Search

Abstract A large amount of heat accumulates in the engine bay for a short time after the engine runs at high load and shuts down, that will lead to thermal damage and thermal fatigue caused by the temperature rise of some heat sensitive components. This paper uses an aero-thermal coupling approach to study the heat transfer problem in the engine bay of an SUV model under thermal soak conditions. Due to the transient characteristics of the heat transfer process, the natural transient CFD software developed based on the LBM method is used to study the engine bay heat transfer during the 400 s key-off soak process. The analysis reveals that convection and radiation are the main heat transfer modes in the early stage of hot immersion (0–120 s), and conduction only makes a significant contribution in contact with high temperature sources. The radiation and convection are the key contributors to heat transfer processes of engine bay during soak, but the efficiency of radiation heat transfer decreases with the increase of time, whereas the efficiency of convection heat transfer is not always reduced, it will increase and then decrease with the increase of time. The coupling method established can predict the thermal state in the engine bay well, and is in good agreement with the experimental results. The results show that the error in the engine coolant temperature is less than 1 °C, and the error in the temperature of the heat-sensitive components is less than 5 °C. Finally, the potential risks of thermal damage and thermal fatigue states were assessed, providing an important reference for the control design of cooling fan running time after key-off.

In 2022, China released the Special Action Plan for the Control of Spartina alterniflora (2022–2025) with the goal of completely clearing S. alterniflora from China's coastal wetlands. It is crucial to conduct a thorough analysis of wetlands under artificial intervention and obtain reliable future wetland simulation results. The Yellow River Delta wetland is one of China's important wetland reserves, has faced severe impacts from S. alterniflora invasion over the past decade. This study focuses on the Yellow River Delta wetland, utilizing Landsat imagery and a combined object-oriented and pixel-based approach to achieve wetland mapping from 2013 to 2023. Through wetland analysis, we designed scenarios for natural development, ecological priority, farmland protection, and combined development. The patch generating land use simulation model was used to simulate wetland scenarios for 2030 and analyze factors influencing wetland use cover change and future conversion patterns. The results indicate that between 2013 and 2022, native vegetation was impacted by the invasion of S. alterniflora, with Suaeda glauca being the most affected. In 2023, the removal of S. alterniflora was effective, reducing its area by 42.37 km2 compared to pre-removal conditions. In all three scenarios, the area of Phragmites australis increased and is expected to dominate the wetlands, while the live space of other native salt marsh vegetation has also been restored. However, the predictions indicate that S. alterniflora still poses a potential threat of expansion in the future, necessitating continuous monitoring of its dynamics. We recommend implementing zoned management of the wetland, balancing ecological restoration in coastal areas with agricultural development in the western regions.

Abstract Lithium‐ion batteries (LIBs) are currently the predominant energy storage power source. However, the urgent issues of enhancing electrochemical performance, prolonging lifetime, preventing thermal runaway‐caused fires, and intelligent application are obstacles to their applications. Herein, bio‐inspired electrodes owning spatiotemporal management of self‐healing, fast ion transport, fire‐extinguishing, thermoresponsive switching, recycling, and flexibility are overviewed comprehensively, showing great promising potentials in practical application due to the significantly enhanced durability and thermal safety of LIBs. Taking advantage of the self‐healing core–shell structures, binders, capsules, or liquid metal alloys, these electrodes can maintain the mechanical integrity during the lithiation–delithiation cycling. After the incorporation of fire‐extinguishing binders, current collectors, or capsules, flame retardants can be released spatiotemporally during thermal runaway to ensure safety. Thermoresponsive switching electrodes are also constructed though adding thermally responsive components, which can rapidly switch LIB off under abnormal conditions and resume their functions quickly when normal operating conditions return. Finally, the challenges of bio‐inspired electrode designs are presented to optimize the spatiotemporal management of LIBs. It is anticipated that the proposed electrodes with spatiotemporal management will not only promote industrial application, but also strengthen the fundamental research of bionics in energy storage.

Although reinforcement learning (RL) methodologies exhibit potential in addressing decision-making and planning problems in autonomous driving, ensuring the safety of the vehicle under all circumstances remains a formidable challenge in practical applications. Current RL methods are predominantly driven by singular reward mechanisms, frequently encountering difficulties in balancing multiple sub-rewards such as safety, comfort, and efficiency. To address these limitations, this paper introduces a constraint-driven safety RL method, applied to decision-making and planning policy in highway scenarios. This method ensures decisions maximize performance rewards within the bounds of safety constraints, exhibiting exceptional robustness. Initially, the framework reformulates the autonomous driving decision-making problem as a Constrained Markov Decision Process (CMDP) within the safety RL framework. It then introduces a Multi-Level Safety-Constrained Policy Optimization (MLSCPO) method, incorporating a cost function to address safety constraints. Ultimately, simulated tests conducted within the CARLA environment demonstrate that the proposed method MLSCPO outperforms the current advanced safe reinforcement learning policy, Proximal Policy Optimization with Lagrangian (PPO-Lag) and the traditional stable longitudinal and lateral autonomous driving model, Intelligent Driver Model with Minimization of Overall Braking Induced by Lane Changes (IDM+MOBIL). Compared to the classic IDM+MOBIL method, the proposed approach not only achieves efficient driving but also offers a better driving experience. In comparison with the reinforcement learning method PPO-Lag, it significantly enhances safety while ensuring driving efficiency, achieving a zero-collision rate. In the future, we will integrate the aforementioned potential expansion plans to enhance the usability and generalization capabilities of the method in real-world applications.

