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Abstract With the rapid growth of automobile ownership, traffic congestion has become a major concern at intersections. In order to alleviate the blockage of intersection traffic flow caused by signals, reduce the length of vehicle congestion and waiting time, and for improving the intersection access efficiency, therefore, this article proposes a vehicle speed guidance strategy based on the intersection signal change by combining the vehicle–road cooperative technology. The randomness of vehicle traveling speed in the road is being considered. According to the vehicle traveling speed, a speed guidance model is established under different conditions.

Abstract In 2018, the state explicitly proposed to “promote the cancellation of expressway toll stations at provincial boundaries.” Electronic toll collection has become the main toll collection method on expressways. With the construction of ETC toll lanes, the proportion of ETC vehicles in the expressway traffic flow is increasing, and the rapid processing of vehicle special situations is facing challenges. At present, various provinces have adopted various methods to improve the traffic efficiency and transaction success rate of ETC from the issuance link, customer service link, and lane transaction link. According to statistical data, the average transaction success rate of ETC lane is not higher than 99% at present. As of October 2021, the number of ETC users nationwide has reached 256 million, and there are an average of 40 million ETC transactions per day across the network, that is, about 400,000 special cases need to be processed.

Abstract Testing and verifying the security of connected and autonomous vehicles (CAVs) under cyber-physical attacks is a critical challenge for ensuring their safety and reliability. Proposed in this article is a novel testing framework based on a model of computation that generates scenarios and attacks in a closed-loop manner, while measuring the safety of the unit under testing (UUT), using a verification vector. The framework was applied for testing the performance of two cooperative adaptive cruise control (CACC) controllers under false data injection (FDI) attacks. Serving as the baseline controller is one of a traditional design, while the proposed controller uses a resilient design that combines a model and learning-based algorithm to detect and mitigate FDI attacks in real-time.

Abstract The use of appropriate loads and regulations is of great importance in weld fatigue assessment of rail on-track maintenance equipment and similar vehicles for optimized design. The regulations and available loads, however, are often generalized for several categories, which proves to be overly conservative for some specific categories of machines. EN (European Norm) and AAR (Association of American Railroads) regulations play a pivotal role in determining the applicable loads and acceptance criteria within this study. The availability of track-induced fatigue load data for the cumulative damage approach in track maintenance machines is often limited. Consequently, the FEA-based validation of rail track maintenance equipment often resorts to the infinite life approach rather than cumulative damage approach for track-induced travel loads, resulting in overly conservative designs.

Abstract Due to the presence of uncertain disturbances in the actual steering system, disturbances in the system may affect the handling stability of the vehicle. Therefore, this article proposes an integrated steering system control strategy with stronger anti-disturbance performance. When disturbances exist in the system, the proposed control strategy effectively reduces the attitude changes during the vehicle steering process. In the upper-level control strategy, a variable transmission ratio curve is designed to coordinate the high-speed handling stability and low-speed steering sensitivity of the vehicle. On this basis, a sideslip angle observer is proposed based on the extended state observation theory, which does not depend on an accurate system model, thus determining the intervention timing of the active front wheel steering system. In the lower-level control strategy, DR-PI/DR-PID controllers are designed for the integrated steering system.

Abstract A time domain analysis method of ride comfort and energy dissipation characteristics is proposed for automotive vibration proportional–integral–derivative (PID) control. A two-degrees-of-freedom single wheel model for automotive vibration control is established, and the conventional vibration response variables for ride comfort evaluation and the energy consumption vibration response variables for energy dissipation characteristics evaluation are determined, and the Routh stability criterion method was introduced to assess the impact of PID control on vehicle stability. The PID control parameters are tuned using the differential evolution algorithm, and to improve the algorithm’s adaptive ability, an adaptive operator is introduced, so that the mutation factor of differential evolution algorithm can change with the number of iterations.

Abstract Letter from the Focus Issue Editors

Abstract An automatic collision avoidance control method integrating optimal four-wheel steering (4WS) and direct yaw-moment control (DYC) for autonomous vehicles on curved road is proposed in this study. Optimal four-wheel steering is used to track a predetermined trajectory, and DYC is adopted for vehicle stability. Two single lane change collision avoidance scenarios, i.e., a stationary obstacle in front and a moving obstacle at a lower speed in the same lane, are constructed to verify the proposed control method. The main contributions of this article include (1) a quintic polynomial lane change trajectory for collision avoidance on curved road is proposed and (2) four different kinds of control method for autonomous collision avoidance, namely 2WS, 2WS+DYC, 4WS, and 4WS+DYC, are compared. In the design of DYC controller, two different feedback control methods are adopted for comparison, i.e., sideslip angle feedback and yaw rate feedback.

