Transportation
Safety of the Intended Functionality (SOTIF) addresses sensor performance limitations and deep learning-based object detection insufficiencies to ensure the intended functionality of Automated Driving Systems (ADS). This paper presents a methodology examining the adaptability and performance evaluation of the 3D object detection methods on a LiDAR point cloud dataset generated by simulating a SOTIF-related Use Case.
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We introduce a novel dataset consisting of approximately 5,700 video files, specifically designed to enhance the development of real-time traffic accident detection systems in smart city environments. It encompasses a diverse range of traffic scenarios, captured through Traffic/Surveillance Cameras (Trafficam) and Dash Cameras (Dashcam), along with additional external data sources. The dataset is meticulously organized into three segments: Training, Validation, and Testing, with each segment offering a unique blend of traffic and dashcam footage across different scenarios.
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The dataset contains ttwo columns, the first column represent the 'Time' data , while the second column represents the 'Current' data's. These figures are from a simulation of the 750 VDC traction system. These figures also show the train's high beginning current and used to study the DC traction system.
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Volkswagen Group of America Innovation and Engineering Center California (VW IECC) is a research facility in Belmont, California working on the future of the mobility. In the recent years exciting developments have happened for the autonomous vehicles. In general, lack of data is the main problem to tackle to solve the task of autonomous driving. One of the important tasks in this topic is the overtaking and lane changes, especially in the highway scenarios.
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The IAMCV Dataset was acquired as part of the FWF Austrian Science Fund-funded Interaction of Autonomous and Manually-Controlled Vehicles project. It is primarily centred on inter-vehicle interactions and captures a wide range of road scenes in different locations across Germany, including roundabouts, intersections, and highways. These locations were carefully selected to encompass various traffic scenarios, representative of both urban and rural environments.
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This dataset presents a set of large-scale Dial-a-Ride Problem (DARP) instances. The instances were created as a standardized set of ridesharing DARP problems for the purpose of benchmarking and comparing different solution methods.
The instances are based on real demand and realistic travel time data from 3 different US cities: Chicago, New York City, and Washington, DC. The instances consist of real travel requests from the selected period, positions of vehicles with their capacities, and realistic shortest travel times between all pairs of locations in each city.
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We have selected three intersections within Xi'an, Shaanxi Province, China for data collection. These include the intersection of Taibai South Road and Keji Road, the intersection of Chang'an North Road and Yanta West Road, and the intersection of Yanta North Road and Youyi Road. The UAV for the purpose of video data collection is employed to investigate intersections, with a high-definition video resolution of 4096 * 2160. The spatiotemporal position information of target vehicles was tracked and labeled using Datafromsky software.
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We obtained this dataset as part of a project to generate a realistic speed profile on a trip specified by GPS coordinates. Specifically, we focused on generating the speed profile for a passenger car traveling on an unfamiliar route, i.e., a route the machine-learning model has yet to see.
The dataset contains 5973 rides of five different passenger cars, with a total length of 9049.3 km. The data was collected during 2021 in the Czech Republic and includes municipal and non-municipal trips.
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In this dataset, 4 radio channel measurements at 28 GHz are presented. These have been captured in two different spots of a highway with a fixed Tx and a mobile Rx onboard a car, emulating a V2I link. The Tx was placed both on the roadside (3 measurement runs) and on an overpass (1 measurement). Each run consists of around 4.29 s of continuously captured channel impulse responses (CIRs), with a channel sampling period of 65 μs and a delay resolution of 2 ns.
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