During the 2014 ITS World Congress a demonstration of the connected vehicle infrastructure in the City of Detroit was conducted. The test site included approximately 14 intersections around Detroit’s COBO convention center and involved 9 equipped vehicles. The Vehicle Situation Data (VSD) data set includes a series of data files that recorded vehicle situational data that were generated by an equipped vehicle. During the ITS World Congress, VSDs were encoded with one of two schemas. The dataset contains decoded data using both 2.0 and 2.1 ASN.1 schemas.

During the 2014 ITS World Congress a demonstration of the connected vehicle infrastructure in the City of Detroit was conducted. The test site included approximately 14 intersections around Detroit’s COBO convention center and involved 9 equipped vehicles. The Vehicle Situation Data (VSD) data set includes a series of data files that recorded vehicle situational data that were generated by an equipped vehicle. During the ITS World Congress, VSDs were encoded with one of two schemas. The dataset contains decoded data using both 2.0 and 2.1 ASN.1 schemas.

During the 2014 ITS World Congress a demonstration of the connected vehicle infrastructure in the City of Detroit was conducted. The test site included approximately 14 intersections around Detroit’s COBO convention center and involved 9 equipped vehicles. The Vehicle Situation Data (VSD) data set includes a series of data files that recorded vehicle situational data that were generated by an equipped vehicle. During the ITS World Congress, VSDs were encoded with one of two schemas. The dataset contains decoded data using both 2.0 and 2.1 ASN.1 schemas.

During the 2014 ITS World Congress a demonstration of the connected vehicle infrastructure in the City of Detroit was conducted. The test site included approximately 14 intersections around Detroit’s COBO convention center and involved 9 equipped vehicles. The Vehicle Situation Data (VSD) data set includes a series of data files that recorded vehicle situational data that were generated by an equipped vehicle. During the ITS World Congress, VSDs were encoded with one of two schemas. The dataset contains decoded data using both 2.0 and 2.1 ASN.1 schemas.

During the 2014 ITS World Congress a demonstration of the connected vehicle infrastructure in the City of Detroit was conducted. The test site included approximately 14 intersections around Detroit’s COBO convention center and involved 9 equipped vehicles. The Vehicle Situation Data (VSD) data set includes a series of data files that recorded vehicle situational data that were generated by an equipped vehicle. During the ITS World Congress, VSDs were encoded with one of two schemas. The dataset contains decoded data using both 2.0 and 2.1 ASN.1 schemas.

During the 2014 ITS World Congress a demonstration of the connected vehicle infrastructure in the City of Detroit was conducted. The test site included approximately 14 intersections around Detroit’s COBO convention center and involved 9 equipped vehicles. The Vehicle Situation Data (VSD) data set includes a series of data files that recorded vehicle situational data that were generated by an equipped vehicle. During the ITS World Congress, VSDs were encoded with one of two schemas. The dataset contains decoded data using both 2.0 and 2.1 ASN.1 schemas.

During the 2014 ITS World Congress a demonstration of the connected vehicle infrastructure in the City of Detroit was conducted. The test site included approximately 14 intersections around Detroit’s COBO convention center and involved 9 equipped vehicles. The Vehicle Situation Data (VSD) data set includes a series of data files that recorded vehicle situational data that were generated by an equipped vehicle. During the ITS World Congress, VSDs were encoded with one of two schemas. The dataset contains decoded data using both 2.0 and 2.1 ASN.1 schemas.

During the 2014 ITS World Congress a demonstration of the connected vehicle infrastructure in the City of Detroit was conducted. The test site included approximately 14 intersections around Detroit’s COBO convention center and involved 9 equipped vehicles. The Vehicle Situation Data (VSD) data set includes a series of data files that recorded vehicle situational data that were generated by an equipped vehicle. During the ITS World Congress, VSDs were encoded with one of two schemas. The dataset contains decoded data using both 2.0 and 2.1 ASN.1 schemas.