Abstract In order to enhance the path-tracking accuracy and adaptability of the electric flatbed vehicle (EFV) in the soft-target vehicle test system, an improved controller is designed based on the linear quadratic regulator (LQR) algorithm. First, the LQR feedback controller is designed based on the EFV dynamics tracking error model, and the genetic algorithm is utilized to obtain the optimal weight coefficient matrix for different speeds. Second, a weight coefficient matrix compensation strategy is proposed to address the changes in the relationship between the vehicle–road position and attitude caused by external disturbances and the state of EFV. An offline parameter table is created to reduce the computational load on the microcontroller of EFV and enhance real-time path-tracking performance. Furthermore, an adaptive preview time control strategy is added to reduce the overshooting caused by control delay. This strategy is based on road curvature and traveling speed.

Abstract Accurate estimation of traction force is essential for the development of advanced control systems, particularly in the domain of autonomous driving. This study presents an innovative approach to enhance the estimation of tire–road interaction forces under combined slip conditions, employing a combination of empirical models and neural networks. Initially, the well-known Pacejka formula, or magic formula, was adopted to estimate tire–road interaction forces under pure longitudinal slip conditions. However, it was observed that this formula yielded unsatisfactory results under non-pure slip conditions, such as during curves. To address this challenge, a neural network architecture was developed to predict the estimation error associated with the Pacejka formula. Two distinct neural networks were developed. The first neural network employed, as inputs, both longitudinal slip ratios of the driving wheels and the slip angles of the driving wheels.

Abstract Ground vibration testing (GVT) is an important phase of the development, or the structural modification of an aircraft program. The modes of vibration and their associated parameters extracted from the GVT are used to modify the structural model of the aircraft to make more reliable dynamics predictions to satisfy certification authorities. Due to the high cost and the extensive preparations for such tests, a new method of vibration testing called taxi vibration testing (TVT) rooted in operational modal analysis (OMA) was recently proposed and investigated by the German Institute for Aerospace Research (DLR) as alternative to conventional GVT. In this investigation, a computational framework based on fully coupled flexible multibody dynamics for TVT is presented to further investigate the applicability of the TVT to flexible airframes. The time domain decomposition (TDD) method for OMA was used to postprocess the response of the airframe during a TVT.

Abstract Connected fuel cell vehicles (C-FCVs) have gained increasing attention for solving traffic congestion and environmental pollution issues. To reduce operational costs, increase driving range, and improve driver comfort, simultaneously optimizing C-FCV speed trajectories and powertrain operation is a promising approach. Nevertheless, this remains difficult due to heavy computational demands and the complexity of real-time traffic scenarios. To resolve these issues, this article proposes a two-level eco-driving strategy consisting of speed planning and energy management layers. In the top layer, the speed planning predictor first predicts dynamic traffic constraints using the long short-term memory (LSTM) model. Second, a model predictive control (MPC) framework optimizes speed trajectories under dynamic traffic constraints, considering hydrogen consumption, ride comfort, and traffic flow efficiency.

Abstract Because of the growing interest in LTE-V2X, there is a need to describe its performance under various conditions and scenarios. This article explores the deployment of long-term evolution vehicle-to-everything (LTE-V2X) technology for vehicle-to-infrastructure (V2I) communication and delves into the deployment of LTE-V2X communication in three major global regions: the United States, Europe, and China. We begin with an overview of the functionality of LTE-V2X and highlight the objectives of V2I communication in terms of safety and mobility applications—and describe why it will be the predominant type of V2X in the first few years of deployment. We also examine the specific Day-1 V2I message sets standardized in each region, along with their potential applications and benefits. The technical details and use cases using these messages are discussed, along with the benefits they offer in improving the accuracy, reliability, and safety for surface transportation.

