"Womp Womp! Your browser does not support canvas :'("

Multi-Sensor Object Detection Data from Infrastructure Sensors Deployed at Traffic Intersections in the City of Colorado Springs, Colorado, USA

Publicly accessible License 

The dataset provided here was collected as a part of the US Department of Transportation (USDOT) Strengthening Mobility and Revolutionizing Transportation (SMART) project, where the City of Colorado Springs (Colorado, USA) and National Renewable Energy Laboratory (NREL) collaborated to collect object-level trajectory data from road users using multiple types of infrastructure sensors deployed at different traffic intersections. The data was collected in 2024 across multiple days at various intersections in and around the City of Colorado Springs. The goal of the data collection exercises was to learn various attributes about infrastructure sensors and to build a repository of high resolution object-level data that can be used for research and development (such as for developing multi-sensor data fusion algorithms).Data presented here was collected from sensors either installed either on the traffic poles or hoisted on top of NREL?s Infrastructure Perception and Control (IPC) mobile trailer. The state-of-the-art IPC trailer can deploy the latest generation of perception sensors at traffic intersections and capture real-time road user data. Sensors used for data collection include Econolite?s EVO RADAR units, Ouster?s OS1 LIDAR units and Axis Camera units. The raw data received from individual sensors is processed at the edge compute device located inside the IPC mobile Lab, and the resulting object-level data is then stored and processed offline. Each data folder contains all the data collected on the day. We have transformed (rotation then translation) the raw detections to ensure the data from all sensors is represented in the same cartesian coordinate system. The object list attributes impacted from the transformation are PositionX, PositionY, SpeedX, SpeedY and HeadingDeg. The rest of the data attribute remains untouched. Users should note that we do not claim that this transformation is perfect and there may be some misalignment among the different sensors.

Citation Formats

TY - DATA AB - The dataset provided here was collected as a part of the US Department of Transportation (USDOT) Strengthening Mobility and Revolutionizing Transportation (SMART) project, where the City of Colorado Springs (Colorado, USA) and National Renewable Energy Laboratory (NREL) collaborated to collect object-level trajectory data from road users using multiple types of infrastructure sensors deployed at different traffic intersections. The data was collected in 2024 across multiple days at various intersections in and around the City of Colorado Springs. The goal of the data collection exercises was to learn various attributes about infrastructure sensors and to build a repository of high resolution object-level data that can be used for research and development (such as for developing multi-sensor data fusion algorithms).Data presented here was collected from sensors either installed either on the traffic poles or hoisted on top of NREL’s Infrastructure Perception and Control (IPC) mobile trailer. The state-of-the-art IPC trailer can deploy the latest generation of perception sensors at traffic intersections and capture real-time road user data. Sensors used for data collection include Econolite’s EVO RADAR units, Ouster’s OS1 LIDAR units and Axis Camera units. The raw data received from individual sensors is processed at the edge compute device located inside the IPC mobile Lab, and the resulting object-level data is then stored and processed offline. Each data folder contains all the data collected on the day. We have transformed (rotation then translation) the raw detections to ensure the data from all sensors is represented in the same cartesian coordinate system. The object list attributes impacted from the transformation are PositionX, PositionY, SpeedX, SpeedY and HeadingDeg. The rest of the data attribute remains untouched. Users should note that we do not claim that this transformation is perfect and there may be some misalignment among the different sensors. AU - Sandhu A2 - Young A3 - Wang A4 - Mir A5 - Calles-Rios Sosa A6 - Sines DB - Open Energy Data Initiative (OEDI) DP - Open EI | National Renewable Energy Laboratory DO - KW - Infrastructure Sensors KW - Object Detection KW - Perception Agents KW - Telemetry KW - Traffic Intersections LA - English DA - 2025/03/11 PY - 2025 PB - National Renewable Energy Laboratory T1 - Multi-Sensor Object Detection Data from Infrastructure Sensors Deployed at Traffic Intersections in the City of Colorado Springs, Colorado, USA UR - https://data.openei.org/submissions/8372 ER -
Export Citation to RIS
Sandhu, et al. Multi-Sensor Object Detection Data from Infrastructure Sensors Deployed at Traffic Intersections in the City of Colorado Springs, Colorado, USA. National Renewable Energy Laboratory, 11 March, 2025, NREL. https://data.nrel.gov/submissions/287.
Sandhu, Young, Wang, Mir, Calles-Rios Sosa, & Sines. (2025). Multi-Sensor Object Detection Data from Infrastructure Sensors Deployed at Traffic Intersections in the City of Colorado Springs, Colorado, USA. [Data set]. NREL. National Renewable Energy Laboratory. https://data.nrel.gov/submissions/287
Sandhu, Young, Wang, Mir, Calles-Rios Sosa, and Sines. Multi-Sensor Object Detection Data from Infrastructure Sensors Deployed at Traffic Intersections in the City of Colorado Springs, Colorado, USA. National Renewable Energy Laboratory, March, 11, 2025. Distributed by NREL. https://data.nrel.gov/submissions/287
@misc{OEDI_Dataset_8372, title = {Multi-Sensor Object Detection Data from Infrastructure Sensors Deployed at Traffic Intersections in the City of Colorado Springs, Colorado, USA}, author = {Sandhu and Young and Wang and Mir and Calles-Rios Sosa and Sines}, abstractNote = {The dataset provided here was collected as a part of the US Department of Transportation (USDOT) Strengthening Mobility and Revolutionizing Transportation (SMART) project, where the City of Colorado Springs (Colorado, USA) and National Renewable Energy Laboratory (NREL) collaborated to collect object-level trajectory data from road users using multiple types of infrastructure sensors deployed at different traffic intersections. The data was collected in 2024 across multiple days at various intersections in and around the City of Colorado Springs. The goal of the data collection exercises was to learn various attributes about infrastructure sensors and to build a repository of high resolution object-level data that can be used for research and development (such as for developing multi-sensor data fusion algorithms).Data presented here was collected from sensors either installed either on the traffic poles or hoisted on top of NREL?s Infrastructure Perception and Control (IPC) mobile trailer. The state-of-the-art IPC trailer can deploy the latest generation of perception sensors at traffic intersections and capture real-time road user data. Sensors used for data collection include Econolite?s EVO RADAR units, Ouster?s OS1 LIDAR units and Axis Camera units. The raw data received from individual sensors is processed at the edge compute device located inside the IPC mobile Lab, and the resulting object-level data is then stored and processed offline. Each data folder contains all the data collected on the day. We have transformed (rotation then translation) the raw detections to ensure the data from all sensors is represented in the same cartesian coordinate system. The object list attributes impacted from the transformation are PositionX, PositionY, SpeedX, SpeedY and HeadingDeg. The rest of the data attribute remains untouched. Users should note that we do not claim that this transformation is perfect and there may be some misalignment among the different sensors.}, url = {https://data.nrel.gov/submissions/287}, year = {2025}, howpublished = {NREL, National Renewable Energy Laboratory, https://data.nrel.gov/submissions/287}, note = {Accessed: 2025-05-03} }

Details

Data from Mar 11, 2025

Last updated Mar 12, 2025

Submitted Mar 11, 2025

Organization

National Renewable Energy Laboratory

Contact

Rimple Sandhu

Authors

Sandhu

National Renewable Energy Laboratory

Young

National Renewable Energy Laboratory

Wang

National Renewable Energy Laboratory

Mir

National Renewable Energy Laboratory

Calles-Rios Sosa

Olsson

Sines

City of Colorado Springs

Research Areas

Share

Submission Downloads