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The drivers' fatigue ratings were associated with vibration frequencies in simulated driving. The study quantitatively demonstrated that different effects on autonomic nerve activities were induced by different vibration frequencies.

The object of this study was to assess the effects of magnitopuncture applied to Dazhui (DU14) point and Neiguan (PC6) points on sympathetic and parasympathetic nerve activities by power spectrum analysis of heart rate variability in healthy drivers during simulated driving. Using power spectrum analysis, the low frequency (LF) and high frequency (HF) components of heart rate variability can be calculated reflecting the sympathetic and parasympathetic activity. The 40 healthy male subjects were randomly divided into two groups: A (study group) and B (control group). All subjects were required to be well rested before the experiment. The subjects of both groups were required to perform a simulated driving task for 3 h. During the driving, magnitopunctures were applied to the DU14 and PC6 points for A while the subject performed the task for 2.5 h, and for B magnitopunctures were applied to non-acupuncture points which were 1.5 cm away from the two acupuncture points respectively over the same time. Subjective response to a questionnaire was obtained after the simulated task in the two groups. At the end of the driving task the LF component in normalized units (NU) had decreased significantly ( P<0.05) indicating a reduced sympathetic nerve activity and the HF component (NU) increased significantly ( P<0.05) indicating a increased parasympathetic nerve activity for A compared with pre-stimulation while for B no significant differences were observed. There were significant group differences in LF (NU), HF (NU) and LF:HF at the end of the driving task ( P<0.05). It was concluded that a modulating effect of magnitopuncture on sympathetic and parasympathetic nerve activities in healthy subjects was associated with the acupuncture points. The findings represent physiological evidence that magnitopuncture may reduce mental fatigue in healthy drivers.

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With the rapid development of automated vehicles (AVs), more and more demands are proposed towards environmental perception. Among the commonly used sensors, MMW radar plays an important role due to its low cost, adaptability In different weather, and motion detection capability. Radar can provide different data types to satisfy requirements for various levels of autonomous driving. The objective of this study is to present an overview of the state-of-the-art radar-based technologies applied In AVs. Although several published research papers focus on MMW Radars for intelligent vehicles, no general survey on deep learning applied In radar data for autonomous vehicles exists. Therefore, we try to provide related survey In this paper. First, we introduce models and representations from millimeter-wave (MMW) radar data. Secondly, we present radar-based applications used on AVs. For low-level automated driving, radar data have been widely used In advanced driving-assistance systems (ADAS). For high-level automated driving, radar data is used In object detection, object tracking, motion prediction, and self-localization. Finally, we discuss the remaining challenges and future development direction of related studies.