To ensure the reliability of autonomous driving, the system must be capable of potential hazard identification and appropriate response to prevent accidents. This involves the prediction of possible developments in traffic situations and an evaluation of the potential danger of future scenarios. Precise Collision Risk Assessment (CRA) faces complex challenges due to uncertainties inherent in vehicle and road environmental conditions. This paper introduces a new CRA approach, the Multi-Dimensional Uncertainties-CRA (MDU-CRA), which integrates uncertainties related to driver behavior, sensor perception, motion prediction models, and road infrastructure into a comprehensive risk evaluation framework. The estimation of vehicle state is initiated using Extended Kalman Filtering (EKF) to capture uncertainties in sensor perception. Concurrently, a probabilistic motion prediction model based on Gaussian distributions has been developed, which considers the uncertainty in driver behavior. Subsequently, the uncertainty of the road structure is modeled using a truncated Gaussian distribution. Finally, collision risk is quantified as the future probability of collision through heuristic Monte Carlo (MC) sampling. This paper presents the results of two experiments Firstly, our proposed method is demonstrated to outperform the reference neural network-based method in terms of short-term motion prediction accuracy. Secondly, two driving scenarios are extracted and reconstructed from the Next Generation Simulation (NGSIM) dataset for validation and evaluation, i.e., an active lane-change scenario and an emergency braking scenario. In the domain of collision risk assessment, our approach consistently outperforms other evaluation methods. It exhibits the capability to perceive collision risks 2 to 5 seconds in advance, significantly reducing the probability of imminent collision incidents.

With the recent advancements in machine learning technology, the accuracy of autonomous driving object detection models has significantly improved. However, due to the complexity and variability of real-world traffic scenarios, such as extreme weather conditions, unconventional lighting, and unknown traffic participants, there is inherent uncertainty in autonomous driving object detection models, which may affect the planning and control in autonomous driving. Thus, the rapid and accurate quantification of this uncertainty is crucial. It contributes to a better understanding of the intentions of autonomous vehicles and strengthens trust in autonomous driving technology. This research pioneers in quantifying uncertainty in the YOLOv5 object detection model, thereby improving the accuracy and speed of probabilistic object detection, and addressing the real-time operational constraints of current models in autonomous driving contexts. Specifically, a novel probabilistic object detection model named M-YOLOv5 is proposed, which employs the MC-drop method to capture discrepancies between detection results and the real world. These discrepancies are then converted into Gaussian parameters for class scores and predicted bounding box coordinates to quantify uncertainty. Moreover, due to the limitations of the Mean Average Precision (MAP) evaluation metric, we introduce a new measure, Probability-based Detection Quality (PDQ), which is incorporated as a component of the loss function. This metric simultaneously assesses the quality of label uncertainty and positional uncertainty. Experiments demonstrate that compared to the original YOLOv5 algorithm, the M-YOLOv5 algorithm shows a 74.7% improvement in PDQ. When compared with the most advanced probabilistic object detection models targeting the MS COCO dataset, M-YOLOv5 achieves a 14% increase in MAP, a 17% increase in PDQ, and a 65% improvement in FPS. Furthermore, against the state-of-the-art probabilistic object detection models for the BDD100K dataset, M-YOLOv5 exhibits a 31.67% enhancement in MAP and a 125.6% increase in FPS.

Urban traffic congestion poses significant economic and environmental challenges worldwide. To mitigate these issues, Adaptive Traffic Signal Control (ATSC) has emerged as a promising solution. Recent advancements in deep reinforcement learning (DRL) have further enhanced ATSC’s capabilities. This paper introduces a novel DRL-based ATSC approach named the Sequence Decision Transformer (SDT), employing DRL enhanced with attention mechanisms and leveraging the robust capabilities of sequence decision models, akin to those used in advanced natural language processing, adapted here to tackle the complexities of urban traffic management. Firstly, the ATSC problem is modeled as a Markov Decision Process (MDP), with the observation space, action space, and reward function carefully defined. Subsequently, we propose SDT, specifically tailored to solve the MDP problem. The SDT model uses a transformer-based architecture with an encoder and decoder in an actor–critic structure. The encoder processes observations and outputs, both encoded data for the decoder, and value estimates for parameter updates. The decoder, as the policy network, outputs the agent’s actions. Proximal Policy Optimization (PPO) is used to update the policy network based on historical data, enhancing decision-making in ATSC. This approach significantly reduces training times, effectively manages larger observation spaces, captures dynamic changes in traffic conditions more accurately, and enhances traffic throughput. Finally, the SDT model is trained and evaluated in synthetic scenarios by comparing the number of vehicles, average speed, and queue length against three baselines, including PPO, a DQN tailored for ATSC, and FRAP, a state-of-the-art ATSC algorithm. SDT shows improvements of 26.8%, 150%, and 21.7% over traditional ATSC algorithms, and 18%, 30%, and 15.6% over the FRAP. This research underscores the potential of integrating Large Language Models (LLMs) with DRL for traffic management, offering a promising solution to urban congestion.

Abstract Under the global carbon neutrality initiative, carbon emissions from the transportation sector are becoming increasingly prominent due to the growth in vehicle ownership. And electric mobility may be a potentially effective measure to reduce road traffic carbon emissions and achieve a green transformation of transportation. This paper systematically collates the relevant carbon accounting standards for the automotive industry and elaborates the current status of road transport greenhouse gas emissions by combining the data from the International Energy Agency. And by comparing the lifecycle carbon footprint of various energy types of vehicles, the necessity and feasibility of electric mobility to reduce carbon emissions are discussed. However, the comparison of vehicle lifecycle carbon footprints shows that electric vehicles (EVs) are not as environmentally friendly as expected, although they can significantly reduce road traffic carbon emissions. The high carbon emissions from the manufacturing process of the core components of EVs, especially the power battery, reduce the low‐carbon potential of electric mobility. Therefore, the carbon emission reduction strategies and outcomes of automakers in the automotive industry chain have been further reviewed. Finally, focusing on vehicle power batteries, this article reviews the technologies such as refined management and echelon utilization that can make EVs more environmentally friendly and promote carbon neutrality in the transportation sector.

