Transportation
This article presents the details of the Cardinal RF (CardRF) dataset. CardRF is acquired to foster research in RF- based UAV detection and identification or RF fingerprinting. RF signals were collected from UAV controllers, UAV, Bluetooth, and Wi-Fi devices. Signals are collected at both visual line-of-sight and beyond-line-of-sight. The assumptions and procedure for the data acquisition are presented. A detailed explanation of how the data can be utilized is discussed. CardRF is over 65 GB in storage memory.
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The experiment of fixing the velocity of both sprockets of UTV proves that the tracked vehicle slips considerably when steering on a slope, making it impossible for the vehicle to follow the desired trajectory precisely, and it is necessary to propose an effective and feasible solution specifically for the motion planning of UTV slope-steering.
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Results for CTPS 2 are listed in CTPS2.docx.
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Experiments about satisfaction from ridesharing, from mturk
The first two experiemtns asked which explanations are likely to increase user satisfaction
The third experiment ask for satisfaction (1-7) given a scenario and some explanations. It's divided to three:
- pbe: explanations are all known info
- random: explanatiitons are random subset of knwon info
- axis: smart choosing of subset of the known info
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This dataset provides a dataset of high resolution image-grade LiDAR SLAM in .bag format.
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This repository shares some simultaneous localization and mapping (SLAM) datasets and detection datasets for railroad application. In the first stage, we decide to realease some LiDAR, visual, IMU sequence for freight-traffic railways. The ground truth cannot be provided due to safety regulations. Please infer https://github.com/YushengWHU/Railroad-dataset for detailed information.
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The number of private vehicles is still increasing from year to year. In order to limit environmental damage, a proper way of dealing with this trend is the introduction of intelligent automotive infrastructure. Besides traffic management solutions, smart parking guidance systems are important for reducing unnecessary traffic. For this, a key prerequisite are sensor networks that provide information about the occupancy state of every single parking spot in the parking infrastructure of high traffic targets e.g. nearby an airport or shopping mall.
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