Abstract Letter from the Special Issue Editor

Abstract In modern era, with the global spread of massive devices, connecting, controlling, and managing a significant amount of data in the IoT environment, especially in the Internet of vehicles (IoV) is a great challenge. There is a big problem of high-energy consumption due to overhead-unwanted data communication to the non-participatory vehicles, at high enduring connection rate. Therefore, this article proposed a social vehicle association-based data dissemination approach, which was segregated into three parts: First, develop an improved power evaluation approach for discovering power-efficient vehicles. Second, using the Fokker–Planck equation, the connection likelihood of these vehicles is calculated in the second phase to find trustworthy and steady connections. Last, develop an evaluation approach for vehicles community association using convolutional neural network (CNN).

Abstract Vehicle-to-infrastructure (V2I) connectivity technology presents the opportunity for vehicles to perform autonomous longitudinal control to navigate safely and efficiently through sequences of V2I-enabled intersections, known as connected corridors. Existing research has proposed several control systems to navigate these corridors while minimizing energy consumption and travel time. This article analyzes and compares the simulated performance of three different autonomous navigation systems in connected corridors: a V2I-informed constant acceleration kinematic controller (V2I-K), a V2I-informed model predictive controller (V2I-MPC), and a V2I-informed reinforcement learning (V2I-RL) agent. A rules-based controller that does not use V2I information is implemented to simulate a human driver and is used as a baseline. The performance metrics analyzed are net energy consumption, travel time, and root-mean-square (RMS) acceleration.

Abstract In this article, pedestrian crossing intention and pedestrian crossing style are studied by means of statistical theory and artificial neural network. Feature parameters such as the average speed of pedestrians, pedestrian attention to vehicles, and vehicle arrival speed are extracted before and during the time pedestrians cross the street from a bird’s-eye view. Based on these parameters, an artificial neural network is used to predict the pedestrian crossing intention. K-means statistical method was used to cluster the pedestrian crossing styles, and the results showed that clustering the crossing styles into three categories, conservative, cautious, and adventurous, has a better classification effect, and the crossing behaviors of different types of pedestrians were analyzed. A random forest-based model is used to identify pedestrian crossing styles, the prediction accuracy reaches 91.83% and the recognition accuracy reaches 93.3%.

Abstract The cooperative platoon of multiple trucks with definite proximity has the potential to enhance traffic safety, improve roadway capacity, and reduce fuel consumption of the platoon. To investigate the truck platooning performance in a real-world environment, two Peterbilt class-8 trucks equipped with cooperative truck platooning systems (CTPS) were deployed to conduct the first-of-its-kind on-road commercial trial in Canada. A total of 41 CTPS trips were carried out on Alberta Highway 2 between Calgary and Edmonton during the winter season in 2022, 25 of which were platooning trips with 3 to 5 sec time gaps. The platooning trips were performed at ambient temperatures from −24 to 8°C, and the total truck weights ranged from 16 to 39 tons. The experimental results show that the average time gap error was 0.8 sec for all the platooning trips, and the trips with the commanded time gap of 5 sec generally had the highest variations.

Abstract This study investigates the use of a road weather model (RWM) as a virtual sensing technique to assist autonomous vehicles (AVs) in driving safely, even in challenging winter weather conditions. In particular, we investigate how the AVs can remain within their operational design domain (ODD) for a greater duration and minimize unnecessary exits. As the road surface temperature (RST) is one of the most critical variables for driving safety in winter weather, we explore the use of the vehicle’s air temperature (AT) sensor as an indicator of RST. Data from both Road Weather Information System (RWIS) stations and vehicles measuring AT and road conditions were used. Results showed that using only the AT sensor as an indicator of RST could result in a high number of false warnings, but the accuracy improved significantly with the use of an RWM to model the RST.

Abstract The European Union’s pro-ecological policy imposes a requirement to use biofuel additives in diesel fuel which is supposed to support the sustainable development of transport and limit its negative impact on the natural environment. The study presents an analysis of the exhaust gas components and the amount of solid particles carried out for internal combustion engines fueled with mixtures of diesel fuel and fatty acid methyl esters. Additionally, the computer software of the tested power units was modified by changing the amount of fuel to be supplied and the air intake. The goal of the tests was to find out how the fuel mixture and reprogramming of the computer control systems would impact the emission of exhaust gas components. Based on the tests, it was found that an additive of fatty acid methyl esters to diesel does have an influence on the tested unit parameters.