The centralized coordination of Connected and Automated Vehicles (CAVs) at unsignalized intersections aims to enhance traffic efficiency, driving safety, and passenger comfort. Autonomous Intersection Management (AIM) systems introduce a novel approach for centralized coordination. However, existing rule-based and optimization methods often face the challenges of poor generalization and low computational efficiency when dealing with complex traffic environments and highly dynamic traffic conditions. Additionally, current Reinforcement Learning (RL)-based methods encounter difficulties around policy inference and safety. To address these issues, this study proposes Constraint-Guided Behavior Transformer for Safe Reinforcement Learning (CoBT-SRL), which uses transformers as the policy network to achieve efficient decision-making for vehicle driving behaviors. This method leverages the ability of transformers to capture long-range dependencies and improve data sample efficiency by using historical states, actions, and reward and cost returns to predict future actions. Furthermore, to enhance policy exploration performance, a sequence-level entropy regularizer is introduced to encourage policy exploration while ensuring the safety of policy updates. Simulation results indicate that CoBT-SRL exhibits stable training progress and converges effectively. CoBT-SRL outperforms other RL methods and vehicle intersection coordination schemes (VICS) based on optimal control in terms of traffic efficiency, driving safety, and passenger comfort.

Winter wheat is one of the most important crops in the world. It is great significance to obtain the planting area of winter wheat timely and accurately for formulating agricultural policies. Due to the limited resolution of single SAR data and the susceptibility of single optical data to weather conditions, it is difficult to accurately obtain the planting area of winter wheat using only SAR or optical data. To solve the problem of low accuracy of winter wheat extraction only using optical or SAR images, a decision tree classification method combining time series SAR backscattering feature and NDVI (Normalized Difference Vegetation Index) was constructed in this paper. By synergy using of SAR and optical data can compensate for their respective shortcomings. First, winter wheat was distinguished from other vegetation by NDVI at the maturity stage, and then it was extracted by SAR backscattering feature. This approach facilitates the semi-automated extraction of winter wheat. Taking Yucheng City of Shandong Province as study area, 9 Sentinel-1 images and one Sentinel-2 image were taken as the data sources, and the spatial distribution of winter wheat in 2022 was obtained. The results indicate that the overall accuracy (OA) and kappa coefficient (Kappa) of the proposed method are 96.10% and 0.94, respectively. Compared with the supervised classification of multi-temporal composite pseudocolor image and single Sentinel-2 image using Support Vector Machine (SVM) classifier, the OA are improved by 10.69% and 5.66%, respectively. Compared with using only SAR feature for decision tree classification, the producer accuracy (PA) and user accuracy (UA) for extracting the winter wheat are improved by 3.08% and 8.25%, respectively. The method proposed in this paper is rapid and accurate, and provide a new technical method for extracting winter wheat.

As an enhanced version of standard CAN, the Controller Area Network with Flexible Data (CAN-FD) rate is vulnerable to attacks due to its lack of information security measures. However, although anomaly detection is an effective method to prevent attacks, the accuracy of detection needs further improvement. In this paper, we propose a novel intrusion detection model for the CAN-FD bus, comprising two sub-models: Anomaly Data Detection Model (ADDM) for spotting anomalies and Anomaly Classification Detection Model (ACDM) for identifying and classifying anomaly types. ADDM employs Long Short-Term Memory (LSTM) layers to capture the long-range dependencies and temporal patterns within CAN-FD frame data, thus identifying frames that deviate from established norms. ACDM is enhanced with the attention mechanism that weights LSTM outputs, further improving the identification of sequence-based relationships and facilitating multi-attack classification. The method is evaluated on two datasets: a real-vehicle dataset including frames designed by us based on known attack patterns, and the CAN-FD Intrusion Dataset, developed by the Hacking and Countermeasure Research Lab. Our method offers broader applicability and more refined classification in anomaly detection. Compared with existing advanced LSTM-based and CNN-LSTM-based methods, our method exhibits superior performance in detection, achieving an improvement in accuracy of 1.44% and 1.01%, respectively.

Addressing decision safety in the unpredictable arena of complex traffic scenarios represents a significant hurdle for autonomous driving systems. Considering the inherent spatial-temporal uncertainties associated with the future actions of surrounding traffic participants, real-time safety verification of autonomous driving decisions is crucial to maintaining vehicular safety. Existing online verification methodologies, such as Responsibility Sensitive Safety (RSS) and Safety Force Field (SFF), ensure driving safety by formalizing human safe-driving rules and constraining the vehicle to maintain safe lateral and longitudinal distances in real-time. While these methods effectively prevent collisions instigated by the autonomous vehicle itself, they lack sufficient foresight and often result in less smooth driving trajectories. To address these limitations, we propose an innovative, interpretable, formal safety verification framework. This approach integrates both explicit and implicit traffic rules to anticipate all legally acceptable transitions of traffic scenarios. It builds the lawful, short-term reachable region for each vehicle, and verifies the safety of autonomous vehicle decisions by assessing whether the regions these vehicles inhabit, in accordance with the expected trajectory, overlap with the accessible zones of other vehicles. Furthermore, in scenarios presenting potential danger, a backup smooth safety trajectory is derived from the autonomous vehicle’s legal reachability domain as a preventive measure to degrade safety threats. As a cornerstone of safety for autonomous vehicles, our proposed method ensures a continual safe trajectory in all traffic scenarios, provided that other participants adhere to traffic rules. Experimental outcomes, grounded in the ISO 34502 standard and real-world critical safety scenarios, demonstrate the method’s efficacy in identifying potentially dangerous decisions and mitigating autonomous vehicle-induced traffic accidents.

