Validate the performance of dedicated short-range communication (DSRC) radios for cooperative adaptive cruise control (CACC) applications by testing receiver signal strength, signal delay, and packet error rates.

USDOT developed an evaluation framework to validate the performance of vehicle-to-vehicle communications for cooperative adaptive cruise control (CACC) systems.

Date Posted

Test and Evaluation of Vehicle Platooning Proof-Of-Concept Based on Cooperative Adaptive Cruise Control

Summary Information

This source report examines vehicle platooning solutions that use cooperative adaptive cruise control (CACC) and vehicle-to-vehicle communications to enable automatic synchronization of longitudinal movements of platooned vehicles. In July 2016, performance testing was conducted using a 5-car platooning proof-of-concept on a closed track under normal driving conditions similar to that of public roadways.

Normal driving characterization tests were designed to create conditions where the lead vehicle was under speed control mode and between one and four following vehicles were under gap control mode once a platoon was formed. Forming the platoon was done under manual control by professional drivers. Drivers were able to brake/steer/accelerate manually in a naturalistic manner without the need to press switch or button to resume control of the vehicle.

Testing was conducting using five Cadillac SRX vehicles equipped with Data Acquisition Systems (DAS) with FHWAs proof-of-concept platooning system implemented on the Connected Automated Research and Mobility Applications (CARMA) platform. When in CACC mode, the lead vehicle had a prescribed speed profile application that used geolocation information and Global Positioning System (GPS) positioning to automatically set its desired speed and other CACC parameters. The geolocations used to set or make changes were called 'Waypoints' along the 7.2-km track. Thus, the lead vehicle was equipped with a custom speed controller to follow the tightly controlled speed profile by using GPS to identify predefined waypoints that triggered the lead vehicle to change its speed to a predefined value.


Based on the test track results the Volpe Center offered several recommendations for features of the CACC-based vehicle platooning system.

Design of Vehicle Platooning Proof-of-Concept

  • When not required for safety, command smoother acceleration changes in order to reduce the extreme torque modulation, shifting, and braking events that occur in both the lead vehicles and following vehicles. This may include each following vehicle easing its acceleration/deceleration as it nears the specified time gap to avoid an over/undershoot.
  • Base the lead vehicle speed profile on the concept of operations for the overall CACC system, which may include:
    • Easing the profile at the start and end of speed changes, which should contribute to smoother acceleration changes and better following stability.
    • Relaxing the dead-band when maintaining a constant speed, which should decrease the frequency of acceleration changes and contribute to better following vehicle stability.
  • While the following vehicle performance must still be safe during contingency braking events, overall string stability may not be a priority in these situations.
  • Make more lead vehicle and following vehicle information available to the following vehicles to improve following vehicles independent threat assessments.

Test and Evaluation Procedures

  • System Documentation: Clearly document the vehicle subsystems and controller design prior to finalizing the test and evaluation plan. This will allow the test and evaluation to be tailored to the specific control objectives.
  • Testing Safety Plan: There are challenges in ensuring driver safety when testing and validating the performance of the cooperative driving automation systems from under normal driving to conflict driving conditions. The following steps are recommended to address these challenges:
    • Have high confidence that the application under test can appropriately respond to pre-crash scenarios commonly encountered on the road.
    • Use simulation, such as software- and hardware-in-the-loop simulations, to advance system/subsystem performance in challenging conditions.
    • Formalize the software and hardware development process of the application to include traceability and functional validation to safe level requirements following system and functional safety standards such as the International Organization for Standardization (ISO) 26262 standard.
  • Test Validity Criteria:
    • Establish and test validity criteria for V2V and V2I DSRC radio performance using some combination of packet error rates, received signal strength, and received signal delay.
    • Establish and test validity criteria for platoon stability, and adjust the number and positioning of waypoints to allow adequate time for the platoon to stabilize while balancing for testing efficiency.
  • Test Procedures:
    • Repeat the same vehicle sequences when comparing the ACC versus CACC controller performance.
    • Performing multiple runs with different vehicle sequences for each procedure would still permit assessing the inter-vehicle performance variations.
    • Collect at least 24 hours of data with the vehicles at rest for assessment of GPS measurements (i.e., noise and bias in position, velocity, and acceleration).
    • Validate following vehicle threat assessment capabilities and the data elements used and available from over-the-air and in-vehicle sensors.

Performance Measures and Data Elements

  • Identify the detailed analysis plan (i.e., objectives, performance metrics, and criteria) and associated data elements prior to finalizing the vehicle and system design to ensure the required data elements are available.
  • Confirm that all data elements that are inputs or outputs to the controller are available for data collection. In addition, include outputs of internal data filters (e.g., smoothed acceleration) to avoid unnecessary post-processing.
  • Identify and validate fuel economy performance measures for comparison between the cooperative driving automation systems and baseline vehicle systems.
  • Ensure that the quality (e.g., noise, bias, frequency, etc.) of the data elements supports the analysis objectives. This will likely require laboratory or limited field verification of the system performance.
  • Establish procedures to verify the quality of the data collection in near-real time. Ideally, this would be performed on at least a daily basis and while there is adequate time remaining in the test campaign to repeat tests in the event critical data are not available.