During the 2014 ITS World Congress a demonstration of the connected vehicle infrastructure in the City of Detroit was conducted. The test site included approximately 14 intersections around Detroit’s COBO convention center and involved 9 equipped vehicles. The Vehicle Situation Data (VSD) data set includes a series of data files that recorded vehicle situational data that were generated by an equipped vehicle. During the ITS World Congress, VSDs were encoded with one of two schemas. The dataset contains decoded data using both 2.0 and 2.1 ASN.1 schemas.

During the 2014 ITS World Congress a demonstration of the connected vehicle infrastructure in the City of Detroit was conducted. The test site included approximately 14 intersections around Detroit’s COBO convention center and involved 9 equipped vehicles. The Vehicle Situation Data (VSD) data set includes a series of data files that recorded vehicle situational data that were generated by an equipped vehicle. During the ITS World Congress, VSDs were encoded with one of two schemas. The dataset contains decoded data using both 2.0 and 2.1 ASN.1 schemas.

During the 2014 ITS World Congress a demonstration of the connected vehicle infrastructure in the City of Detroit was conducted. The test site included approximately 14 intersections around Detroit’s COBO convention center and involved 9 equipped vehicles. The Vehicle Situation Data (VSD) data set includes a series of data files that recorded vehicle situational data that were generated by an equipped vehicle. During the ITS World Congress, VSDs were encoded with one of two schemas. The dataset contains decoded data using both 2.0 and 2.1 ASN.1 schemas.

During the 2014 ITS World Congress a demonstration of the connected vehicle infrastructure in the City of Detroit was conducted. The test site included approximately 14 intersections around Detroit’s COBO convention center and involved 9 equipped vehicles. The Vehicle Situation Data (VSD) data set includes a series of data files that recorded vehicle situational data that were generated by an equipped vehicle. During the ITS World Congress, VSDs were encoded with one of two schemas. The dataset contains decoded data using both 2.0 and 2.1 ASN.1 schemas.

The datasets contain the subject ADAS-equipped vehicle’s trajectory collected in naturalistic traffic conditions in central Ohio. The instrumented subject vehicle was either a discreet or readily-identifiable ADAS-equipped vehicle with SAE L2 capabilities. The dataset also contains trajectories for adjacent vehicles in traffic (observed by the subject vehicle’s onboard sensors).

The datasets contain the subject ADAS-equipped vehicle’s trajectory collected in naturalistic traffic conditions in central Ohio. The instrumented subject vehicle was either a discreet or readily-identifiable ADAS-equipped vehicle with SAE L2 capabilities. The dataset also contains trajectories for adjacent vehicles in traffic (observed by the subject vehicle’s onboard sensors).

The datasets contain the subject ADAS-equipped vehicle’s trajectory collected in naturalistic traffic conditions in central Ohio. The instrumented subject vehicle was either a discreet or readily-identifiable ADAS-equipped vehicle with SAE L2 capabilities. The dataset also contains trajectories for adjacent vehicles in traffic (observed by the subject vehicle’s onboard sensors).

The datasets contain the subject ADAS-equipped vehicle’s trajectory collected in naturalistic traffic conditions in central Ohio. The instrumented subject vehicle was either a discreet or readily-identifiable ADAS-equipped vehicle with SAE L2 capabilities. The dataset also contains trajectories for adjacent vehicles in traffic (observed by the subject vehicle’s onboard sensors).

The datasets contain the subject ADAS-equipped vehicle’s trajectory collected in naturalistic traffic conditions in central Ohio. The instrumented subject vehicle was either a discreet or readily-identifiable ADAS-equipped vehicle with SAE L2 capabilities. The dataset also contains trajectories for adjacent vehicles in traffic (observed by the subject vehicle’s onboard sensors).

