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It is necessary for automated vehicles (AVs) and advanced driver assistance systems (ADASs) to have a better understanding of the traffic environment including driving behaviors. This study aims to build a driving behavior awareness (DBA) model that can infer driving behaviors such as lane change. In this study, a dynamic Bayesian network DBA model is proposed, which includes three layers, namely, the observation, hidden and behavior layer. To enhance the performance of the DBA model, the network structure is optimized by employing a distributed genetic algorithm (GA). Using naturalistic driving data in Beijing, the comparison between the optimized model and other non-optimized models such as the hidden Markov model (HMM) and HMM with a mixture of Gaussian outputs (GM-HMM) indicates that the optimized model could estimate driving behaviors earlier and more accurately.

Situational assessment (SA) is one of the key parts for the application of intelligent alternative-energy vehicles (IAVs) in the sustainable transportation. It helps IAVs understand and comprehend traffic environments better. In SA, it is crucial to be aware of uncertainty-risks, such as sensor failure or communication loss. The objective of this study is to assess traffic situations considering uncertainty-risks, including environment predicting uncertainty. According to the stochastic environment model, collision probabilities between multiple vehicles are estimated based on integrated trajectory prediction under uncertainty, which combines the physics- and maneuver-based trajectory prediction models for accurate prediction results in the long term. The SA method considers the probabilities of collision at different predicting points, the masses, and relative speeds between the possible colliding objects. In addition, risks beyond the prediction horizon are considered with the proposition of infinite risk assessments (IRAs). This method is applied and proved to assess risks regarding unexpected obstacles in traffic, sensor failure or communication loss, and imperfect detections with different sensing accuracies of the environment. The results indicate that the SA method could evaluate traffic risks under uncertainty in the dynamic traffic environment. This could help IAVs’ plan motion trajectories and make high-level decisions in uncertain environments.

Road parameter cognition is essential for intelligent and connected vehicles (ICVs) operating on unstructured roads because it can influence their path planning and motion control. A method is presented for the extraction of longitudinal slopes and lateral slopes and the roughness of unstructured roads using multi‐frame three‐dimensional point clouds. First, a segmentation method based on the multiple features is proposed to extract and divide the regions of interest into blocks with different slopes, which is valuable for further study on ground segmentation. Second, a parameter cognition method is proposed to estimate the slope and roughness of unstructured roads. Last but not least, a multi‐frame fusion method is proposed to improve cognition accuracy. Experimental tests on unstructured roads demonstrate that the proposed algorithm has satisfactory performance in terms of the accuracy of recognizing road slopes and roughness.

The camera is one of the important sensors to realise the intelligent driving environment. It can realise lane detection and tracking, obstacle detection, traffic sign detection, identification and discrimination and visual simultaneous localisation and mapping. The visual sensor model, quantity and installation location are different on different intelligent driving hardware experimental platform as well as the visual sensor information processing module, thus a number of intelligent driving system software modules and interfaces are different. In this study, the software architecture of the autonomous vehicle based on the driving brain is used to adapt to different types of visual sensors. The target segment is extracted by the image segmentation algorithm, and then the segmentation of the region of interest is carried out. According to the input feature calculation results, the obstacle search is done in the second segmentation region, the output of the accessible road area. As driving information is complete, the authors will increase or reduce one or more visual sensors, change the visual sensor model or installation location, which will no longer directly affect the intelligent driving decision, they make the multi-vision sensors adapted to the requirements of different intelligent driving hardware test platforms.

PurposeOver the past decades, there has been significant research effort dedicated to the development of autonomous vehicles. The decision-making system, which is responsible for driving safety, is one of the most important technologies for autonomous vehicles. The purpose of this study is the use of an intensive learning method combined with car-following data by a driving simulator to obtain an explanatory learning following algorithm and establish an anthropomorphic car-following model.Design/methodology/approachThis paper proposed car-following method based on reinforcement learning for autonomous vehicles decision-making. An approximator is used to approximate the value function by determining state space, action space and state transition relationship. A gradient descent method is used to solve the parameter.FindingsThe effect of car-following on certain driving styles is initially achieved through the simulation of step conditions. The effect of car-following initially proves that the reinforcement learning system is more adaptive to car following and that it has certain explanatory and stability based on the explicit calculation of R.Originality/valueThe simulation results show that the car-following method based on reinforcement learning for autonomous vehicle decision-making realizes reliable car-following decision-making and has the advantages of simple sample, small amount of data, simple algorithm and good robustness.

PurposeTo the industrial application of intelligent and connected vehicles (ICVs), the robustness and accuracy of environmental perception are critical in challenging conditions. However, the accuracy of perception is closely related to the performance of sensors configured on the vehicle. To enhance sensors’ performance further to improve the accuracy of environmental perception, this paper aims to introduce an obstacle detection method based on the depth fusion of lidar and radar in challenging conditions, which could reduce the false rate resulting from sensors’ misdetection.Design/methodology/approachFirstly, a multi-layer self-calibration method is proposed based on the spatial and temporal relationships. Next, a depth fusion model is proposed to improve the performance of obstacle detection in challenging conditions. Finally, the study tests are carried out in challenging conditions, including straight unstructured road, unstructured road with rough surface and unstructured road with heavy dust or mist.FindingsThe experimental tests in challenging conditions demonstrate that the depth fusion model, comparing with the use of a single sensor, can filter out the false alarm of radar and point clouds of dust or mist received by lidar. So, the accuracy of objects detection is also improved under challenging conditions.Originality/valueA multi-layer self-calibration method is conducive to improve the accuracy of the calibration and reduce the workload of manual calibration. Next, a depth fusion model based on lidar and radar can effectively get high precision by way of filtering out the false alarm of radar and point clouds of dust or mist received by lidar, which could improve ICVs’ performance in challenging conditions.