Objective: Autonomous vehicles (Avs) have paved the way for the arrangement of swivel seats in vehicles, which could pose a challenge to traditional safety systems. The integration of automated emergency braking (AEB) and pre-pretension (PPT) seatbelts improves protection for a vehicle’s occupant. The objective of this study is to explore the control strategies of an integrated safety system for swiveled seating orientations.Methods: Occupant restraints were examined in various seating configurations using a single-seat model with a seat-mounted seatbelt. Seat orientation was set at different angles, from −45° to 45° with 15° increments. A pretension was used on the shoulder belt to represent an active belt force cooperating with AEB. A generic full frontal vehicle pulse of 20 mph was applied to the sled. The occupant’s kinematics response under various integrated safety system control strategies was analyzed by extracting a head pre-crash kinematics envelope. The injury values were calculated for various seating directions with or without an integrated safety system at the collision speed of 20 mph.Results: In a lateral movement, the excursions of the dummy head were 100 mm and 70 mm in the global coordinate system for negative and positive seat orientations, respectively. In the axial movement, the head traveled 150 mm and 180 mm in the global coordinate system for positive and negative seating directions, respectively. The 3-point seatbelt did not restrain the occupant symmetrically. The occupant experienced greater y-axis excursion and smaller x-axis excursion in the negative seat position. Various integrated safety system control strategies led to significant differences in head movement in the y direction. The integrated safety system reduced the occupant’s potential injury risks in different seating positions. When the AEB and PPT were activated, the absolute HIC15, brain injury criteria (BrIC), neck injury (Nij), and chest deflection were reduced in most seating directions. However, the pre-crash increased the injury risks at some seating positions.Conclusion: The pre-pretension seatbelt could reduce the occupant’s forward movement in the rotating seat positions in a pre-crash period. The occupant’s pre-crash motion envelope was generated, which could be beneficial to future restraint systems and vehicle interior design. The integrated safety system could reduce injuries in different seating orientations.

The main application scenario for wearable sensors involves the generation of data and monitoring metrics. fNIRS (functional near-infrared spectroscopy) allows the nonintrusive monitoring of human visual perception. The quantification of visual perception by fNIRS facilitates applications in engineering-related fields. This study designed a set of experimental procedures to effectively induce visible alterations and to quantify visual perception in conjunction with the acquisition of Hbt (total hemoglobin), Hb (hemoglobin), and HbO2 (oxygenated hemoglobin) data obtained from HfNIRS (high-density functional near-infrared spectroscopy). Volunteers completed the visual task separately in response to different visible changes in the simulated scene. HfNIRS recorded the changes in Hbt, Hb, and HbO2 during the study, the time point of the visual difference, and the time point of the task change. This study consisted of one simulated scene, two visual variations, and four visual tasks. The simulation scene featured a car driving location. The visible change suggested that the brightness and saturation of the car operator interface would change. The visual task represented the completion of the layout, color, design, and information questions answered in response to the visible change. This study collected data from 29 volunteers. The volunteers completed the visual task separately in response to different visual changes in the same simulated scene. HfNIRS recorded the changes in Hbt, Hb, and HbO2 during the study, the time point of the visible difference, and the time point of the task change. The data analysis methods in this study comprised a combination of channel dimensionality reduction, feature extraction, task classification, and score correlation. Channel downscaling: This study used the data of 15 channels in HfNIRS to calculate the mutual information between different channels to set a threshold, and to retain the data of the channels that were higher than those of the mutual information. Feature extraction: The statistics derived from the visual task, including time, mean, median, variance, extreme variance, kurtosis, bias, information entropy, and approximate entropy were computed. Task classification: This study used the KNN (K-Nearest Neighbors) algorithm to classify different visual tasks and to calculate the accuracy, precision, recall, and F1 scores. Scoring correlation: This study matched the visual task scores with the fluctuations of Hbt, Hb, and HbO2 and observed the changes in Hbt, Hb, and HbO2 under different scoring levels. Mutual information was used to downscale the channels, and seven channels were retained for analysis under each visual task. The average accuracy was 96.3% ± 1.99%; the samples that correctly classified the visual task accounted for 96.3% of the total; and the classification accuracy was high. By analyzing the correlation between the scores on different visual tasks and the fluctuations of Hbt, Hb, and HbO2, it was found that the higher the score, the more obvious, significant, and higher the fluctuations of Hbt, Hb, and HbO2. Experiments found that changes in visual perception triggered changes in Hbt, Hb, and HbO2. HfNIRS combined with Hbt, Hb, and HbO2 recorded by machine learning algorithms can effectively quantify visual perception. However, the related research in this paper still needs to be further refined, and the mathematical relationship between HfNIRS and visual perception needs to be further explored to realize the quantitative study of subjective and objective visual perception supported by the mathematical relationship.

Abstract To improve the ride comfort and safety of a traditional adaptive cruise control (ACC) system when the preceding vehicle changes lanes, it proposes a target vehicle selection algorithm based on the prediction of the lane-changing intention for the preceding vehicle. First, the Next Generation Simulation dataset is used to train a lane-changing intention prediction algorithm based on a sliding window support vector machine, and the lane-changing intention of the preceding vehicle in the current lane is identified by lateral position offset. Second, according to the lane-changing intention and collision threat of the preceding vehicle, the target vehicle selection algorithm is studied under three different conditions: safe lane-changing, dangerous lane-changing, and lane-changing cancellation. Finally, the effectiveness of the proposed algorithm is verified in a co–simulation platform. The simulation results show that the target vehicle selection algorithm can ensure the smooth transfer of the target vehicle and effectively reduce the longitudinal acceleration fluctuation of the subject vehicle when the preceding vehicle changes lanes safely or cancels their lane change maneuver. In the case of a dangerous lane change, the target vehicle selection algorithm proposed in this paper can respond more rapidly to a dangerous lane change than the target vehicle selection method of the traditional ACC system; thus, it can effectively avoid collisions and improve the safety of the subject vehicle.

