Human-Machine Interface System for Motorcyclists: Quantification of Total Reliability as Human-Machine System

Lee, Joohyeong; Ozaki, Ikumi; Kishino, Saya; Suzuki, Keisuke; Nakajima, M.

doi:10.1007/978-3-319-93885-1_55

Cited by 3 publications

(3 citation statements)

References 10 publications

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“…However, in order to apply this algorithm to the safety systems, problems such as the selection of an index suitable for the characteristics of the two-wheeled vehicles, real-time measurement, and low measurement cost are still remaining. Furthermore, it was confirmed that driving behavior was changed depending on the riding style when riders using ARAS (7) . These differences are larger than four-wheeled vehicle drivers.…”

Section: Introductionmentioning

confidence: 79%

Prediction of Collision Avoidance Ability of Two-wheeled Vehicle Riders Using Driving Behaviors and Emotional States

Lee

Kishino

Suzuki

2021

IJAE

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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 R 2 = 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 (R 2 = 0.685, R 2 adj = 0.655).

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Section: Introductionmentioning

confidence: 79%

Prediction of Collision Avoidance Ability of Two-wheeled Vehicle Riders Using Driving Behaviors and Emotional States

Lee

Kishino

Suzuki

2021

IJAE

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“…We think that this is because the trigger threshold is calculated without considering/assuming the driving performance of each driver. In our previous study (Lee, J. et. al., 2018), we simulated and quantified riding behaviour when using and not using ADAS for two-wheeled vehicles.…”

Section: Introduction 11 Motivation For the Studymentioning

confidence: 92%

Riding performance quantification method for motorcycles in terms of collision probability using the logit model

Lee

Kishino

Suzuki

2020

Mechanical Engineering Journal

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In recent years, many Advanced Driver Assistance Systems (ADAS) have been proposed and introduced under the development of sensing technology and the issue of driving safety. But many kinds of ADASs have a specific threshold to control the alarm or some support. This is decided based on the experimental or mathematical calculations in terms of the optimization of the human-machine interface of each system. But almost all of the systems (especially warning systems) have just a single threshold value to issue the warning, and the driving performance of drivers fluctuating in real time is not considered. In this study, we proposed a quantification method of riding performance and performed the logistic regression analysis for the collision prediction model based on riding performance to optimize the warning threshold of ADAS. For this study, 64 test subjects (Mean age = 22.14, S.D. = 3.71) participated in the experiments using simulator. Experiments were conducted for three risk events (left-angle collision when a rider was driving on priority road or driving on non-priority road, and right turning collision) and dummy events with the same road environment without risky situations. We proposed a quantification method of riding performance through the total sum of a product of the generalized value of riding behaviours. We also proposed the logit model, which can be constructed in terms of the collision probabilities and riding performance, which is quantified using our proposed method. In the logit model, collision occurrence was used as the dependent variable and riding performance was used as the independent variable for logistic regression analysis to clarify the condition where the probability of collision increases. Finally, we proposed a concept of the setting method of threshold value for the warning timing of ADAS according to the rider's performance level based on collision probabilities during each riding performance.

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“…Therefore, we are focusing on an evaluation method for upcoming ARAS for two-wheeled vehicles. This study focuses on the collision warning system of an ARAS for twowheeled vehicles, and evaluates the accident-reduction effect through combing RS experiments [12,13], computer simulation and cost-benefit analysis. The simulation was performed to quantify the accident-reduction effect using a model that can evaluate comprehensive safety as a human-machine system.…”

Section: Introductionmentioning

confidence: 99%

Evaluation Method of ARAS Combining Simulator Experiment and Computer Simulation in Terms of Cost-Benefit Analysis

et al. 2020

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In this study, we propose an evaluation method for an Advanced Rider Assistance System (ARAS) for two-wheeled vehicles, combining riding simulator experiments and computer simulations in terms of cost-benefit analysis. This evaluation method focuses on the collision warning system at intersections using an ARAS for two-wheeled vehicles. The study was carried out experiments with 30 test subjects who have two-wheeled vehicle licenses and are not novice riders. To quantify the accidentreduction effect, a Monte-Carlo simulation based on a time-series reliability model was used. Based on the collision probability results derived from the Monte-Carlo Simulation, the overall error probability as a human-machine system was calculated based on an integrated error model. In addition, cost-benefit analysis was conducted to quantify the social benefits and costs of introducing the ARAS to the market. As a result, we confirmed that the system can be beneficial after 4 years when introduced into the market. Keywords Cost-benefit analysis. Evaluation method. Advanced rider assistance system (ARAS). Human-machine Interface (HMI). Two-wheeled vehicles. Riding simulator. Time-series reliability model. Integrated error model

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Human-Machine Interface System for Motorcyclists: Quantification of Total Reliability as Human-Machine System

Cited by 3 publications

References 10 publications

Prediction of Collision Avoidance Ability of Two-wheeled Vehicle Riders Using Driving Behaviors and Emotional States

Prediction of Collision Avoidance Ability of Two-wheeled Vehicle Riders Using Driving Behaviors and Emotional States

Riding performance quantification method for motorcycles in terms of collision probability using the logit model

Evaluation Method of ARAS Combining Simulator Experiment and Computer Simulation in Terms of Cost-Benefit Analysis

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