The datasets contain the subject ADAS-equipped vehicle’s trajectory collected in naturalistic traffic conditions in central Ohio. The instrumented subject vehicle was either a discreet or readily-identifiable ADAS-equipped vehicle with SAE L2 capabilities. The dataset also contains trajectories for adjacent vehicles in traffic (observed by the subject vehicle’s onboard sensors).

The datasets contain the subject ADAS-equipped vehicle’s trajectory collected in naturalistic traffic conditions in central Ohio. The instrumented subject vehicle was either a discreet or readily-identifiable ADAS-equipped vehicle with SAE L2 capabilities. The dataset also contains trajectories for adjacent vehicles in traffic (observed by the subject vehicle’s onboard sensors).

The datasets contain the subject ADAS-equipped vehicle’s trajectory collected in naturalistic traffic conditions in central Ohio. The instrumented subject vehicle was either a discreet or readily-identifiable ADAS-equipped vehicle with SAE L2 capabilities. The dataset also contains trajectories for adjacent vehicles in traffic (observed by the subject vehicle’s onboard sensors).

The datasets contain the subject ADAS-equipped vehicle’s trajectory collected in naturalistic traffic conditions in central Ohio. The instrumented subject vehicle was either a discreet or readily-identifiable ADAS-equipped vehicle with SAE L2 capabilities. The dataset also contains trajectories for adjacent vehicles in traffic (observed by the subject vehicle’s onboard sensors).

The datasets contain the subject ADAS-equipped vehicle’s trajectory collected in naturalistic traffic conditions in central Ohio. The instrumented subject vehicle was either a discreet or readily-identifiable ADAS-equipped vehicle with SAE L2 capabilities. The dataset also contains trajectories for adjacent vehicles in traffic (observed by the subject vehicle’s onboard sensors).

Dataset contains two subject vehicles’ trajectory data connected in naturalistic traffic conditions in central Ohio. Instrumented subject vehicles were either a discreet or readily-identifiable ADAS-equipped vehicle with SAE L2 capabilities. Dataset also contains trajectories for adjacent vehicles in traffic (observed by the subject vehicles’ onboard sensors).

Dataset contains two subject vehicles’ trajectory data connected in naturalistic traffic conditions in central Ohio. Instrumented subject vehicles were either a discreet or readily-identifiable ADAS-equipped vehicle with SAE L2 capabilities. Dataset also contains trajectories for adjacent vehicles in traffic (observed by the subject vehicles’ onboard sensors).

Dataset contains two subject vehicles’ trajectory data connected in naturalistic traffic conditions in central Ohio. Instrumented subject vehicles were either a discreet or readily-identifiable ADAS-equipped vehicle with SAE L2 capabilities. Dataset also contains trajectories for adjacent vehicles in traffic (observed by the subject vehicles’ onboard sensors).

Dataset contains two subject vehicles’ trajectory data connected in naturalistic traffic conditions in central Ohio. Instrumented subject vehicles were either a discreet or readily-identifiable ADAS-equipped vehicle with SAE L2 capabilities. Dataset also contains trajectories for adjacent vehicles in traffic (observed by the subject vehicles’ onboard sensors).

Dataset contains two subject vehicles’ trajectory data connected in naturalistic traffic conditions in central Ohio. Instrumented subject vehicles were either a discreet or readily-identifiable ADAS-equipped vehicle with SAE L2 capabilities. Dataset also contains trajectories for adjacent vehicles in traffic (observed by the subject vehicles’ onboard sensors).

Dataset contains two subject vehicles’ trajectory data connected in naturalistic traffic conditions in central Ohio. Instrumented subject vehicles were either a discreet or readily-identifiable ADAS-equipped vehicle with SAE L2 capabilities. Dataset also contains trajectories for adjacent vehicles in traffic (observed by the subject vehicles’ onboard sensors).

Dataset contains two subject vehicles’ trajectory data connected in naturalistic traffic conditions in central Ohio. Instrumented subject vehicles were either a discreet or readily-identifiable ADAS-equipped vehicle with SAE L2 capabilities. Dataset also contains trajectories for adjacent vehicles in traffic (observed by the subject vehicles’ onboard sensors).