This paper investigates the method to improve the property that can decrease the impact response of composite plate. Embedding the super-elastic shape memory alloy wires into composite plates has increased the attention of material researchers. Super-elastic shape memory alloy has the properties of absorbing mechanical energy, large recoverable deformation and so on. In this study, experiments were conducted to analyze the impact properties of composite plates with Ni-Ti SMA wires. Composite plates with Ni-Ti SMA wires and without Ni-Ti SMA wires were subjected to two impacts respectively. This study measured the responses of two impacts. The results showed that the composite plate with Ni-Ti SMA wires were subjected to a second impact with a peak deflection of 5.47 mm, which was only 0.22 mm larger than the first impact. The relevant data of the composite plate without Ni-Ti SMA wires were 9.02 mm, 1.22 mm, and serious damage occurred. It was verified that the Ni-Ti SMA wires improved the impact resistance of the composite plate. After studying the impact tests of variable diameters of SMA wires embedded at the low layer of composite plate, it was shown that as the diameter of SMA wires increased, the impact resistance of composite plates was improved.

Abstract Lithium‐ion batteries (LIBs) have become the main choice for electric vehicles (EVs). However, the thermal runaway problems of LIBs largely limit the wider promotion of EVs. To provide background and insight for the improvement of battery safety, the general working mechanism of LIBs is described in this review, followed by a discussion of the thermal runaway process, including the trigger conditions and material factors. Moreover, advances made to improve battery safety are examined from the perspective of battery materials and management systems. Thus, this review provides a general picture of the thermal runaway risks of LIBs and corresponding solutions with the aim of facilitating safer battery designs.

With the rapid development of new-energy vehicles worldwide, lithium-ion batteries (LIBs) are becoming increasingly popular because of their high energy density, long cycle life, and low self-discharge rate. They are widely used in different kinds of new-energy vehicles, such as hybrid electric vehicles and battery electric vehicles. However, low-temperature (−20–−80 °C) environments hinder the use of LIBs by severely deteriorating their normal performance. From the perspective of material design, this review summarized and analyzed common methods of improving LIBs’ performance via structure optimization and material optimization, and the future development of methods in this regard is discussed. This review is expected to provide cell design ideas for enhancing the low-temperature performance of LIBs.

With the development of electric vehicles, fast-charging is greatly demanded for commercialisation on lithium-ion batteries. The rapid charging process could lead to serious side reactions on the graphite anodes, such as lithium plating and solid electrolyte interface (SEI) film growth, which severely affect the battery performances. However, there is a lack of quantitative research on their contribution ratio to battery performance and the occurrence thresholds. In this work, a P2D model of a lithium-ion battery with the correction of SEI film growth and lithium plating was built. A cyclic charge/discharge experiment was also designed to analyze the changes of SEI film and lithium plating under high charge-rate conditions. It was found that under such conditions, the battery capacity attenuation in the early stage was mainly caused by lithium plating. In the middle and late stages, as the lithium plating tended to be stable, the capacity attenuation was largely caused by the growth of the SEI film. The study provides theoretical support for the improvement of the charge/discharge strategy of lithium-ion batteries.

Abstract Gas diffusion layers (GDLs) are the most rigid layers in a 5‐layer or 7‐layer membrane electrode assembly (MEA) of a proton exchange membrane fuel cell. Therefore, in the fuel cell analysis, the mechanical properties of GDLs have a great impact on the stress distribution of the membrane as well as the performance of the whole cell. However, the mechanical properties of GDLs are not sufficiently studied. Nonlinear behavior of GDLs under cyclic compression is discussed rarely in literature. The existing model takes both constraints into consideration, but due to a geometrical oversimplification of the carbon paper microstructure, it shows some deviation, which cannot be overcome by selecting appropriate parameters. In this paper, the geometry of carbon paper microstructure has been reanalyzed. Based on the improved geometry formula, a modified model of carbon paper GDL is presented and verified by experimental data. Furthermore, both the experimental mechanical characteristics are achieved as support data for the improved model.

Regarding changes of subjective and objective parameters of comfort during short and long-term driving, the arbitrariness of traditional subjective evaluation methods, as well as the defects of objective method where issues can be qualitatively but not quantitatively analyzed. By measuring pressure distribution and electromyography during short and long-term driving respectively, the article obtained pressure indicators, electromyography characteristic parameters and the corresponding subjective evaluations of drivers. The variation of subjective and objective parameters of comfort, the difference in terms of comfort and the fatigable body parts were tested. By means of correlation analysis completed the indicators screen. Proposed a new comprehensive weighting method-AHP to limit entropy method, established the mapping relations between subjective comfort and objective indicators, which based on pressure distribution and physiological information during short-term and long-term driving. Obtained a quantitative evaluation method that applies pressure distribution, physiological information and subjective evaluation to effectively evaluate driving comfort, and thus provided a theoretical basis for evaluating driving comfort.

Abstract Vigorous development of electric vehicles is one way to achieve global carbon reduction goals. However, fires caused by thermal runaway of the power battery has seriously hindered large‐scale development. Adding thermal runaway retardants (TRRs) to electrolytes is an effective way to improve battery safety, but it often reduces electrochemical performance. Therefore, it is difficult to apply in practice. TRR encapsulation is inspired by the core–shell structures such as cells, seeds, eggs, and fruits in nature. In these natural products, the shell isolates the core from the outside, and has to break as needed to expose the core, such as in seed germination, chicken hatching, etc. Similarly, TRR encapsulation avoids direct contact between the TRR and the electrolyte, so it does not affect the electrochemical performance of the battery during normal operation. When lithium‐ion battery (LIB) thermal runaway occurs, the capsules release TRRs to slow down and even prevent further thermal runaway. This review aims to summarize the fundamentals of bioinspired TRR capsules and highlight recent key progress in LIBs with TRR capsules to improve LIB safety. It is anticipated that this review will inspire further improvement in battery safety, especially for emerging LIBs with high‐electrochemical performance.

