Kun Gao – författare

This book gathers selected papers presented at the KES International Symposium on Smart Transportation Systems (KES STS 2025). Modern transportation systems have undergone a rapid transformation in recent years, producing a range of technological innovations such as connected vehicles, self-driving cars, electric vehicles, Hyperloop, and even flying cars, and with them, fundamental changes in transport systems around the world. The book discusses current challenges, innovations, and breakthroughs in smart transportation systems, as well as transport infrastructure modeling, safety analysis, freeway operations, intersection analysis, and other related cutting-edge topics.

This book gathers selected papers presented at the KES International Symposium on Smart Transportation Systems (KES STS 2025). Modern transportation systems have undergone a rapid transformation in recent years, producing a range of technological innovations such as connected vehicles, self-driving cars, electric vehicles, Hyperloop, and even flying cars, and with them, fundamental changes in transport systems around the world. The book discusses current challenges, innovations, and breakthroughs in smart transportation systems, as well as transport infrastructure modeling, safety analysis, freeway operations, intersection analysis, and other related cutting-edge topics.

Automated driving requires robust and reliable perception systems, but rare and dangerous scenarios are often missing from real-world data. Kun Gao proposes an approach to scene data augmentation that combines real and virtual data to improve the performance of perception systems in complex environments. The goal is to reduce the limitations caused by insufficient training data for AI models. The method first analyzes important risk factors that influence perception performance. A scene data augmentation framework is then developed, integrating the realism of real data with the flexibility of virtual data. Using computer graphics and reinforcement learning, the approach generates a large number of challenging scenes and efficiently explores high-risk parameter combinations. The experimental results show that the proposed method improves robustness in rare and hazardous situations and increases the performance of AI-based object detection. The study also demonstrates that combining real and virtual data helps reduce the domain gap between them.

Automated driving requires robust and reliable perception systems, but rare and dangerous scenarios are often missing from real-world data. Kun Gao proposes an approach to scene data augmentation that combines real and virtual data to improve the performance of perception systems in complex environments. The goal is to reduce the limitations caused by insufficient training data for AI models. The method first analyzes important risk factors that influence perception performance. A scene data augmentation framework is then developed, integrating the realism of real data with the flexibility of virtual data. Using computer graphics and reinforcement learning, the approach generates a large number of challenging scenes and efficiently explores high-risk parameter combinations. The experimental results show that the proposed method improves robustness in rare and hazardous situations and increases the performance of AI-based object detection. The study also demonstrates that combining real and virtual data helps reduce the domain gap between them.

This book is based on multidisciplinary research focusing on low-carbon healthy city planning, policy and assessment. This includes city-development strategy, energy, environment, healthy, land-use, transportation, infrastructure, information and other related subjects.This book begins with the current status and problems of low-carbon healthy city development in China. It then introduces the global experience of different regions and different policy trends, focusing on individual cases. Finally, the book opens a discussion of Chinese low-carbon healthy city development from planning and design, infrastructure and technology assessment-system perspectives.It presents a case study including the theory and methodology to support the unit city theory for low-carbon healthy cities. The book lists the ranking of China’s 269 high-level cities, with economic, environmental, resource, construction, transportation and health indexes as an assessment for creating a low-carbon healthy future.The book provides readers with a comprehensive overview of building low-carbon healthy cities in China.

This book is based on multidisciplinary research focusing on low-carbon healthy city planning, policy and assessment. This includes city-development strategy, energy, environment, healthy, land-use, transportation, infrastructure, information and other related subjects.This book begins with the current status and problems of low-carbon healthy city development in China. It then introduces the global experience of different regions and different policy trends, focusing on individual cases. Finally, the book opens a discussion of Chinese low-carbon healthy city development from planning and design, infrastructure and technology assessment-system perspectives.It presents a case study including the theory and methodology to support the unit city theory for low-carbon healthy cities. The book lists the ranking of China’s 269 high-level cities, with economic, environmental, resource, construction, transportation and health indexes as an assessment for creating a low-carbon healthy future.The book provides readers with a comprehensive overview of building low-carbon healthy cities in China.  

Finally, the book opens a discussion of Chinese low-carbon healthy city development from planning and design, infrastructure and technology assessment-system perspectives. It presents a case study including the theory and methodology to support the unit city theory for low-carbon healthy cities.

This book gathers selected papers presented at the KES International Symposium on Smart Transportation Systems (KES STS 2024).

This book gathers selected papers presented at the KES International Symposium on Smart Transportation Systems (KES STS 2024). Modern transportation systems have undergone a rapid transformation in recent years, producing a range of technological innovations such as connected vehicles, self-driving cars, electric vehicles, Hyperloop, and even flying cars, and with them, fundamental changes in transport systems around the world. The book discusses current challenges, innovations, and breakthroughs in smart transportation systems, as well as transport infrastructure modeling, safety analysis, freeway operations, intersection analysis, and other related cutting-edge topics.

This book gathers selected papers presented at the KES International Symposium on Smart Transportation Systems (KES STS 2023). Modern transportation systems have undergone a rapid transformation in recent years, producing a range of technological innovations such as connected vehicles, self-driving cars, electric vehicles, Hyperloop, and even flying cars, and with them, fundamental changes in transport systems around the world. The book discusses current challenges, innovations, and breakthroughs in smart transportation systems, as well as transport infrastructure modeling, safety analysis, freeway operations, intersection analysis, and other related cutting-edge topics.

This book gathers selected papers presented at the KES International Symposium on Smart Transportation Systems (KES STS 2023). Modern transportation systems have undergone a rapid transformation in recent years, producing a range of technological innovations such as connected vehicles, self-driving cars, electric vehicles, Hyperloop, and even flying cars, and with them, fundamental changes in transport systems around the world. The book discusses current challenges, innovations, and breakthroughs in smart transportation systems, as well as transport infrastructure modeling, safety analysis, freeway operations, intersection analysis, and other related cutting-edge topics.