In recent years, the traffic accidents rate and fatality are decreasing year by year. In comparison, the accident rate and fatality caused by two-wheeled vehicles are not decreasing trends. It is also a problem that the development of the safety systems for the two-wheeled vehicles is insufficient compared to that of the four-wheeled vehicles. Therefore, this study has the purpose modeling to predict the collision avoidance ability in case of a risky situation by using driving behaviors that can be obtained in real-time. For the experiment, a dynamic riding simulator that can control rolling motion was constructed, and the experiment was conducted with 18 test subjects (Mean age = 21.83, S.D. = 1.34). In the experiment, the driving behaviors of each emotional state were investigated based on an emotional model consisting of two axes of valence and arousal with sound stimulation and driving conditions. Driving behaviors were quantified using lateral control ability, head motion as confirmation behavior, and emotional state. The correlation between driving behaviors and collision avoidance ability was investigated. Lane position, one of the indicators of lateral control ability, has a quadratic functional correlation of R2 = 0.568, which is more correlated than other indicators. Moreover, multiple regression analysis was conducted using driving behaviors to predict overall collision avoidance ability. As a result, a model was constructed using driving behaviors with real-time measurement, to predict the rider’s collision avoidance ability when risky situations occur (R2 = 0.685, R2 adj = 0.655).
As deep learning methods in image recognition have achieved excellent performance, researchers have begun to apply CNNs(convolutional neural networks) to automated driving. However, the explainability for the decision making of automated driving is highly desired. In order to trust the model in automated driving, visualization methods have become important for understanding the internal calculation process of CNNs. Therefore, in a previous study, we proposed a method to evaluate the visualization performance of CNN models by using a mathematical model instead of a human driver to generate a dataset that can determine the ground-truth point in images. However, the reliability of the proposed method for validating the visualization performance was not provided. Therefore, in this paper, we verify the proposed method through two experiments to demonstrate the task-dependent performance and visualization performance during training. The reliability of the visualization performance has been demonstrated through experimental results. Therefore, we proposed an evaluation method for visualization performance in automated driving systems.
This paper describes a mathematical model that considers the effects of thermal stress and strain caused by thermal expansion and contraction during cooling and the effect of bending by welding electrodes. As a result of predicting the effect of the initial gap between the steel plate and the welding electrode, the so-called clearance, using the model, when the clearance is large, the strain generated at the time of springback is reduced, and it is shown that liquid metal embrittlement cracking is unlikely to occur. This prediction corresponded well with the experimental results. Various effects other than clearance were predicted by the model.
Being able to generate adequate test scenarios in order to validate autonomous driving functions is of paramount importance for the deployment of autonomous vehicles. However, asserting the completeness of generated test cases and coverage of all possibilities has proven to be very difficult. Previously, few attempts that tried to generate representative possibilities have all been based on the designer ′ s experience and thus inherently incomplete. This paper proposes a formal system inspired approach that solves this issue. With this approach, 2,544 basic use-cases were generated from a completeness perspective. Thereafter, for the sake of accelerating the development work of our autonomous vehicle, we used this asset to clarify which use-cases should be solved and in what order.
The diesel engine commonly introduces high boost pressure to achieve high engine efficiency and reduce soot emission. This extra air is supposed to significantly influence mixing process and improve combustion efficiency. In experiment, surrounding gas densities of 11.7 – 46.8 kg/m3 were prepared by rapid compression and expansion machine RCEM. The nozzle hole length to diameter L/D ratio of 2.77 – 6.94 were used corresponding to orifice diameter of 0.072 – 0.180 mm. The high-speed imaging with soot and NOx measurement were arranged. Flame temperature and KL factor were analyzed based on two-color method. The result showed that with identical orifice size nozzle of D=0.180 mm, the shorter hole length nozzle of L=0.5 mm (L/D=2.77) provided higher entrained gas amount and higher vapor fuel/air mixture was achieved at near-field region. This near-field mixture was found to consistently behave throughout the injection period as it was responsible for shorter ignition delay and combustion drastically promoted with shorter combustion duration. At high boosted gas condition, the lower amount of soot produced by the shorter hole length nozzle was prominently exhibited due to strong combustion resulting in higher flame temperature and soot oxidation performed intensively during late combustion phase. Additionally, NOx emission was found to be a function of equivalence ratio of vapor fuel/air mixture at upstream of ignition and strongly related with flame temperature. The combustion and emission showed significant correlation with near-field spray characteristics.
With the commercialization of autonomous driving technology and diversification of how passengers spend their time, it is concerned that a mismatch between somatosensory and visual information induces motion sickness. However, no effective quantitative method using physiological indices in driving conditions of a vehicle has been found out. The objective of this study is to develop a robust method for quantitative evaluation in real driving environment. As a result of inducing motion sickness of a back passenger by driving a vehicle on a test course, changes of forehead humidity, which reflects the amount of sweating, increased significantly. The result indicates forehead humidity is an effective index for quantifying motion sickness in real driving environment.