Driving style is a very important indicator and a crucial measurement of a driver's performance and ability to drive in a safe and protective manner. A dangerous driving style would possibly result in dangerous behaviors. If the driving styles can be recognized by some appropriate classification methods, much attention could be paid to the drivers with dangerous driving styles. The driving style recognition module can be integrated into the advanced driving assistance system (ADAS), which integrates different modules to improve driving automation, safety and comfort, and then the driving safety could be enhanced by pre-warning the drivers or adjusting the vehicle's controlling parameters when the dangerous driving style is detected. In most previous studies, driver's questionnaire data and vehicle's objective driving data were utilized to recognize driving styles. And promising results were obtained. However, these methods were indirect or subjective in driving style evaluation. In this paper a method based on objective driving data and electroencephalography (EEG) data was presented to classify driving styles. A simulated driving system was constructed and the EEG data and the objective driving data were collected synchronously during the simulated driving. The driving style of each participant was classified by clustering the driving data via K-means. Then the EEG data was denoised and the amplitude and the Power Spectral Density (PSD) of four frequency bands were extracted as the EEG features by Fast Fourier transform and Welch. Finally, the EEG features, combined with the classification results of the driving data were used to train a Support Vector Machine (SVM) model and a leave-one-subject-out cross validation was utilized to evaluate the performance. The SVM classification accuracy was about 80.0%. Conservative drivers showed higher PSDs in the parietal and occipital areas in the alpha and beta bands, aggressive drivers showed higher PSD in the temporal area in the delta and theta bands. These results imply that different driving styles were related with different driving strategies and mental states and suggest the feasibility of driving style recognition from EEG patterns.

Unmanned aerial vehicles (UAVs) face significant challenges in maintaining stability when subjected to external wind disturbances and internal noise. This paper addresses these issues by introducing a real-time wind speed fitting algorithm and a wind field model that accounts for varying wind conditions, such as wind shear and turbulence. To improve control in such conditions, a deep reinforcement learning (DRL) strategy is developed and tested through both simulations and real-world experiments. The results indicate a 65% reduction in trajectory tracking error with the DRL controller. Additionally, a UAV built for testing exhibited enhanced stability and reduced angular deviations in wind conditions up to level 5. These findings demonstrate the effectiveness of the proposed DRL-based control strategy in increasing UAV resilience to wind disturbances.

Parkinson's disease (PD) is a neurodegenerative disorder affecting middle-aged and elderly people. PD can be viewed as "circuit disorder", indicating that large scale cortico-subcortical pathways were involved in its pathophysiology. The brain network in an experimental context is emerging as an important biomarker in disease diagnosis and prognosis prediction. This context-dependent network for PD and the underling functional mechanism remains unclear. In this paper, the brain network profiles in 11 PD patients without dementia were studied and compared with 12 healthy controls. The functional magnetic resonance imaging (fMRI) data were acquired when the subjects were performing a pseudorandomized unimanual or bimanual finger-to-thumb movement task. The activation was detected and the network profiles were analysed by psychophysiological interaction (PPI) toolbox. For the controls and PD patients, the motor areas including the primary motor and premotor areas, supplementary motor area, the cerebellum and parts of the frontal, temporal and parietal gyrus were activated. The right putamen exhibited significant control > PD activation and weaker activity during the bimanual movement relative to the unimanual movement in the control group. The decreased putamen modulation on some nucleus in basal ganglia, such as putamen, thalamus and caudate, and some cortical areas, such as cingulate, parietal, angular, frontal, temporal and occipital gyrus was detected in the bimanual hand movement condition relative to the unimanual movement condition. Between group PPI difference was detected in cingulate gyrus, angular gyrus and precuneus (control > PD) and inferior frontal gyrus (PD > control). The deficient putamen activation and its enhanced connectivity with the frontal gyrus could be a correlate of impaired basal ganglia inhibition and frontal gyrus compensation to maintain the task performance during the motor programs of PD patients.

To improve the driving performance of vehicles, it is of great significance to study the changes in the driver’s brain cognition during driving and to establish an intelligent driving computational framework based on the cognitive process. Electroencephalogram (EEG) is an effective means to study driver cognition because of its low cost, high temporal resolution, and different cognitive state information. The application of brain-computer interface(BCI) technology based on EEG signals to driver assistance systems has the potential to transform the way humans interact with vehicles. It can also help intelligent vehicles to understand and predict driver’s behavior, and to enhance the cognitive ability of vehicles. This paper reviews the research on theorizing and modeling driver cognitive processes based on cognitive architectures (e.g., ACT-R, QN, Soar, etc.), and proposes an EEG-based driver cognitive architecture. Then according to the relationship between the modules of this proposed driver cognitive architecture, the driver’s perception of stationary and hazardous scenarios in the driving environment, the understanding of the driver’s intention to control the longitudinal and lateral movements of the vehicle, and the influence of driver’s working memory as well as human factors such as fatigue, distraction, and emotion on driving performance based on EEG signals are reviewed. The integration of EEG signals with cognitive modeling has the potential to improve the accuracy of driver perception, intention, and cognitive state prediction, thereby enhancing vehicle safety.

Drivers play the most important role in the human-vehicle-environment system and driving behaviors are significantly influenced by the cognitive state of the driver and his/her personality. In this paper, we aimed to explore the correlation among driving behaviors, personality and electroencephalography (EEG) using a simulated driving experiment. A total of 36 healthy subjects participated in the study. The 64-channel EEG data and the driving data, including the real-time position of the vehicle, the rotation angle of the steering wheel and the speed were acquired simultaneously during driving. The Cattell 16 Personality Factor Questionnaire (16PF) was utilized to evaluate the personalities of subjects. Through hierarchical clustering of the 16PF personality traits, the subjects were divided into four groups, i.e., the Inapprehension group, Insensitivity group, Apprehension group and the Unreasoning group, named after their representative personality trait. Their driving performance and turning behaviors were compared and EEG preprocessing, source reconstruction and the comparisons among the four groups were performed using Statistical Parameter Mapping (SPM). The turning process of the subjects can be formulated into two steps, rotating the steering wheel toward the turning direction and entering the turn, and then rotating the steering wheel back and leaving the turn. The bilateral frontal gyrus was found to be activated when turning left and right, which might be associated with its function in attention, decision-making and executive control functions in visual-spatial and visual-motor processes. The Unreasoning group had the worst driving performance with highest rates of car collision and the most intensive driving action, which was related to a higher load of visual spatial attention and decision making, when the occipital and superior frontal areas played a very important role. Apprehension (O) and Tension (Q4) had a positive correlation, and Reasoning (B) had a negative correlation with dangerous driving behaviors. Our results demonstrated the close correlation among driving behaviors, personality and EEG and may be taken as a reference for the prediction and precaution of dangerous driving behaviors in people with specific personality traits.