Intelligent Transportation Systems
For optimizing RSU deployment, comprehensive data is essential. Geographical data includes detailed road network topology, traffic flow patterns, and geographic coordinates. Communication range data covers effective ranges of RSUs and connected vehicles, considering signal strength and environmental conditions. Vehicle data involves classifications based on communication capabilities and percentages of connected vehicles. Network performance data includes latency requirements, packet loss rates, and bandwidth needs.
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The JKU-ITS AVDM contains data from 17 participants performing different tasks with various levels of distraction.
The data collection was carried out in accordance with the relevant guidelines and regulations and informed consent was obtained from all participants.
The dataset was collected using the JKU-ITS research vehicle with automated capabilities under different illumination and weather conditions along a secure test route within the
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This dataset is dedicated to the assesment of cooperative localization algorithms using realistic driving patterns from many vehicles moving in CARLA simulator, along with realistic V2V communication and network quality conditions.
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Dataset: IQ samples of LTE, 5G NR, WiFi, ITS-G5, and C-V2X PC5
Thes dataset comprises IQ samples captured from ITSG-5, C-V2X PC5, WiFi, LTE, 5G NR and Noise. Six different dataset bunches are collected at sampling rates of 1, 5, 10, 15 , 20, and 25 Msps. In each dataset cluster, 7500 examples are collected from each considered technology. The dataset size at each considered sampling rate is 7500 X M, where M can be 44, 220, 440, 660, 880, and 1100 for a sampling rate of 1, 5, 10, 15 , 20, and 25 Msps,respectively.
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A high-fidelity CarSim model is used to collect the data for almost 50 maneuvers for two different tractors with different trailer attached to them. For instance, 10 Single Lane Change (SLC) maneuvers are considered in CarSim including 5 tests with E-class SUV and 5 tests with a pick-up truck. Moreover, at each test, the trailer payload and geometry, CG location, and track width, have been changed to collect sufficient data.
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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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Vision and lidar are complementary sensors that are incorporated into many applications of intelligent transportation systems. These sensors have been used to great effect in research related to perception, navigation and deep-learning applications. Despite this success, the validation of algorithm robustness has recently been recognised as a major challenge for the massive deployment of these new technologies. It is well known that algorithms and models trained or tested with a particular dataset tend not to generalise well for other scenarios.
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