Drivers are the center of vehicles and transportation systems. Because of the rapid development of advanced technologies, artificial drivers have been developed as key elements in vehicles and transportation systems. The inconsistency between human drivers and artificial drivers will lead to accidents and congestion. To make future vehicles and transportation systems trustworthy in driving safety and acceptable in travel efficiency, developing technologies based on human drivers’ reliable knowledge and cognitive intelligence together with smart operations is an essential and promising solution. However, there are many challenges to be addressed including the learning of smart human perception, reliable smart inference strategies in decision-making, adaptive correction of inappropriate driving operation, knowledge mapping and enhancement of smart human driving in various scenarios, etc.
To alleviate these challenges, emerging technologies inspired by human intelligence (e.g., self-supervised learning, reinforcement learning, game theory, etc.) have been extensively developed in the related communities. This special issue aims to provide a platform for researchers, engineers, and policymakers to share their latest innovative ideas and contributions in developing and applying these novel technologies to address the challenges concerning human-like smart autonomous driving in intelligent vehicles and transportation systems.
Four articles are collected in this feature topic that promotes the recent advances in the field of human-like autonomous driving systems. The feature topic highlights the progress in environment perception, driver behavior analysis, human-vehicle shared control, and decision-making. The core contributions of these four articles are listed below.

The take-over control (ToC) of human–machine interaction is a hotspot. From automatic driving to manual driving, some factors affecting driver response time have not been considered in existing models, and little attention has been paid to its effects on mixed traffic flow. This study establishes a ToC model of response based on adaptive control of thought-rational cognitive architecture (CAR-ToC) to investigate the effects of driver response time on traffic flow. A quantification method of driver’s situation cognition uncertainty is also proposed. This method can directly describe the cognitive effect of drivers with different cognitive characteristics on vehicle cluster situations. The results show that when driver response time in ToC is 4.2 s, the traffic state is the best. The greater the response time is, the more obvious the stop-and-go waves exhibit. Besides, crashes happen when manual vehicles hit other types of vehicles in ToC. Effects of driver response time on traffic are illustrated and verified from various aspects. Experiments are designed to verify that road efficiency and safety are increased by using a dynamic take-over strategy. Further, internal causes of effects are revealed and suggestions are discussed for the safety and efficiency of autonomous vehicles.

Driver distraction has been deemed a major cause of traffic accidents. However, drivers’ brain response activities to different distraction types have not been well investigated. The purpose of this study is to investigate the response of electroencephalography (EEG) activities to different distraction tasks. In the conducted simulation tests, three secondary tasks (i.e., a clock task, a 2-back task, and a navigation task) are designed to induce different types of driver distractions. Twenty-four participants are recruited for the designed tests, and differences in drivers’ brain response activities concerning distraction types are investigated. The results show that the differences in comprehensive distraction are more significant than that in single cognitive distraction. Friedman test and post hoc two-tailed Nemenyi test are conducted to further identify the differences in band activities among brain regions. The results show that the theta energy in the frontal lobe is significantly higher than that in other brain regions in distracted driving, whereas the alpha energy in the temporal lobe significantly decreases compared to other brain regions. These results provide theoretical references for the development of distraction detection systems based on EEG signals.

The driver's behavior plays a crucial role in transportation safety. It is widely acknowledged that driver vigilance is a major contributor to traffic accidents. However, the quantitative impact of driver vigilance on driving risk has yet to be fully explored. This study aims to investigate the relationship between driver vigilance and driving risk, using data recorded from 28 drivers who maintain a speed of 80 km/h on a monotonous highway for 2 hours. The k-means and linear fitting methods are used to analyze the driving risk distribution under different driver vigilance states. Additionally, this study proposes a research framework for analyzing driving risk and develops three classification models (KNN, SVM, and DNN) to recognize the driving risk status. The results show that the frequency of low-risk incidents is negatively correlated with the driver's vigilance level, whereas the frequency of moderate-risk and high-risk incidents is positively correlated with the driver's vigilance level. The DNN model performs the best, achieving an accuracy of 0.972, recall of 0.972, precision of 0.973, and f1-score of 0.972, compared to KNN and SVM. This research could serve as a valuable reference for the design of warning systems and intelligent vehicles.

Parameter effects of the potential-field-driven model predictive control (PF-MPC) method on performances of shared control systems during obstacles avoidance are investigated. The PF-MPC controllers of autonomous driving and shared control systems are designed based on the constructed potential fields and model predictive control method, and the driver-vehicle dynamics and the driver-related costs are also considered in the design of the shared controller. To explore a potential approach of alleviating driver-automation conflicts of the shared control systems, different motion planning results generated by the PF-MPC controller are explored by adjusting effects of potential fields’ parameters, which provides possibilities to decrease driver-automation conflicts between the planned trajectory and driver’s target path. Moreover, two case studies are designed to discuss different frameworks and parameters effects on shared control systems. Results show that the proposed shared control frameworks considering driver-vehicle dynamics and the driver-related cost show better performances regarding driver-automation conflicts management and driving safety than the decentralized control framework. And the longitudinal normalized constant of potential fields parameters shows influences on the driver-automation conflicts management and driving safety performances of shared control.

