The dataset includes three files collected from the the CAN bus of a moving vehicle. They are:
1- CAN log with no injection of fabricated messages
2- CAN log with injection of fabricated RPM reading messages
3- CAN log with injection of fabricated speed reading messages
The description of the data collection process is available in this paper:
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The dataset includes three files collected from the the CAN bus of a moving vehicle. They are:
1- CAN log with no injection of fabricated messages
2- CAN log with injection of fabricated RPM reading messages
3- CAN log with injection of fabricated speed reading messages
The description of the data collection process is available in this paper:
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This dataset contains RF signals from drone remote controllers (RCs) of different makes and models. The RF signals transmitted by the drone RCs to communicate with the drones are intercepted and recorded by a passive RF surveillance system, which consists of a high-frequency oscilloscope, directional grid antenna, and low-noise power amplifier. The drones were idle during the data capture process. All the drone RCs transmit signals in the 2.4 GHz band. There are 17 drone RCs from eight different manufacturers and ~1000 RF signals per drone RC, each spanning a duration of 0.25 ms.
The dataset contains ~1000 RF signals in .mat format from the remote controllers (RCs) of the following drones:
- DJI (5): Inspire 1 Pro, Matrice 100, Matrice 600*, Phantom 4 Pro*, Phantom 3
- Spektrum (4): DX5e, DX6e, DX6i, JR X9303
- Futaba (1): T8FG
- Graupner (1): MC32
- HobbyKing (1): HK-T6A
- FlySky (1): FS-T6
- Turnigy (1): 9X
- Jeti Duplex (1): DC-16.
In the dataset, there are two pairs of RCs for the drones indicated by an asterisk above, making a total of 17 drone RCs. Each RF signal contains 5 million samples and spans a time period of 0.25 ms.
The scripts provided with the dataset defines a class to create drone RC objects and creates a database of objects as well as a database in table format with all the available information, such as make, model, raw RF signal, sampling frequency, etc. The scripts also include functions to visualize data and extract a few example features from the raw RF signal (e.g., transient signal start point). Instructions for using the scripts are included at the top of each script and can also be viewed by typing help scriptName in MATLAB command window.
The drone RC RF dataset was used in the following papers:
- M. Ezuma, F. Erden, C. Kumar, O. Ozdemir, and I. Guvenc, "Micro-UAV detection and classification from RF fingerprints using machine learning techniques," in Proc. IEEE Aerosp. Conf., Big Sky, MT, Mar. 2019, pp. 1-13.
- M. Ezuma, F. Erden, C. K. Anjinappa, O. Ozdemir, and I. Guvenc, "Detection and classification of UAVs using RF fingerprints in the presence of Wi-Fi and Bluetooth interference," IEEE Open J. Commun. Soc., vol. 1, no. 1, pp. 60-79, Nov. 2019.
- E. Ozturk, F. Erden, and I. Guvenc, "RF-based low-SNR classification of UAVs using convolutional neural networks." arXiv preprint arXiv:2009.05519, Sept. 2020.
Other details regarding the dataset and data collection and processing can be found in the above papers and attached documentation.
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Author Contributions:
- Experiment design: O. Ozdemir and M. Ezuma
- Data collection: M. Ezuma
- Scripts: F. Erden and C. K. Anjinappa
- Documentation: F. Erden
- Supervision, revision, and funding: I. Guvenc
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Acknowledgment
This work was supported in part by NASA through the Federal Award under Grant NNX17AJ94A, and in part by NSF under CNS-1939334 (AERPAW, one of NSF's Platforms for Advanced Wireless Research (PAWR) projects).
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2265 Views
While the number of highway tunnels is increasing, the current Chinese criteria and Uniform Traffic Control Equipment Manual (MUTCD) 2009 guidelines provide no clear method for the setting of exit advance guide signs in highway tunnels. To solve this problem, this paper proposes separate guide signs, consisting of location and distance signs. Location signs on tops of tunnels use Chinese characters with a height of 40 cm and 20 cm.
