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

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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.

Data collected at the intersection of N Nevada Ave & E Platte Ave, Colorado Springs, CO, USA, using 2 EVO radars at 10 Hz, 2 Ouster lidars at 10 Hz, and 1 Axis Camera at 10 Hz, spanning a total duration of 63 mins.

Data collected at the intersection of N Nevada Ave & E Platte Ave, Colorado Springs, CO, USA, using 2 EVO radars at 10 Hz, 2 Ouster lidars at 10 Hz, and 1 Axis Camera ... more

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Data collected at the intersection of N Powers Blvd & Palmer Park Blvd, Colorado Springs, CO, USA, using 2 EVO radars at 1 Hz and 2 EVO radars at 2 Hz, spanning a total duration of 80 mins.

Data collected at the intersection of N Powers Blvd & Palmer Park Blvd, Colorado Springs, CO, USA, using 2 EVO radars at 1 Hz and 2 EVO radars at 2 Hz, spanning a total... more

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Data collected at the intersection of N Powers Blvd & Palmer Park Blvd, Colorado Springs, CO, USA, using 2 EVO radars at 1 Hz, 2 EVO radars at 2 Hz, 1 Ouster lidar at 10 Hz, spanning a total duration of 84 mins.

Data collected at the intersection of N Powers Blvd & Palmer Park Blvd, Colorado Springs, CO, USA, using 2 EVO radars at 1 Hz, 2 EVO radars at 2 Hz, 1 Ouster lidar at ... more

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Data collected at the intersection of N Powers Blvd & Palmer Park Blvd, Colorado Springs, CO, USA, using 4 EVO radars at 1 Hz (85 mins) and 10 Hz (8 mins), 1 Ouster lidar at 10 Hz, and 1 Axis Camera at 10 Hz, spanning a total duration of 93 mins.

Data collected at the intersection of N Powers Blvd & Palmer Park Blvd, Colorado Springs, CO, USA, using 4 EVO radars at 1 Hz (85 mins) and 10 Hz (8 mins), 1 Ouster lid... more

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Data collected at the intersection of Stapleton Dr & Meridian Rd, El Paso County, CO, USA, using 4 EVO radars at 2 Hz and 2 Ouster lidars at 10 Hz, spanning a total duration of 94 mins.

Data collected at the intersection of Stapleton Dr & Meridian Rd, El Paso County, CO, USA, using 4 EVO radars at 2 Hz and 2 Ouster lidars at 10 Hz, spanning a total dur... more

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More details on the data and the sensors.

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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, Rimple
A2  - Young, Stanley
A3  - Wang, Qichao
A4  - Mir, Faizan
A5  - Calles-Rios Sosa, Zoami
A6  - Sines, Dan
DB  - Open Energy Data Initiative (OEDI)
DP  - Open EI | National Laboratory of the Rockies
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, Rimple, 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.nlr.gov/submissions/287.

Sandhu, R., Young, S., Wang, Q., Mir, F., Calles-Rios Sosa, Z., & Sines, D. (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.nlr.gov/submissions/287

Sandhu, Rimple, Stanley Young, Qichao Wang, Faizan Mir, Zoami Calles-Rios Sosa, and Dan 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.nlr.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, Rimple and Young, Stanley and Wang, Qichao and Mir, Faizan and Calles-Rios Sosa, Zoami and Sines, Dan},
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.nlr.gov/submissions/287},
year = {2025},
howpublished = {NREL, National Renewable Energy Laboratory, https://data.nlr.gov/submissions/287},
note = {Accessed: 2026-11-10}
}

Details

Data from Mar 11, 2025

Last updated Mar 12, 2026

Submitted Mar 11, 2025