Analyzing the Effects of Driving Experience on Prebraking Behaviors Based on Data Collected by Motion Capture Devices

Abstract

The prebraking-related actions typically studied are the main maneuvers carried out to avoid collision. Especially for those braking actions taken when turning or parking, accidents often occur because of human errors such as the incorrect choice of pedal. However, regarding these daily braking-related driving behaviors, the effects of the driver characteristics, such as driving experience and gender, on the prebraking behaviors remain unknown. Therefore, defining prebraking behaviors as the movements of a driver's body before his or her foot touches the brake pedal, this paper identifies the details of drivers' driving behaviors while prebraking by analyzing the data collected from a wearable high-precision 23-joint motion capture device and further confirms the effects of driver experience, gender and stature on these behaviors. According to two-way analyses of variance (ANOVAs) that were performed on 100 sets of motion data collected from a set of driving experiments involving two different tasks, drivers perform similar prebraking body actions even under different braking scenarios. Moreover, the results of an interaction effects analysis confirmed the impact of drivers' experiences, gender and stature on their prebraking actions. The results of this study can serve as guidelines for future self-driving and advanced driver assistance system (ADAS) development and provide useful insights for the identification and training of new drivers.

Pre-braking behaviors analysis based on Hilbert–Huang transform

“…For example, the results indicated that the experienced drivers had a higher score on the measures of kfs2 and kas2, which means that for the sub-action IMF2, the experienced drivers' knee joint's changing movements were heavier than for the novices. These results are consistent with our previous studies (Wu et al 2020b) that during prebraking, experienced drivers' foot movement distance was longer than the novice for safety (an experienced driver's foot is kept away from the pedals to ensure the pedals are not depressed by accident), so experienced drivers needed to move their foot to the brake pad with a higher speed. Based on our assumptions about experienced drivers (i.e., experienced drivers are safer and rationalize their driving behaviors), it can be deduced from the results that the IMF2 sub-action may be a relatively necessary safety assurance action during pre-braking.…”

“…Meanwhile, compared with IMF1, we also found that its AMP presented a drastically decreasing trend, and the stronger AMP occurred outside the P period (focus on the front), which could mean that the actions represented by IMF2 occurred mainly during the lift phase of the subject's foot. Combined with the results of statistical analysis and the conclusions of our previous research (Wu et al 2020b), we speculate that IMF2 represents the transverse movement of the driver's foot. As a safety precaution, some drivers keep their feet as far away from the brake pad as possible.…”

“…According to our previous studies (Wu et al 2020b) and related research (Lyu et al 2017(Lyu et al , 2018Hault-Dubrulle et al 2011), drivers' experience, gender, and physique as human factors may affect a driver's braking behavior and thus his driving safety. However, most studies have mounted sensors on the brake pad.…”

Comprehensive Analysis of Minibuses Gravity Center: A Post-Production Review for Car Body Industry

“…From the calculation results, the values of the longitudinal and lateral distances for the test vehicle type without load and full load are shown in Table 4 for the longitudinal distance and Table 5 for the lateral distance. The calculation of the height or distance of the center of gravity from the ground surface h0 is determined in several stages, namely starting with determining the angle of inclination through Equation (3). By using Equation (3), where the front wheel is raised as high as H = 400 mm, while the front and rear axle distances are L = 3850 mm, then angle = 0.104 radial = 5.964 o .…”

Deriving Driver Behavioral Pattern Analysis and Performance Using Neural Network Approaches

“…It does not evaluate the risk perception k-NN classifier [31] Analyze the movement of neighboring vehicles while shifting the driver's sight Use a longer interval without analyzing gaze Advanced driver assistance systems (ADASs) [32] It defines the "braking" behavior as the driver's body movement It is difficult to identify specific drivers and predict their behavior braking Convolutional neural network (CNN) [33] Founded on the analysis of time and space characteristics of the -driving state Adjacent levels of attention rating are not clear Neural network algorithm [5] To be more observant and concurrently conscious of numerous vehicles positions in order to react swiftly The problem of sensing abnormal driver behavior with the support of face netting and analyzation is tough Feedforward neural network (FFNN) [6] Identification task to identify evaluations with different characteristics It is difficult to collect more real-world data sets and identify deeper driver behavior.…”