autonomous vehicles
The UQTR dataset consists of 7838 real and synthetic images of the Université du Québec à Trois-Rivières (UQTR) campus road under normal and snow conditions. The image resolution is 1280×720. It includes lane labels in .txt files, where each row stores the set of points of a lane. The points are stored as x1 y1 x2 y2, as in the tutorial by Ruijin Liu, Zejian Yuan, Tie Liu, Zhiliang Xiong: Train and Test Your Custom Data.
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The objective of this study is to conduct a systematic examination of research trends and hotspots in the domain of autonomous vehicles leveraging deep learning, through a bibliometric analysis. By scrutinizing research publications from various countries spanning 2017 to 2023, this paper aims to summarize effective research methodologies and identify potential innovative pathways to foster further advancements in AVs research. A total of 1,239 publications from the core collection of scientific networks were retrieved and utilized to construct a clustering network.
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Recently, self-driving vehicles have been introduced with several automated features including lane-keep assistance, queuing assistance in traffic-jam, parking assistance and crash avoidance. These self-driving vehicles and intelligent visual traffic surveillance systems mainly depend on cameras and sensors fusion systems.
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As one of the research directions at OLIVES Lab @ Georgia Tech, we focus on the robustness of data-driven algorithms under diverse challenging conditions where trained models can possibly be depolyed. To achieve this goal, we introduced a large-sacle (~1.72M frames) traffic sign detection video dataset (CURE-TSD) which is among the most comprehensive datasets with controlled synthetic challenging conditions. The video sequences in the
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As one of the research directions at OLIVES Lab @ Georgia Tech, we focus on the robustness of data-driven algorithms under diverse challenging conditions where trained models can possibly be depolyed.
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4215 Views