Studies on autonomous driving systems are being conducted to realize a safe and secure mobile society. According to these studies, the safety of the system and the driver’s feeling of safety often do not match. To enable automatic driving control according to the driver's comfort, previous researchers introduced a risk feeling index to quantify the driver’s feeling of safety. These studies, however, did not consider the effects of the drivers’ individual differences for the risk feeling index. We hypothesized that the risk perception was formed by the difference between the actual autonomous driving behavior and the driver's prediction for it. The driver's risk feeling is expected to vary depending on his/her prior prediction of autonomous driving behavior. We considered that this difference, which we term as the prediction error, explains an individual difference in the risk perception. The purpose of this study is to reveal the effect of the prediction error on the risk perception. We conducted an experiment to investigate the effect of these factors on the risk feeling in the overtaking scene using a driving simulator. We obtained two types of subjective evaluations to examine the effects of the prediction errors on risk perception. One is perceived risk based on the driver’s original prediction, and the other is perceived risk based on the predictions that we manipulated by conducting a learning session during the experiment. The results suggest that the risk is perceived based on both the experimentally manipulated prediction and the individual driving characteristic of anxiety while driving. These findings will enable the development of a secure automatic driving system that suits each driver by controlling the system appropriately based on the driving characteristic of the driver.
In this investigation, the planetary gear differential is intended to the independently rotating wheels as a passive control device of the left and right wheels. Based on dynamics and kinematics analysis of the railway vehicle system and differential system, the differential coupling wheels vehicle (DWV) model and the comparative models, including rigid-wheelset vehicle (RWV) model and the independently rotating wheels vehicle (IRWV) model, are built. Through numerical studies, it can be concluded that the longitudinal creep forces of independently rotating wheels disappear for the separation of the wheels. But in the coupling effect of differential on wheels, the differential coupling wheels vehicle regains longitudinal creep forces and the resetting capability on straight lines. Compared with the rigid-wheelset vehicle, the differential coupling wheels vehicle has superior dynamics, including safety, guiding performance and wear performance on sharp curves. However, due to lack of sufficient longitudinal creep forces, the dynamics of the differential coupling wheels vehicle is slightly worse than that of the rigid-wheelset vehicle on medium radius curves. In general, the differential coupling wheels vehicle solves the problem of guiding and safety of the independently rotating wheels vehicle, and has better dynamic performance than the rigid-wheelset vehicle on sharp curves, which indicates that the differential coupling wheels vehicle is applicable to the urban railway transit which contains many sharp curves.
In aluminum alloy double pulse metal inert-gas (DPMIG) welding, the outputting arc current waveform is distorted under the influence of various factors such as harmonics and impact loads. The arc distortion of aluminum alloy DPMIG welding affects the arc stability and welding quality. Based on the collected welding current signal, a feature extraction method is proposed for quality detection of the aluminum alloy DPMIG welding. The wavelet method is adopted to eliminate the noise of the welding current signal. The local mean decomposition (LMD) is performed to the welding current signal to obtain a series of Product Function (PF) components with real physical meaning. The Hilbert transformation is subsequently performed to the PF components to obtain the time frequency distribution of welding arc signal energy. The approximate entropy (ApEn) of the time frequency distribution of the welding current signal is calculated to evaluate the arc stability and the welding formation quality. Application of the proposed feature extraction method indicates that the combination of the wavelet and LMD can effectively extract the distortion components of the welding current signal. The time frequency distribution of the PF components of the welding current can clearly reflect the concentration and dispersion of the arc energy. The approximate entropy of the time frequency distribution of the welding current can be quantitively reflect the arc stability and the welding formation quality in the aluminum alloy double pulse MIG welding.