Big data already covers intelligent vehicles and is driving the autonomous driving industry’s transformation. However, the large amounts of driving data generated will result in complex issues and a huge workload for the test and verification processes of an autonomous driving system. Only effective and precise data extraction and recording aimed at the challenges of low efficiency, poor quality, and a long-time limit for traditional data acquisition can substantially reduce the algorithm development cycle. Based on the premise of driver-dominated vehicle movement, the virtual decision-making of autonomous driving systems under the accompanying state was considered as a reference. Based on a dynamic time warping algorithm and forming a data filtering approach under a dynamic time window, an automatic trigger recording control model for human-vehicle difference feature data was suggested. In this method, the data dimension was minimized, and the efficiency of the data mining was improved. The experimental findings showed that the suggested model decreased recorded invalid data by 75.35% on average and saved about 2.65 TB of data storage space per hour. Compared with industrial-grade methods, it saves an average of 307 GB of storage space per hour.

Electric vehicles (EVs) in severe cold regions face the real demand for fast charging under low temperatures, but low-temperature environments with high C-rate fast charging can lead to severe lithium plating of the anode material, resulting in rapid degradation of the lithium-ion battery (LIB). In this paper, by constructing an electrode–thermal model coupling solid electrolyte interphase (SEI) growth and lithium plating, the competition among different factors of capacity degradation under various ambient temperatures and C-rates are systematically analyzed. In addition, the most important cause of rapid degradation of LIBs under low temperatures are investigated, which reveal the change pattern of lithium plating with temperature and C-rate. The threshold value and kinetic law of lithium plating are determined, and a method of lithium-free control under high C-rate is proposed. Finally, by studying the average aging rate of LIBs, the reasons for the abnormal attenuation of cycle life at lower C-rates are ascertained. Through the chromaticity diagram of the expected life of LIBs under various conditions, the optimal fast strategy is explored, and its practical application in EVs is also discussed. This study can provide a useful reference for the development of high-performance and high-safety battery management systems to achieve fine management.

The 3D point cloud data collected by 3D lidars have become a significant resource for autonomous vehicles to acquire road information. But these data tend to be inaccurate due to the turning or moving of autonomous vehicles while the lidar is working. Moreover, the traditional Euclidean clustering algorithm often causes false detection in the vicinity or missed detection in the distance if the Euclidean distance threshold is not selected properly. This paper proposes a method which contains three main steps to solve these problems: First, a de-distortion algorithm is applied to diminish the influence of distortion caused by the moving and turning of lidar; second, optimize the structure of the Euclidean clustering algorithm to make it run faster; and third, we applied an adaptive threshold of the Euclidean distance in the improved algorithm, so the improved algorithm is able to detect the relatively small objects in the distance while it can also detect the objects nearby without misjudgment. These features are all confirmed by the results of vehicle experiments which shows that with the help of proposed method, the distortion of point cloud reduced, the detection distance has increased by nearly 8 meters, and the clustering speed has also increased by 15%.

Summary: Lithium-ion batteries are applied in electric vehicles to mitigate climate change. However, their practical applications are impeded by poor safety performance owing mainly to the cell eruption gas (CEG) fire triangle. Here, we report quantitatively the three fire boundaries corresponding to the CEG fire triangle of four types of mainstream cells with the state of charge (SOC) values ranging from 0% to 143% based on 29 thermal runaway tests conducted in an inert atmosphere in open literature. Controlling the SOC and/or selecting a reasonable cell type can alter the minimum CEG and oxygen concentrations required for ignition, thereby changing the probability of a battery fire. The ignition temperature varies greatly according to the type of ignition source type. Temperature and ignition source type play a leading role in the ignition mode. Breaking any fire boundary will stop the ignition of CEG, thus significantly improving the battery safety performance.

The automatic stop-and-go task of intelligent vehicles can make the adaptive cruise control system achieve a full-speed range. However, the conventional design methods mostly focus on functional safety, without considering drivers’ behaviors, thereby leading to a poor driving experience. To improve the situation, a humanized learning control model is used instead of mechanical switching logic. Therefore, first, the common characteristics of human drivers with different driving styles are found by analyzing real drivers’ experiments. Then, the vehicle automatic starting function is designed based on iterative learning control with the fast Fourier transform for acceleration fitting. Next, the vehicle automatic braking function is designed based on dynamic time to collision. Finally, the simulation of the stop-and-go scenario is shown in CARSIM, and the real vehicle test is performed under the urban overpass driving condition. Results show that the proposed model can improve the humanization in the vehicle stop-and-go task.

In this article we used simulator experiments to explore the intelligent mechanisms of human decision-making. Three types of typical emergency scenarios were used in the experiment, in which Scenario 1 was used to analyze the driver’s choice to protect themselves or to protect pedestrians in emergency situations. Scenario 2 was compared with Scenario 1 to verify whether the driver’s avoidance behavior to protect pedestrians was instinctive or selective. Scenario 3 was to verify whether the driver would follow the principle of damage minimization. The driver’s decisions and actions in emergency situations, from the cumulative frequency of time to collision (TTC) to the maximum steering wheel angle rate during the experiments, were recorded. The results show that the driver was not just instinctively avoiding the immediate obstacle, but more selectively protecting pedestrians. At the same time, the time taken up by the driver’s instinctive avoidance response also had a negative impact on decision-making. The actual decisions of the driver were analyzed to provide a basis for building up the ethical decision-making of autonomous vehicles.

