision paramount. This is particularly challenging for braking in response to the command of virtual driver instead of the real driver, for which the hydraulic pressure is nonlinear with the electric motor’s torque. While much of the research on brake-by-wire has focused on improving energy regeneration and vehicle stability, comparatively little is known about the braking pressure control of self-driving vehicles. Therefore, we develop a novel architecture of the electro-hydraulic brake-by-wire adopting ball screw for the self-driving, in which there is a quadratic-polynomial relationship between the position of the electric motor and the brake pressure of the master cylinder. The brake-by-wire dynamics model is built by the speed of the electric motor and the brake pressure of the master cylinder, and a sliding mode control law is designed based on the pressure demand of the master cylinder. Here we discuss a series of studies on electro-hydraulic braking dynamics that, collectively, design a pressure demand control approach of how the sliding mode controller operates the master cylinder pressure by the electric motor torque. Testing and analyzing the designed approach embedded into the braking control unit is applied to a vehicle test bench brake-by-wire to fully realize the accurate pressure tracking of the electro-hydraulic brake-by-wire system.