This paper presents an energy management optimization system based on an adaptive functional state model of battery aging for internal combustion engine vehicles (ICEVs). First, the functional characteristics of batteries in ICEVs are investigated. Then, an adaptive functional state model is proposed to represent battery aging throughout the entire battery service life. A battery protection scheme is developed, including over-discharge and graded over-current protection to improve battery safety. A model-based energy management strategy is synthesized to comprehensively optimize fuel economy, battery life preservation, and vehicle performance. The performance of the proposed scheme was examined under comprehensive test scenarios based on field and bench tests. The results show that the proposed energy management algorithm can effectively improve fuel economy.

Increasing efficiency and reducing emissions are fundamental approaches to achieving peak carbon emissions and carbon neutrality for the transportation and power industries. The Argon power cycle (APC) is a novel concept for high efficiency and zero emissions. However, APC faces the challenges of severe knock and low power density at high efficiency. To elevate efficiency and power density simultaneously of APC, the Miller cycle is applied and combined with APC. The calculation method is based on a modification of the previous thermodynamic method. The mixture of hydrogen and oxygen is controlled in the stoichiometric ratio. The results indicate that to obtain a thermal conversion efficiency of 70%, in the Otto cycle, the compression ratio and the AR (argon molar ratio in the argon-oxygen mixture) could be 9 and 95%, respectively. In comparison, for the Miller cycle, these two parameters only need to be 7 and 91%. A lower compression ratio can reduce the negative effect of knock, and a reduced AR increases the power density by 66% with the same efficiency. The improvement effect is significant when the expansion-compression ratio is 1.5. Meanwhile, increasing the expansion-compression ratio is more effective in the argon-oxygen mixture than in the nitrogen–oxygen mixture. For the next-generation Argon/Miller power cycle engine, the feasible design to achieve the indicated thermal efficiency of 58.6% should be a compression ratio of 11, an expansion-compression ratio of 1.5, and an AR of 91%.

Clustering is an unsupervised learning technology, and it groups information (observations or datasets) according to similarity measures. Developing clustering algorithms is a hot topic in recent years, and this area develops rapidly with the increasing complexity of data and the volume of datasets. In this paper, the concept of clustering is introduced, and the clustering technologies are analyzed from traditional and modern perspectives. First, this paper summarizes the principles, advantages, and disadvantages of 20 traditional clustering algorithms and 4 modern algorithms. Then, the core elements of clustering are presented, such as similarity measures and evaluation index. Considering that data processing is often applied in vehicle engineering, finally, some specific applications of clustering algorithms in vehicles are listed and the future development of clustering in the era of big data is highlighted. The purpose of this review is to make a comprehensive survey that helps readers learn various clustering algorithms and choose the appropriate methods to use, especially in vehicles.

This paper proposes a novel density-based real-time segmentation algorithm, to extract ground point cloud in real time from point cloud data collected by roadside LiDAR. The algorithm solves the problems such as the large amount of original point cloud data collected by LiDAR, which leads to heavy computational burden in ground point search. First, point cloud data is filtered by straight-through filtering method and rasterized to improve the real-time performance of the algorithm. Then, the density of the point cloud in horizontal plane is calculated, and the threshold of the density is selected to extract the low-density regional point cloud according to the density statistical histogram and 95% loci. Finally, the low-density regional point cloud is used as the initial ground seeds for iterative optimization of ground parameters, and the ground point cloud is extracted by the fitted ground model to realize road point cloud extraction. The experimental results on 1055 frames of continuous data collected on real scenes show that the average time consumption of the proposed method is 0.11 s, and the average segmentation precision is 92.48%. This shows that the density-based road segmentation algorithm can reduce the time of point cloud traversal in the process of ground parameter fitting and improve the real-time performance of the algorithm while maintaining the accuracy of ground extraction.

The fuel cell hybrid powertrain is a potential power supply system for fuel cell vehicles. The underlying problem is that the fuel cell vehicles encounter exhaustive hydrogen consumption. To effectively manage hydrogen consumption, the aim is to propose fuel cell city bus power and control system. The underlying idea is to determine the target power of fuel cell through simulation study on fuel cell and battery energy management strategy and road test verifications. A half-power prediction energy management strategy is implemented to predict the target power of the fuel cell in the current time step based on the demand power of the vehicle and the state of charge (SOC) of the battery in the previous time steps. This offers better understanding of the correlation between fuel cell power and vehicle drive cycle for enabling effective power supply management. The research results show that the half-power prediction energy management strategy effectively reduces the hydrogen consumption of the vehicle by 7.1% and the number of battery cycle by 6.0%, compared to the stepped management strategy of battery SOC. When applied to a 12-m fuel cell city bus—F12, specially designed and manufactured for the Winter Olympic Games in 2022—the fuel economy of 3.7 kg/100 km is achieved in urban road conditions. This study lays a foundation for providing the powertrain configuration and energy management strategy of fuel cell city bus.