Dataset contains two subject vehicles’ trajectory data connected in naturalistic traffic conditions in central Ohio. Instrumented subject vehicles were either a discreet or readily-identifiable ADAS-equipped vehicle with SAE L2 capabilities. Dataset also contains trajectories for adjacent vehicles in traffic (observed by the subject vehicles’ onboard sensors).

Dataset contains two subject vehicles’ trajectory data connected in naturalistic traffic conditions in central Ohio. Instrumented subject vehicles were either a discreet or readily-identifiable ADAS-equipped vehicle with SAE L2 capabilities. Dataset also contains trajectories for adjacent vehicles in traffic (observed by the subject vehicles’ onboard sensors).

Data is from the small-scale demonstration of the Intelligent Network Flow Optimization (INFLO) Prototype System and applications in Seattle, Washington. Connected vehicle systems were deployed in 21 vehicles in a scripted driving scenario circuiting this I-5 corridor northbound and southbound during morning rush hour. This data set contains speed harmonization messages that were recommended by the INFLO SPD-HARM algorithm and sent by the traffic management center to the connected vehicles, which provided drivers with the suggested speed while driving on the segment of I-5 that was included in the test. The objective of speed harmonization is to dynamically adjust and coordinate maximum appropriate vehicle speeds in response to downstream congestion, incidents, and weather or road conditions in order to maximize traffic throughput and reduce crashes.

Data is from the small-scale demonstration of the Intelligent Network Flow Optimization (INFLO) Prototype System and applications in Seattle, Washington. Connected vehicle systems were deployed in 21 vehicles in a scripted driving scenario circuiting this I-5 corridor northbound and southbound during morning rush hour. This data set contains speed harmonization messages that were recommended by the INFLO SPD-HARM algorithm and sent by the traffic management center to the connected vehicles, which provided drivers with the suggested speed while driving on the segment of I-5 that was included in the test. The objective of speed harmonization is to dynamically adjust and coordinate maximum appropriate vehicle speeds in response to downstream congestion, incidents, and weather or road conditions in order to maximize traffic throughput and reduce crashes.

Data is from the small-scale demonstration of the Intelligent Network Flow Optimization (INFLO) Prototype System and applications in Seattle, Washington. Connected vehicle systems were deployed in 21 vehicles in a scripted driving scenario circuiting this I-5 corridor northbound and southbound during morning rush hour. This data set contains speed harmonization messages that were recommended by the INFLO SPD-HARM algorithm and sent by the traffic management center to the connected vehicles, which provided drivers with the suggested speed while driving on the segment of I-5 that was included in the test. The objective of speed harmonization is to dynamically adjust and coordinate maximum appropriate vehicle speeds in response to downstream congestion, incidents, and weather or road conditions in order to maximize traffic throughput and reduce crashes.

Data is from the small-scale demonstration of the Intelligent Network Flow Optimization (INFLO) Prototype System and applications in Seattle, Washington. Connected vehicle systems were deployed in 21 vehicles in a scripted driving scenario circuiting this I-5 corridor northbound and southbound during morning rush hour. This data set contains speed harmonization messages that were recommended by the INFLO SPD-HARM algorithm and sent by the traffic management center to the connected vehicles, which provided drivers with the suggested speed while driving on the segment of I-5 that was included in the test. The objective of speed harmonization is to dynamically adjust and coordinate maximum appropriate vehicle speeds in response to downstream congestion, incidents, and weather or road conditions in order to maximize traffic throughput and reduce crashes.

Data is from the small-scale demonstration of the Intelligent Network Flow Optimization (INFLO) Prototype System and applications in Seattle, Washington. Connected vehicle systems were deployed in 21 vehicles in a scripted driving scenario circuiting this I-5 corridor northbound and southbound during morning rush hour. This data set contains speed harmonization messages that were recommended by the INFLO SPD-HARM algorithm and sent by the traffic management center to the connected vehicles, which provided drivers with the suggested speed while driving on the segment of I-5 that was included in the test. The objective of speed harmonization is to dynamically adjust and coordinate maximum appropriate vehicle speeds in response to downstream congestion, incidents, and weather or road conditions in order to maximize traffic throughput and reduce crashes.

