Abstract
Autonomous vehicles require high-level semantic maps, which contain the activities of pedestrians and cars, to ensure safe navigation. High-level semantics can be obtained from mobile probe sensor data. Analyzing pedestrian trajectories obtained from mobile probe data is an effective approach to avoid collisions between autonomous vehicles and pedestrians. Such analyses of pedestrian trajectories can generate new information such as pedestrian behaviors in violation of traffic regulations. However, pedestrian trajectories obtained from mobile probe data significantly sparse and noisy, making it challenging to analyze pedestrian activity. To address this issue, we propose multiple daily data and graph-based approaches to treat sparse and noisy data for estimating the flow of pedestrians based on mobile probe data. To improve the sparseness of the data, multiple daily data are fused. After that, a pedestrian graph is created to enhance the region’s coverage by connecting the sparse data indicating the flow of pedestrians. This proposed approach successfully obtained pedestrian trajectory data from the sparse and noisy data. Moreover, it was possible to identify the potential locations where pedestrians tend to cross the street by analyzing the pedestrian flow. The results indicate that 83% of well-known regions where pedestrians tend to cross the street corresponded with those extracted using the proposed approach. Furthermore, a high-level semantic map of the regions where pedestrians tend to cross the street along a 1-km road is presented. The trajectory information obtained using the proposed approach is expected to be essential for understanding different scenarios of the interactions between individuals and autonomous vehicles.
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All data and materials as well as software application or custom code support this published claims comply with field standards. The data that support the findings of this study are available from the corresponding author, RPBN, upon reasonable request.
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Funding
This work was partially supported by JST CREST Recognition, Summarization, and Retrieval of Large-Scale Multimedia Data Grant Number JP14532298, NEDO’s commissioned research project, Integration at the core of next-generation artificial intelligence and robots / 1. Research and development, Demonstration for social implementation of artificial intelligence technology / Research and development of transportation of soil at local small and medium construction sites using robot technology and artificial intelligence”, and Advanced Intelligence Project (AIP) Challenge Research Funding, Japan.
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All authors contributed to the study conception and design. Material preparation, data collection and analysis were performed by RPBN, KO and TW. The first draft of the manuscript was written by RPBN and all authors commented on previous versions of the manuscript. All authors read and approved the final manuscript.
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This work was partially supported by JST CREST Recognition, Summarization, and Retrieval of Large-Scale Multimedia Data Grant Number JP14532298, NEDO’s commissioned research project, Integration at the core of next-generation artificial intelligence and robots / 1. Research and development, Demonstration for social implementation of artificial intelligence technology / Research and development of transportation of soil at local small and medium construction sites using robot technology and artificial intelligence”, and AIP Challenge Research Funding, Japan.
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Neto, R.P.B., Ohno, K., Westfechtel, T. et al. Knowledge Acquisition from Pedestrian Flow Analysis using Sparse Mobile Probe Data. J Intell Robot Syst 102, 85 (2021). https://doi.org/10.1007/s10846-021-01419-w
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DOI: https://doi.org/10.1007/s10846-021-01419-w