Transportation
The proposed dataset, termed PC-Urban (Urban Point Cloud), is captured with an Ouster LiDAR sensor with 64 channels. The sensor is installed on an SUV that drives through the downtown of Perth, Western Australia (WA), Australia. The dataset comprises over 4.3 billion points captured for 66K sensor frames. The labelled data is organized as registered and raw point cloud frames, where the former has a different number of registered consecutive frames. We provide 25 class labels in the dataset covering 23 million points and 5K instances.
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This dataset contains road networks used in experiments for DRL-Router, including Sioux Falls, Anaheim, Winnipeg and Barcelona.
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The dataset collects the results of a survey of 325 respondents. Each respondent is asked to design a route from an origin to a destination taking into account the following considerations:
- The route should avoid crowds to avoid getting COVID-19.
- They should take into account the context provided: day, time, month, holiday period.
A total of 10 scenarios located in the city of Ciudad Real were designed.
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Both passenger demand and service supply are among the most important factors that determine the performance of urban rail transit system. It is not easy to find out optimal solution for the match between the passenger demand and service supply with traditional methods, due to the complexity of the combinatorial intelligent supply — demand matching problem. In order to get the comprehensively optimal matching degree, this paper transforms the multi-criteria problem into the distributed artificial intelligence optimization by using multi-agent dynamic interaction technique.
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This dataset is released with our research paper titled “Scene-graph Augmented Data-driven Risk Assessment of Autonomous Vehicle Decisions” (https://arxiv.org/abs/2009.06435). In this paper, we propose a novel data-driven approach that uses scene-graphs as intermediate representations for modeling the subjective risk of driving maneuvers. Our approach includes a Multi-Relation Graph Convolution Network, a Long-Short Term Memory Network, and attention layers.
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