Skilled drivers have the driving behavioral characteristic of pre-sighted following, and similarly intelligent vehicles need accurate prediction of future trajectories. The LSTM (Long Short-Term Memory) is a common model of trajectory prediction. The existing LSTM models pay less attention to the interactions between the target and the surrounding vehicles. Furthermore, the impacts on future trajectories of the target vehicle have also barely been a focus of the current models. On these bases, a Residual Attention-based Long Short-Term Memory (RA-LSTM) model was proposed, an interaction tensor based on the surroundings of the target vehicle at the predictive moments was constructed and the weight coefficients of the interaction tensor for the surrounding vehicles relative to the target vehicle were calculated and re-programmed in this study. The proposed RA-LSTM model can implicitly represent the different degrees of influence of the surrounding vehicles on the target vehicle; the probability distributions of the future trajectory coordinates of the target vehicle is predicted based on the extracted interaction features. The RA-LSTM model was tested and verified in multiple scenarios by using the NGSIM (next generation simulation) public dataset, and the results showed that the prediction accuracy of the proposed model is significantly improved compared with the current LSTM models.

With the development of intelligent vehicle technology, the demand for advanced driver assistant systems kept increasing. To improve the performance of the active safety systems, we focused on right-turning vehicle’s collision warning and avoidance. We put forward an algorithm based on Monte Carlo simulation to calculate the collision probability between the right-turning vehicle and another vehicle (or pedestrian) in intersections. We drew collision probability curves which used time-to-collision as the horizontal axis and collision probability as the vertical axis. We established a three-level collision warning system and used software to calculate and simulate the collision probability and warning process. To avoid the collision actively when turning right, a two-stage braking strategy is applied. Taking four right-turning collision conditions as examples, the two-stage braking strategy was applied, analysing and comparing the anteroposterior curve diagram simultaneously to avoid collision actively and reduce collision probability. By comparison, the collision probability 2 s before active collision avoidance was more than 80% and the collision probability may even reach 100% in certain conditions. To improve the active safety performance, the two-stage braking strategy can reduce the collision probability from exceeding 50% to approaching 0% in 2 s and reduce collision probability to less than 5% in 3 s. By changing four initial positions, the collision probability curve calculation algorithm and the two-stage braking strategy are validated and analysed. The results verified the rationality of the collision probability curve calculation algorithm and the two-stage braking strategy.

Lateral velocity is an important parameter to characterize vehicle stability. The acquisition of lateral velocity is of great significance to vehicle stability control and the trajectory following control of autonomous vehicles. Aiming to resolve the problems of poor estimation accuracy caused by the insufficient modeling of traditional model-based methods and significant decline in performance in the case of a change in road friction coefficient, a deep learning method for lateral velocity estimation using an LSTM, long-term and short-term memory network, is designed. LSTM can well reflect the inertial characteristics of vehicles. The training data set contains sensor data under various working conditions and roads. The simulation results show that the prediction model has high accuracy in general and robustness to the change of road friction coefficient.

A global reference path generated by a path search algorithm based on a road-level driving map cannot be directly used to complete the efficient autonomous path-following motion of autonomous vehicles due to the large computational load and insufficient path accuracy. To solve this problem, this paper proposes a lane-level bidirectional hybrid path planning method based on a high-definition map (HD map), which effectively completes the high-precision reference path planning task. First, the global driving environment information is extracted from the HD map, and the lane-level driving map is constructed. Real value mapping from the road network map to the driving cost is realized based on the road network information, road markings, and driving behavior data. Then, a hybrid path search method is carried out for the search space in a bidirectional search mode, where the stopping conditions of the search method are determined by the relaxation region in the two search processes. As the search process continues, the dimension of the relaxation region is updated to dynamically adjust the search scope to maintain the desired search efficiency and search effect. After the completion of the bidirectional search, the search results are evaluated and optimized to obtain the reference path with the optimal traffic cost. Finally, in an HD map based on a real scene, the path search performance of the proposed algorithm is compared with that of the simple bidirectional Dijkstra algorithm and the bidirectional BFS search algorithm. The results show that the proposed path search algorithm not only has a good optimization effect, but also has a high path search efficiency.

In the development of autonomous driving, decision-making has become one of the technical difficulties. Traditional rule-based decision-making methods lack adaptive capacity when dealing with unfamiliar and complex traffic conditions. However, reinforcement learning shows the potential to solve sequential decision problems. In this article, an independent decision-making method based on reinforcement Q-learning is proposed. First, a Markov decision process model is established by analysis of car-following. Then, the state set and action set are designed by the synthesized consideration of driving simulator experimental results and driving risk principles. Furthermore, the reinforcement Q-learning algorithm is developed mainly based on the reward function and update function. Finally, the feasibility is verified through random simulation tests, and the improvement is made by comparative analysis with a traditional method.

Brain-like intelligent decision-making is a prevailing trend in today’s world. However, inspired by bionics and computer science, the linear neural network has become one of the main means to realize human-like decision-making and control. This paper proposes a method for classifying drivers’ driving behaviors based on the fuzzy algorithm and establish a brain-inspired decision-making linear neural network. Firstly, different driver experimental data samples were obtained through the driving simulator. Then, an objective fuzzy classification algorithm was designed to distinguish different driving behaviors in terms of experimental data. In addition, a brain-inspired linear neural network was established to realize human-like decision-making and control. Finally, the accuracy of the proposed method was verified by training and testing. This study extracts the driving characteristics of drivers through driving simulator tests, which provides a driving behavior reference for the human-like decision-making of an intelligent vehicle.

Simultaneous localization and mapping have become a basic requirement for most automatic moving robots. However, the LiDAR scan suffers from skewing caused by high-acceleration motion that reduces the precision in the latter mapping or classification process. In this study, we improve the quality of mapping results through a de-skewing LiDAR scan. By integrating high-sampling frequency IMU (inertial measurement unit) measurements and establishing a motion equation for time, we can get the pose of every point in this scan’s frame. Then, all points in this scan are corrected and transformed into the frame of the first point. We expand the scope of optimization range from the current scan to a local range of point clouds that not only considers the motion of LiDAR but also takes advantage of the neighboring LiDAR scans. Finally, we validate the performance of our algorithm in indoor and outdoor experiments to compare the mapping results before and after de-skewing. Experimental results show that our method smooths the scan skewing on each channel and improves the mapping accuracy.

