Can be done manually by measuring the angles between the two frames, or automatically, by using the dynamics of the vehicle
To determine the trajectory of the vehicle, it is critical to determine the orientation of the IMU to measure the acceleration along the longitudinal, lateral and vertical axes of the vehicle.
This could be achieved by carefully determining the orientation of the device in the vehicle (assumed to be constant, since the device is rigidly connected to an USB port), and compute a corresponding rotation matrix mapping the coordinates of the device to the coordinates of the vehicle
the dynamics of ground vehicles is constrained, which allows us to develop an algorithm that automatically computes the rotation matrix transforming the sensor coordinates into the vehicle coordinates.
Results to highlight :
- Different orientation to the left
- Rotated, filtered acceleration in the vehicle’s frame
- Y-axis of sensor is the one aligned with the forward axis of the car.
IMU-Based Road Condition Monitoring
IMU-based road condition
Assistant Professor, CAEE
University of Texas, Austin
Maintaining a transportation network is one of the
mandates of a transportation agency
Monitoring is usually done with expensive equipment
Tradeoff between accuracy and refresh rate
Inertial measurement Units
Proposed solution: using Inertial Measurement Units
(IMUs) onboard vehicles for monitoring road condition
IMUs are or will be part of future CVs (BSM requirement)
Could enable all CVs to generate road condition data
1. IMUs generate vector measurements (acceleration, rotation
vector, magnetic field), relative to coordinates of the device.
We need self-calibrating IMUs.
2. Road condition indices, for example the Present
Serviceability Rating (PSR) are usually obtained by
humans. How can we reliably estimate the road condition
indices from vertical acceleration timeseries data?
To determine the vertical acceleration component, we need to
compute the orientation of the IMU within the vehicle
Achieved by computing a rotation matrix mapping the
coordinates of the device to the coordinates of the vehicle. This
computation is done automatically, after a few minutes driving.
Automatic Calibration of IMUs
(Mousa, et al. ACM/IEEE IPSN‘16)
In device frame In vehicle frame
Automatic calibration allows
IMUs to be quickly and easily
retrofitted to existing vehicles for
Applications include road
condition monitoring, but also
Road condition monitoring principle
• We intend to use Supervised Learning to automatically
learn the road condition grading process used by
humans (when determining the PSR)
• Identify possible regressor variables:
- Average acceleration power spectral density in different
- Number of high acceleration events (potholes) per unit
- Peak to peak acceleration level
- Vehicle speed
• Collect measurement data on a subset of the road
network for which PSRs are available
• Train ML framework to estimate PSRs from regressors
• Are regressors relevant to explain the variations of PSR
data? How should the IMU be configured, and are
additional measurements needed (ex: microphone)? Is
the signal to noise ratio of conventional IMUs sufficient?
• How fast does the ML framework converge/how much
data is needed for training?
• Is the ML computed framework transposable to different
vehicles (within the same category)?
• The road condition monitoring system could be part of a
larger monitoring system, including traffic monitoring
• Could be integrated with Bluetooth/WiFi readers, or
Roadside Equipments (RSEs). The readers would
receive measurement data from almost all vehicles