Autonomous driving datasets are essential for validating the progress of intelligent vehicle algorithms, which include localization, perception, and prediction. However, existing datasets are predominantly focused on structured urban environments, which limits the exploration of unstructured and specialized scenarios, particularly those characterized by significant dust levels. This paper introduces the LiDARDustX dataset, which is specifically designed for perception tasks under high-dust conditions, such as those encountered in mining areas. The LiDARDustX dataset consists of 30,000 LiDAR frames captured by six different LiDAR sensors, each accompanied by 3D bounding box annotations and point cloud semantic segmentation. Notably, over 80% of the dataset comprises dust-affected scenes. By utilizing this dataset, we have established a benchmark for evaluating the performance of state-of-the-art 3D detection and segmentation algorithms. Additionally, we have analyzed the impact of dust on perception accuracy and delved into the causes of these effects. The data and further information can be accessed at: https://github.com/vincentweikey/LiDARDustX.

The continuously growing importance of information storage, transmission, and authentication impose many new demands and challenges for modern nano-photonic materials and information storage technologies, both in security and storage capacity. Recently, luminescent lanthanide-doped nanomaterials have drawn much attention in this field because of their photostability, multimodal/multicolor/narrowband emissions, and long luminescence lifetime. Here, we report a multimodal nanocomposite composed of lanthanide-doped upconverting nanoparticle and EuSe semiconductor, which was constructed by utilizing a cation exchange strategy. The nanocomposite can emit blue and white light under 365 and 394 nm excitation, respectively. Meanwhile, the nanocomposites show different colors under 980 nm laser excitation when the content of Tb3+ ions is changed in the upconversion nanoparticles. Moreover, the time-gating technology is used to filter the upconversion emission of a long lifetime from Tb3+ or Eu3+, and the possibilities for modulating the emission color of the nanocomposites are further expanded. Based on the advantage of multiple tunable luminescence, the nanocomposites are designed as optical modules to load optical information. This work enables multi-dimensional storage of information and provides new insights into the design and fabrication of next-generation storage materials.Lanthanide-doped heterostructured nanocomposites were developed by using cation exchange method and successfully applied toward advanced optical anti-counterfeiting and information storage

Accurate calibration and validation of remote sensing soil moisture products critically depend on high-quality in situ measurements. However, effectively capturing representative soil moisture patterns across heterogeneous catchments using ground-based sensors remains a significant challenge. To address this, we propose a machine-learning-based framework for optimizing soil moisture sensor network deployment at the catchment scale. The framework was validated using Sentinel-1 SAR-derived soil moisture data within a humid catchment in southern China. Results show that a network of nine optimally placed sensors minimized prediction errors (RMSE: 7.20%), outperforming both sparser and denser configurations. The optimized sensor network achieved a 52.45% reduction in RMSE compared to random placement. Moreover, the optimal number of sensors varied with seasonal dynamics: the wet season required 11 sensors due to increased precipitation-induced spatial variability, whereas the dry season could be adequately monitored with only six sensors. The proposed optimization approach offers a cost-effective strategy for collecting reliable in situ data, which is essential for improving the accuracy and applicability of remote sensing products in catchment-scale soil moisture monitoring.

Acute lung injury/acute respiratory distress syndrome (ALI/ARDS) undergoes the process of pathological event including lung tissue dysfunction, pulmonary edema, and inflammation in sepsis. Autophagy is a cytoprotective process recognized as one of the major pathways for degradation and recycling of cellular constituents. Autophagy as a protective or maladaptive response was still confused in ALI during sepsis. Acute lung injury was performed by cecal ligation and puncture (CLP). Autophagic inducer rapacymin and inhibitor 3-MA and autophagosomal-lysosome fusion inhibitor bafilomucin (Baf) A1 and chloroquine (CQ) were administrated by intraperitoneal injection at 1 h after CLP operation. Microtubule-associated protein light chain 3 II (LC3II), Beclin 1, Rab7, and lysosome-associated membrane protein type 2 (LAMP2) were detected by western blotting. Seven-day survival rate of septic mice was observed. Histologic scores, lung wet-to-dry (W/D) weight ratio, oxygenation index (PaO2/FiO2), total cells and polymorphonuclear neutrophils (PMN) in bronchial alveolar lavage fluid (BALF) and myeloperoxidase (MPO) activity and cytokine tumor necrosis factor (TNF)-α, high-mobility group box (HMGB)1, interleukin (IL)-6, IL-10, and monocyte chemotactic protein (MCP)1 were measured after sham or ALI operation. ALI induced the increasing expression of autophagy-related protein LC3II, Beclin 1, Rab7, and LAMP2 in CLP operation. Autophagic inducer rapacymin significantly induced the expression of LC3II, Beclin 1, LAMP2, and Rab7 in mice model of CLP, and inhibitor 3-MA reduced expression of LC3II, Beclin 1, LAMP2, and Rab7 expressions in CLP + RAP mice compared to CLP group. Compared with ALI group, Baf and CQ obviously elevated the level of LC3II and Beclin 1, and reduced the LAMP2 and Rab7 expressions in CLP + Baf group and ALI + CQ group. Compared with CLP group, autophagic inducer rapacymin improved the survival rate, histologic scores, lung wet/dry weight ratio, PaO2/FiO2, total cells, and PMNS in BALF and MPO activity and cytokines TNF-α, HMGB1, IL-6, IL-10, and MCP1 in CLP + RAP group, but there were exacerbated above indicators in CLP + 3-MA group, CLP + Baf group, and CLP + CQ group. Autophagy activation participated in the pathophysiologic process of sepsis, and alleviated the cytokine excessive release and lung injury in sepsis.