Data is from the small-scale demonstration of the Intelligent Network Flow Optimization (INFLO) Prototype System and applications in Seattle, Washington. Connected vehicle systems were deployed in 21 vehicles in a scripted driving scenario circuiting this I-5 corridor northbound and southbound during morning rush hour. This data set contains speed harmonization messages that were recommended by the INFLO SPD-HARM algorithm and sent by the traffic management center to the connected vehicles, which provided drivers with the suggested speed while driving on the segment of I-5 that was included in the test. The objective of speed harmonization is to dynamically adjust and coordinate maximum appropriate vehicle speeds in response to downstream congestion, incidents, and weather or road conditions in order to maximize traffic throughput and reduce crashes.

Data is from the small-scale demonstration of the Intelligent Network Flow Optimization (INFLO) Prototype System and applications in Seattle, Washington. Connected vehicle systems were deployed in 21 vehicles in a scripted driving scenario circuiting this I-5 corridor northbound and southbound during morning rush hour. This data set contains speed harmonization messages that were recommended by the INFLO SPD-HARM algorithm and sent by the traffic management center to the connected vehicles, which provided drivers with the suggested speed while driving on the segment of I-5 that was included in the test. The objective of speed harmonization is to dynamically adjust and coordinate maximum appropriate vehicle speeds in response to downstream congestion, incidents, and weather or road conditions in order to maximize traffic throughput and reduce crashes.

Data is from the small-scale demonstration of the Intelligent Network Flow Optimization (INFLO) Prototype System and applications in Seattle, Washington. Connected vehicle systems were deployed in 21 vehicles in a scripted driving scenario circuiting this I-5 corridor northbound and southbound during morning rush hour. This data set contains speed harmonization messages that were recommended by the INFLO SPD-HARM algorithm and sent by the traffic management center to the connected vehicles, which provided drivers with the suggested speed while driving on the segment of I-5 that was included in the test. The objective of speed harmonization is to dynamically adjust and coordinate maximum appropriate vehicle speeds in response to downstream congestion, incidents, and weather or road conditions in order to maximize traffic throughput and reduce crashes.

Data is from the small-scale demonstration of the Intelligent Network Flow Optimization (INFLO) Prototype System and applications in Seattle, Washington. Connected vehicle systems were deployed in 21 vehicles in a scripted driving scenario circuiting this I-5 corridor northbound and southbound during morning rush hour. This data set contains speed harmonization messages that were recommended by the INFLO SPD-HARM algorithm and sent by the traffic management center to the connected vehicles, which provided drivers with the suggested speed while driving on the segment of I-5 that was included in the test. The objective of speed harmonization is to dynamically adjust and coordinate maximum appropriate vehicle speeds in response to downstream congestion, incidents, and weather or road conditions in order to maximize traffic throughput and reduce crashes.

The Belle Isle data was collected between May 1st, 2014 and September 16th, 2014 on the Belle Isle Park in Michigan. However, within the data file provided as part of this data environment, only data during the World Congress demonstration period from September 5, 2014 to September 11, 2014 is included. Several vehicles equipped with multiple sensors drove around the island collecting 572,030 readings of multiple variables. The uploaded data file lists all those observations and the pertaining details about the sensor equipment, the sensor platform and the status of quality checking performed for each observation.

The Belle Isle data was collected between May 1st, 2014 and September 16th, 2014 on the Belle Isle Park in Michigan. However, within the data file provided as part of this data environment, only data during the World Congress demonstration period from September 5, 2014 to September 11, 2014 is included. Several vehicles equipped with multiple sensors drove around the island collecting 572,030 readings of multiple variables. The uploaded data file lists all those observations and the pertaining details about the sensor equipment, the sensor platform and the status of quality checking performed for each observation.

