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pro vyhledávání: '"Ran, Bin"'
Vehicle trajectory prediction is crucial for advancing autonomous driving and advanced driver assistance systems (ADAS), enhancing road safety and traffic efficiency. While traditional methods have laid foundational work, modern deep learning techniq
Externí odkaz:
http://arxiv.org/abs/2406.11941
As urbanization advances, cities are expanding, leading to a more decentralized urban structure and longer average commuting durations. The construction of an urban expressway system emerges as a critical strategy to tackle this challenge. However, t
Externí odkaz:
http://arxiv.org/abs/2406.08750
Facing the congestion challenges of mixed road networks comprising expressways and arterial road networks, traditional control solutions fall short. To effectively alleviate traffic congestion in mixed road networks, it is crucial to clear the intera
Externí odkaz:
http://arxiv.org/abs/2405.06125
Truck parking on freight corridors faces various challenges, such as insufficient parking spaces and compliance with Hour-of-Service (HOS) regulations. These constraints often result in unauthorized parking practices, causing safety concerns. To enha
Externí odkaz:
http://arxiv.org/abs/2401.12920
Autor:
Liu, Hongjie, Shi, Haotian, Fu, Sicheng, Yuan, Tengfei, Zhang, Xinhuan, Xu, Hongzhe, Ran, Bin
Customizing services for bus travel can bolster its attractiveness, optimize usage, alleviate traffic congestion, and diminish carbon emissions. This potential is realized by harnessing recent advancements in positioning communication facilities, the
Externí odkaz:
http://arxiv.org/abs/2312.01687
Exploring Driving Behavior for Autonomous Vehicles Based on Gramian Angular Field Vision Transformer
Effective classification of autonomous vehicle (AV) driving behavior emerges as a critical area for diagnosing AV operation faults, enhancing autonomous driving algorithms, and reducing accident rates. This paper presents the Gramian Angular Field Vi
Externí odkaz:
http://arxiv.org/abs/2310.13906
Predicting vehicle trajectories is crucial for ensuring automated vehicle operation efficiency and safety, particularly on congested multi-lane highways. In such dynamic environments, a vehicle's motion is determined by its historical behaviors as we
Externí odkaz:
http://arxiv.org/abs/2309.01981
Tribal lands in the United States have consistently exhibited higher crash rates and injury severities compared to other regions. To address this issue, effective data-driven safety analysis methods are essential for resource allocation and tribal sa
Externí odkaz:
http://arxiv.org/abs/2308.08177
Traffic safety is important in reducing death and building a harmonious society. In addition to studies of accident incidences, the perception of driving risk is significant in guiding the implementation of appropriate driving countermeasures. Risk a
Externí odkaz:
http://arxiv.org/abs/2211.03304
Publikováno v:
中国工程科学, Vol 26, Iss 1, Pp 178-189 (2024)
Recently, the autonomous driving industry in China has been gradually shifting its focus from individual-vehicle intelligence to vehicle‒infrastructure cooperation. This shift has brought significant opportunities for the intelligent transportation
Externí odkaz:
https://doaj.org/article/1191cf68eb794b4486e846467a2c6de8