Prabu, AvinashRanjan, NityaLi, LingxiTian, RenranChien, StanleyChen, YaobinSherony, Rini2024-01-102024-01-102022-10Prabu, A., Ranjan, N., Li, L., Tian, R., Chien, S., Chen, Y., & Sherony, R. (2022). SceNDD: A Scenario-based Naturalistic Driving Dataset. 2022 IEEE 25th International Conference on Intelligent Transportation Systems (ITSC), 4363–4368. https://doi.org/10.1109/ITSC55140.2022.9921953https://hdl.handle.net/1805/37952In this paper, we propose SceNDD: a scenario-based naturalistic driving dataset that is built upon data collected from an instrumented vehicle in downtown Indianapolis. The data collection was completed in 68 driving sessions with different drivers, where each session lasted about 20–40 minutes. The main goal of creating this dataset is to provide the research community with real driving scenarios that have diverse trajectories and driving behaviors. The dataset contains ego-vehicle's waypoints, velocity, yaw angle, as well as non-ego actor's waypoints, velocity, yaw angle, entry-time, and exit-time. Certain flexibility is provided to users so that actors, sensors, lanes, roads, and obstacles can be added to the existing scenarios. We used a Joint Probabilistic Data Association (JPDA) tracker to detect non-ego vehicles on the road. We present some preliminary results of the proposed dataset and a few applications associated with it. The complete dataset is expected to be released by the end of 2022.en-USAttribution 4.0 InternationalSceNDDdriving scenariosnon-ego vehiclesJoint Probabilistic Data AssociationConference proceedings