While the number of highway tunnels is increasing, the current Chinese criteria and Uniform Traffic Control Equipment Manual (MUTCD) 2009 guidelines provide no clear method for the setting of exit advance guide signs in highway tunnels. To solve this problem, this paper proposes separate guide signs, consisting of location and distance signs. Location signs on tops of tunnels use Chinese characters with a height of 40 cm and 20 cm. Distance signs are 62.6 m from location signs, with a character height and width of 1.5 m, and a total sign length of 22.5 m. Four schemes were tested, in which the distance sign was on the left or right wall of the tunnel, and the vehicle was in the left or right lane. A Markov chain was used to analyze the driver’s eye movement characteristics to evaluate the effect of separate guide signs. The results are as follows. In the left lane, when a distance sign on the left wall of the tunnel appeared, the driver’s fixation points were relatively balanced except in the left area. After the sign disappeared, the driver’s fixation points were no different than before, except in the front area. However, when a distance sign appeared on the right wall, 77.84% of the driver’s fixation points were concentrated in the front and right areas, which decreased driving safety. Similar results were obtained in the right lane. Therefore, distance signs should be placed on the left wall of the highway tunnel. This study can forewarn a driver in a tunnel of the exit location and distance, and facilitate leaving the highway safely.
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26 Views
Route planning also known as pathfinding is one of the key elements in logistics, mobile robotics and other applications, where engineers face many conflicting objectives. However, most of the current route planning algorithms consider only up to three objectives. In this paper, we propose a scalable many-objective benchmark problem covering most of the important features for routing applications based on real-world data. We define five objective functions representing distance, traveling time, delays caused by accidents, and two route specific features such as curvature and elevation.
In folder 'referenceFronts', you can find the corresponding Pareto-Fronts (.pf) (comma seperated values) and -Sets (.ps)Deserilisation of the sets is possible through Gson/JSON. Each line contains all nodes of Path, delimited by '||'
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49 Views
High-voltage batteries in battery electric vehicles face significant load fluctuations due to driving behavior. This dynamic performance of the powertrain is contrasted by the almost constant load of the auxiliary consumers. The highest auxiliary consumption is generated by the heating and air conditioning system, which decreases the vehicles range significantly. 72 real driving trips with a BMW i3 (60 Ah) were recorded, serving for model validation of a full vehicle model consisting of the powertrain and the heating circuit.
Plase see the attached readme.txt.
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756 Views
This provides the code and data used in the paper "Optimal EV Scheduling in Residential Distribution Networks Considering Customer Charging Preferences" by Mailys Le Cam and Barry Hayes.
Some material has been adapated from the OpenDSS help files: http://smartgrid.epri.com/SimulationTool.aspx Some data has been taken from the IEEE test feeders archive: http://sites.ieee.org/pes-testfeeders/
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173 Views
We propose a driver pattern dataset consists of 51 features extracted from CAN (Controller Area Network) of Hyundai YF Sonata while four drivers drove city roads of Seoul, Republic of Korea. Under the belief that different driving patterns implicitly exist at CAN data, we collected CAN diagnosis data from four drivers in pursuit of research on driver identification, driver profiling, and abnormal driving behavior detection. Four drivers are named A, B, C, and D.
Description
The dataset contains 51 features extracted from CAN along with numerous trips performed by four drivers. The four drivers drove along city roads of Seoul, the Republic of Korea. The recorded 51 features can be employed for driver identification, driver profiling, abnormal driving pattern identification, and any related tasks. Please check the abstract for a more detailed description.
CSV Files
Directory A, B, C and D contains .csv files of CAN data. Each .csv file represents a trip.
Features
The names of 51 features are described in the features.pkl file. Please check the file for detailed information.
Citations
Park, Kyung Ho, and Huy Kang Kim. "This Car is Mine!: Automobile Theft Countermeasure Leveraging Driver Identification with Generative Adversarial Networks." arXiv preprint arXiv:1911.09870 (2019).
Park, Kyung Ho, and Huy Kang Kim. "This Car is Mine!: Automobile Theft Countermeasure Leveraging Driver Identification with Generative Adversarial Networks.", ESCAR Asia (2019)
Acknowledgement
The study was funded by Institute for Information and communications Technology Promotion (Grant No. 2020-0-00374, Development of Security Primitives for Unmanned Vehicles).
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418 Views
Robotic Vision
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38 Views
An inductive power transfer (IPT) system is envisaged as the best solution to conveniently charge electric vehicles (EVs). While stationary IPT systems are becoming commercialized, significant research is being conducted to address the challenges related to dynamic IPT systems. Dynamic or in-motion IPT systems require a fully electrified roadway with embedded inductive couplers with accompanying circuitry. The large number of electronic components required, however, increases the system complexity, reducing the reliability and economic viability of dynamic IPT systems proposed to-date.
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182 Views