The Belle Isle data was collected between May 1st, 2014 and September 16th, 2014 on the Belle Isle Park in Michigan. However, within the data file provided as part of this data environment, only data during the World Congress demonstration period from September 5, 2014 to September 11, 2014 is included. Several vehicles equipped with multiple sensors drove around the island collecting 572,030 readings of multiple variables. The uploaded data file lists all those observations and the pertaining details about the sensor equipment, the sensor platform and the status of quality checking performed for each observation.

The Belle Isle data was collected between May 1st, 2014 and September 16th, 2014 on the Belle Isle Park in Michigan. However, within the data file provided as part of this data environment, only data during the World Congress demonstration period from September 5, 2014 to September 11, 2014 is included. Several vehicles equipped with multiple sensors drove around the island collecting 572,030 readings of multiple variables. The uploaded data file lists all those observations and the pertaining details about the sensor equipment, the sensor platform and the status of quality checking performed for each observation.

The Belle Isle data was collected between May 1st, 2014 and September 16th, 2014 on the Belle Isle Park in Michigan. However, within the data file provided as part of this data environment, only data during the World Congress demonstration period from September 5, 2014 to September 11, 2014 is included. Several vehicles equipped with multiple sensors drove around the island collecting 572,030 readings of multiple variables. The uploaded data file lists all those observations and the pertaining details about the sensor equipment, the sensor platform and the status of quality checking performed for each observation.

The Belle Isle data was collected between May 1st, 2014 and September 16th, 2014 on the Belle Isle Park in Michigan. However, within the data file provided as part of this data environment, only data during the World Congress demonstration period from September 5, 2014 to September 11, 2014 is included. Several vehicles equipped with multiple sensors drove around the island collecting 572,030 readings of multiple variables. The uploaded data file lists all those observations and the pertaining details about the sensor equipment, the sensor platform and the status of quality checking performed for each observation.

The Belle Isle data was collected between May 1st, 2014 and September 16th, 2014 on the Belle Isle Park in Michigan. However, within the data file provided as part of this data environment, only data during the World Congress demonstration period from September 5, 2014 to September 11, 2014 is included. Several vehicles equipped with multiple sensors drove around the island collecting 572,030 readings of multiple variables. The uploaded data file lists all those observations and the pertaining details about the sensor equipment, the sensor platform and the status of quality checking performed for each observation.

The Belle Isle data was collected between May 1st, 2014 and September 16th, 2014 on the Belle Isle Park in Michigan. However, within the data file provided as part of this data environment, only data during the World Congress demonstration period from September 5, 2014 to September 11, 2014 is included. Several vehicles equipped with multiple sensors drove around the island collecting 572,030 readings of multiple variables. The uploaded data file lists all those observations and the pertaining details about the sensor equipment, the sensor platform and the status of quality checking performed for each observation.

The Belle Isle data was collected between May 1st, 2014 and September 16th, 2014 on the Belle Isle Park in Michigan. However, within the data file provided as part of this data environment, only data during the World Congress demonstration period from September 5, 2014 to September 11, 2014 is included. Several vehicles equipped with multiple sensors drove around the island collecting 572,030 readings of multiple variables. The uploaded data file lists all those observations and the pertaining details about the sensor equipment, the sensor platform and the status of quality checking performed for each observation.

The Belle Isle data was collected between May 1st, 2014 and September 16th, 2014 on the Belle Isle Park in Michigan. However, within the data file provided as part of this data environment, only data during the World Congress demonstration period from September 5, 2014 to September 11, 2014 is included. Several vehicles equipped with multiple sensors drove around the island collecting 572,030 readings of multiple variables. The uploaded data file lists all those observations and the pertaining details about the sensor equipment, the sensor platform and the status of quality checking performed for each observation.