Traffic accidents due to fatigue account for a large proportion of road fatalities. Based on simulated driving experiments with drivers recruited from college students, this paper investigates the use of heart rate variability (HRV) features to detect driver fatigue while considering sex differences. Sex-independent and sex-specific differences in HRV features between alert and fatigued states derived from 2 min electrocardiogram (ECG) signals were determined. Then, decision trees were used for driver fatigue detection using the HRV features of either all subjects or those of only males or females. Nineteen, eighteen, and thirteen HRV features were significantly different (Mann–Whitney U test, p < 0.01) between the two mental states for all subjects, males, and females, respectively. The fatigue detection models for all subjects, males, and females achieved classification accuracies of 86.3%, 94.8%, and 92.0%, respectively. In conclusion, sex differences in HRV features between drivers’ mental states were found according to both the statistical analysis and classification results. By considering sex differences, precise HRV feature-based driver fatigue detection systems can be developed. Moreover, in contrast to conventional methods using HRV features from 5 min ECG signals, our method uses HRV features from 2 min ECG signals, thus enabling more rapid driver fatigue detection.

Motion sickness is a common disease encountered in traditional vehicles as well as autonomous vehicles, which will negatively affect user acceptance. To make clear the pathogenesis of motion sickness, this study focused on drivers’ brain activity changes before and after motion sickness happens. Based on the six-degree-of-freedom driving simulator and noninvasive functional near-infrared spectroscopy (fNIRS), a database containing driving operation data synchronized with drivers’ brain activity record from 52 participants was collected under straight and curved driving conditions. The correlation analysis between motion sickness and changes of cerebral oxyhemoglobin concentration in the cerebral cortex was carried out based on this database. Results suggest that brain activity associated with motion sickness may differ under different driving conditions. However, the emergence of motion sickness responses is related to the occipital lobe under both driving conditions. Experimental results corroborate with several theoretical hypothesis about motion sickness in neuroscience. Consequently, this study proposes a new approach to research the mechanism of the correlation between motion sickness and cerebral cortex activity, which will contribute to developing the driving assistance system for preventing or alleviating motion sickness in autonomous vehicles.

Cooperative driving refers to a notion that advanced driver assistance system (ADAS) sharing the control with human driver and completing driving task together. One of the key technologies is that the ADAS can identify the driver's driving intention in real time to implement consistent driving decisions. Based on driving simulator (DS) and functional near‐infrared spectroscopy (fNIRS) experiments, this study established a model of driver's brake intention identification with machine‐learning algorithms and analysed cerebral cortex activity mechanism of the driver's brake intention with parametric test. This study suggested that the test accuracy of the model established here was 90.91%. Moreover, the activity in the Brodmann area 7 (BA7), BA17, and BA40 in cerebral cortex was significantly different between the driver with braking intention and those with driving at constant speed ( p < 0.05). The study presented here not only identified the driver's driving intention through fNIRS for the first time, but also analysed the brain activity of the drivers when he had the braking intention. It lays a foundation for the future research on the driving cognitive model about human perception and driver's driving intention identification.

The development of a machine-learning method with high accuracy, high generalization, and strong robustness for evaluating battery health states is essential in the field of battery health management. In this work, the data-driven stacking regressor (SR) method with a two-layer diagnostic framework was proposed to estimate the state of health (SOH) and predict the remaining useful life (RUL). Five individual estimators were merged in the first layer, including bagging, gradient boosting regression (GBR), support vector regression (SVR), Hist-GBR, and AdaBoost, and linear regression (LR) was used in the second layer to construct the SR model. The SR model produces highly accurate results without the requirement of excessive parameter adjustment. Fifteen batteries from the NASA dataset were used for our experiments, resulting in rather low values of average root mean square error (ARMSE) and relative error (RE) for the SOH estimation and RUL predictions of the different batteries, demonstrating the superiority of the SR model.

Semantic Change Detection (SCD) based on remote sensing images can provide an effective solution for large-scale land use monitoring. Existing change detection networks face limitations due to their limited receptive fields, which make it difficult to provide a comprehensive and consistent response to changes in similar large objects. Moreover, these limitations often cause the network to ignore weak changes localized in the images. To address these limitations, we propose CG-SPNet, a decoupled multi-task architecture that integrates three key components, including the multiscale positional similarity pyramid (MSSP), the symmetric fusion change detection unit (SFCD), and the change-guided feature interaction module (CGM). MSSP module captures positional correlation of semantic features at multiple scales by introducing an attention pyramid during pooling down-sampling. SFCD utilizes a convolutional attention mechanism to enhance local features and highlight weak changes in dual images. And the symmetric fusion approach reduces the network’s sensitivity to timing. CGM is used to address the challenge of effective feature interaction, which combines cosine similarity loss to leverages cross-attention to compute the positional relevance of change features to embed a priori information for semantic features. Through rigorous experiments and analyses, we have successfully validated the essential components of CG-SPNet. It achieves state-of-the-art performance on the SECOND dataset as well as our self-built CZWZ dataset.

Vision-based localization techniques and detection technologies are key algorithms for the localization and navigation of unmanned vehicles. Especially in scenarios where GPS signals are missing, Simultaneous Localization and Mapping (SLAM) techniques that rely on vision, inertial navigation system (INS) and other sensors have important applications. Among them, vision combined with the IMU SLAM system has the advantage of realistic scale, which is lacking in monocular vision and computational power compared to multi-visual vision, so it is suitable for application in an unmanned vehicle system. In this paper, we propose a fusion localization algorithm that combines a visual-inertial SLAM system and map road information, processing road information in a map under structured roads, and detecting lane lines and locating its local position by a monocular camera, applying a strategy of position prediction and update for map-SLAM fusion localization. It solves the problem of accumulating errors in a pure SLAM system without loopback and provides accurate global-local positioning results for unmanned vehicle positioning and navigation.