For Preventative Automated Driving System (PADS): Traffic Accident Context Analysis Based on Deep Neural Networks

Kang, Minhee; Jun-mei, Song; Hwang, Keeyeon

doi:10.3390/electronics9111829

Cited by 9 publications

(6 citation statements)

References 38 publications

(39 reference statements)

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“…It is evident that with the rapid development of autonomous driving technology and the complex and variable nature of the traffic environment, the number of safety incidents and vehicle recalls for AVs continues to climb. This directly affects users' trust in the safety of self-driving technology and weakens people's willingness to purchase and use self-driving cars (Kang et al 2020).…”

Section: Introductionmentioning

confidence: 99%

The Role of Technical Safety Riskiness and Behavioral Interventions in the Public Acceptance of Autonomous Vehicles in China

Deng

Guo

Yu-xi

et al. 2023

J. Transp. Eng., Part A: Systems

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After reports of self-driving car accidents, public acceptance of autonomous vehicles (AVs) has shown a downward trend. To explore factors influencing this decline of acceptance and to bring acceptance back to the normal range, this study proposed a new factor, human-manipulated risk perception (HMRP), in the context of human-machine collaborative driving. On this basis, we explored the relationship between HMRP and acceptance of AVs and the mechanism of influence. Furthermore, we proposed a behavioral intervention approach to mediate and moderate the relationship between the two. First, an improved acceptance model was developed based on HMRP. Subsequently, a survey was executed, and 300 valid questionnaires were returned. Test results for the improved model showed that HMRP had a significant negative effect on acceptance of AVs. A hybrid model of mediation and moderation was designed, and user experience was introduced as a moderating variable. The results showed that the proposed behavioral intervention strategy was effective in attenuating the negative effect of HMRP on acceptance of AVs. This study provides theoretical support and methodological guidance for developing behavioral interventions to improve user acceptance.

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Section: Introductionmentioning

confidence: 99%

The Role of Technical Safety Riskiness and Behavioral Interventions in the Public Acceptance of Autonomous Vehicles in China

Deng

Guo

Yu-xi

et al. 2023

J. Transp. Eng., Part A: Systems

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“…In response, scholars have proposed hypotheses to verify the intrinsic link between traffic accidents and declining acceptance 3 . In recent studies, it has been found that more and more users are concerned about the safety and stability of AVs 4 , 5 .…”

Section: Introductionmentioning

confidence: 99%

A study of safety acceptance and behavioral interventions for autonomous driving technologies

Deng

Guo

2022

Sci Rep

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Explaining the phenomenon of declining acceptance of automated driving technology (ADT) and predicting trends in acceptance has become an important area of research. To explore the reasons for the decline in acceptance of automated vehicles and how to improve user acceptance, we studied mechanisms of the influence process from the relationship between safety riskiness of ADT and user acceptance, and examined the mediating and moderating effects of the proposed intervention behaviors on the influence relationship between these two. First, an improved acceptance model incorporating safety risk factors was developed. Subsequently, the psychological change process of user acceptance was analyzed based on people’s response to accident information. Ultimately, the results show that safety cognition risk regarding ADT has a significant negative impact on user acceptance. Next, the mediating model where user experience was introduced as a moderating variable was designed. From the test results of this model, it is found that the proposed behavioral intervention strategy is effective in attenuating the degree of impact of the safety riskiness of ADT on acceptance. The risk-based acceptance explanation model and intervention method designed in this study provide a scientific basis and practical approach to develop the market for automated vehicles.

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“…Therefore, tests must be performed to optimize the number of vectors in the hidden layer. In this paper, hidden vector sizes of 32, 64, 128 and 256 are studied (Minhee et al, 2020).…”

Section: Methods For Predicting the Acbcmentioning

confidence: 99%

An advanced method for predicting the axial compressive bearing capacity of fiber-reinforced polymer-strengthened hollow section metal columns

Cao

Zhang

et al. 2023

Advances in Structural Engineering

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Successful experimental and theoretical studies have promoted fiber-reinforced polymer (FRP) as an innovative strengthening material for metal columns in structural engineering, highlighting the necessity for an effective and reliable calculation method for such strengthened members. Here, based on existing experimental data, an advanced method using an artificial neural network (ANN) is developed to predict the axial compression bearing capacity (ACBC) of FRP-strengthened hollow section metal (FSHM) columns, where FRP strengthening includes wrapping/bonding FRP sheets/laminates; this method considers realistic conditions, incorporates a variety of parameters, and produces accurate and fast predictions. First, a multilayer perceptron (MLP) neural network was applied. By studying the optimization algorithm, learning rate, hidden vector and activation function, an optimal ANN model was established, which yielded better predictive accuracies and safety than existing calculation methods. Using the validated model, an prediction program was developed that promptly generated predictions of ACBC with a high reliability. Finally, a sensitivity analysis was performed to further assess the effects of key parameters on the behavior of FSHM columns to provide practical recommendations for their design.

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For Preventative Automated Driving System (PADS): Traffic Accident Context Analysis Based on Deep Neural Networks

Cited by 9 publications

References 38 publications

The Role of Technical Safety Riskiness and Behavioral Interventions in the Public Acceptance of Autonomous Vehicles in China

The Role of Technical Safety Riskiness and Behavioral Interventions in the Public Acceptance of Autonomous Vehicles in China

A study of safety acceptance and behavioral interventions for autonomous driving technologies

An advanced method for predicting the axial compressive bearing capacity of fiber-reinforced polymer-strengthened hollow section metal columns

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