Driving characteristics of lane departure can provide design principles for lane departure warning system and lane keeping assist system. In this article, based on driving simulator experiments of unintentional lane departure, the operation performances of human drivers, the motion characteristics of vehicles, and the relative motion between the vehicle and lane line are synthetically studied. First, unintentional lane departure is classified into lane departure by fatigue and lane departure by secondary task. Subsequently, two simulator experiments of fatigue-based lane departure and secondary task–based are designed and performed to collect the synchronous driver–vehicle–road data. The data of steering angle, steering angle velocity, steering angle entropy, lateral acceleration, lateral velocity, and yaw velocity, under fatigue-based and secondary task–based lane departure are collected and compared with those gathered under normal lane changing. Results show that the characteristics of unintentional lane departures differ from that of normal lane departure changing. Furthermore, the characteristics of fatigue-based lane departures are shown some differences with that of secondary task–based ones.

We present a novel pedestrian count estimation approach based on global image descriptors formed from multi-scale texture features that considers spatial distribution. For regions of interest, local texture features are represented based on histograms of multi-scale block local binary pattern, which jointly constitute the feature vector of the whole image. Therefore, to achieve an effective estimation of pedestrian count, principal component analysis is used to reduce the dimension of the global representation features, and a fitting model between image global features and pedestrian count is constructed via support vector regression. The experimental result shows that the proposed method exhibits high accuracy on pedestrian count estimation and can be applied well in the real world.

The study on the damage tolerance and failure mechanism of lithium-ion batteries (LIBs) subject to mechanical attack has attracted considerable attention. The electrochemical performance and thermal behavior of LIB were significantly affected by operation temperature and charging rate, but the dependence of these two factors on mechanical response remains unclear. Hence, we investigated how the environmental temperatures and rates in charging process affected the mechanical response characteristics of 18650 LIB cells. The onset of the short circuit in the cells which charged at temperatures above −25 °C occurred around their modulus peak under compression. At −25 °C, there was a strong possibility that a premature short circuit occurred locally in the cells during charging, thus they might show complex and variable mechanical response under compression. The failure moduli and crushing stresses of cells subject to compression tended to decrease as their ambient charging temperatures went down. Besides, 0.5 C-charged cells exhibited higher failure moduli and crushing stresses than the 1 C-charged cells above −20 °C. Morphology analyses of the cell electrode surfaces revealed that mossy lithium deposits became evident at temperatures below −10 °C. Furthermore, their distribution was uniform. Mechanical results also indicated that the short-term cycling at −20 °C and 0.5 C would soften the cell.

Driving characteristics of human drivers, such as driving safety, comfort, handiness, and efficiency, which are interrelated and contradictory, are synthetically considered to maintain a safe inter-vehicle distance in this article. For the multi-objective coordination control problem, the safety, handiness, comfort, and efficiency indicators are established via driving states and manipulated variable. Furthermore, a multi-performance indicator coordination mechanism is proposed via the invariant set and quadratic boundedness theory. A headway control algorithm for adaptive cruise control is established under the dynamic output feedback control framework. Finally, feasibility and effectiveness of the proposed algorithm are verified via closed-loop simulations under the following, cut-out, and cut-in typical operating conditions.

CDK9 is the kinase subunit of positive transcription elongation factor b (P-TEFb) that enables RNA polymerase (Pol) II's transition from promoter-proximal pausing to productive elongation. Although considerable interest exists in CDK9 as a therapeutic target, little progress has been made due to lack of highly selective inhibitors. Here, we describe the development of i-CDK9 as such an inhibitor that potently suppresses CDK9 phosphorylation of substrates and causes genome-wide Pol II pausing. While most genes experience reduced expression, MYC and other primary response genes increase expression upon sustained i-CDK9 treatment. Essential for this increase, the bromodomain protein BRD4 captures P-TEFb from 7SK snRNP to deliver to target genes and also enhances CDK9's activity and resistance to inhibition. Because the i-CDK9-induced MYC expression and binding to P-TEFb compensate for P-TEFb's loss of activity, only simultaneously inhibiting CDK9 and MYC/BRD4 can efficiently induce growth arrest and apoptosis of cancer cells, suggesting the potential of a combinatorial treatment strategy.

We improve the typical GM car-following model. In order to describe the impacts of the lateral offset between the vehicles in car-following state, this paper proposes time distance to measure the drivers' attention of driving environment and uses generalized expectation headway to describe the ideal driving state of the driver. Take the difference between the actual and expected headway as the important standard for driver to decide whether to speed up or slow down. Then take the drivers' attentions and the expected headway as the decision-making factors, and consider them while revising the GM car-following model. The numerical simulation shows the improved GM model conforms to the general driving behavior, enhances the function of the typical model, and strengthens the model stability.

For expanding the application scope of car-following, based on the basic idea of the noncontact interaction of the objects in physics, establish an attention field model to describe the driving behavior. Firstly, propose the time distance concept to describe the degree of driver perception to the front one-dimensional space and extend its application range to the two-dimensional space. Secondly, connect the point which has the same time distance to constitute the equipotential line of drivers’ attention field equipotent, and establish a model to describe it. Thirdly, define the effective range of the driver’s psychological field with the feature of the driver’s visual distance range increasing and the angle decreasing. Finally, design the calculation method to collect projection of the object in the psychological field scope and calculate the curve points to determine the object’s intensity of psychological field. Preliminarily build the driving behavior model and use the numerical simulation method to simulate the simple transport scenarios; initially verify the validity of the model.