Analysis of the Perception of Autonomous Vehicles Using Text Mining Technique

Automated Vehicles (AVs) are under development to reduce traffic accidents to a great extent. Therefore, safety will play a pivotal role to determine their social acceptability. Despite the fast development of AVs technologies, related accidents can occur even in an ideal environment. Therefore, measures to prevent traffic accidents in advance are essential. This study implemented a traffic accident context analysis based on the Deep Neural Network (DNNs) technique to design a Preventive Automated Driving System (PADS). The DNN-based analysis reveals that when a traffic accident occurs, the offender’s injury can be predicted with 85% accuracy and the victim’s case with 67%. In addition, to find out factors that decide the degree of injury to the offender and victim, a random forest analysis was implemented. The vehicle type and speed were identified as the most important factors to decide the degree of injury of the offender, while the importance for the victim is ordered by speed, time of day, vehicle type, and day of the week. The PADS proposed in this study is expected not only to contribute to improve the safety of AVs, but to prevent accidents in advance.

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Defining Smart Mobility Service Levels via Text Mining

So¹,

Lee

2020

Sustainability

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The concept of smart mobility depends on a country’s or city’s visions and surroundings, such as traffic issues and available transportation modes. This study, therefore, proposes a clear and consistent set of definitions for smart mobility, in the context of past, present, and future, based on investigations of smart mobility practices in South Korea and overseas. In addition, smart mobility definitions are collected from various written sources and analyzed via text mining to define levels of smart mobility beyond the present service level. This study therefore defines smart mobility in six stages: level 0, base infrastructure; level 1, individual digitization; level 2, partial integration; level 3, full integration; level 4, personalized integration; and level 5, mobility transformation. The definition of each stage includes the scope of transportation modes to be integrated, required technology level, mobility operations, and user convenience. This definition of smart mobility by stage will be beneficial for setting the targeted levels of smart mobility services in projects and for setting goals not only in the present context but also for the future of smart mobility, which will be utilized as a roadmap for the implementation of smart mobility in many countries and cities.

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Analysis of the Perception of Autonomous Vehicles Using Text Mining Technique

Cited by 3 publications

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Generating Traffic Safety Test Scenarios for Automated Vehicles using a Big Data Technique

Generating Traffic Safety Test Scenarios for Automated Vehicles using a Big Data Technique

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

Defining Smart Mobility Service Levels via Text Mining

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