SUV Kinematics during a Steer-Induced Rollover Resolved Using Consumer-Grade Video, Laser Scans and Match-Moving Techniques

SAE Intwernational cole young

Young C, King D, Siegmund G (2020). SUV Kinematics during a Steer-Induced Rollover Resolved Using Consumer-Grade Video, Laser Scans and Match-Moving Techniques. SAE Technical Paper. 2020-01-0642.

Rollover crashes are complex events that generate motions in all six degrees of freedom (6DOF). Directly quantifying the angular rotations from video can be difficult and vehicle orientation as a function of time is often not reported for staged rollover crashes. Our goal was to evaluate the ability of using a match-moving technique and consumer-grade video cameras to quantify the roll, pitch and yaw angles and angular velocities of a rollover crash.

We staged a steer-induced rollover of an SUV at 106 km/h. The vehicle was fitted with tri-axial accelerometers and angular rate sensors, and five consumer-grade video cameras (2 on tripods, 2 on drones, 1 handheld, ~30 fps) captured the event. Roll, pitch and yaw angles were determined from the video using specialized software. We then compared the vehicle orientation angles from the video data to the integrated angular rate data measured by onboard sensors, and also compared the angular rates from the differentiated video data to the angular rates measured directly by the sensors.

We found that both methods of measuring the 3D angles and angular rates generated similar results. The integrated sensor data drifted a maximum of 13° relative to the video-based angles, with RMS differences of ±2.7° or less when the drift was removed. The differentiated video data did not drift relative to the sensor data, with RMS differences of ±0.22 rad/s or less.

These findings indicate that both methods generate similar results and are suitable for reconstructing rollovers. Given the drift we observed in the integrated sensor data, we recommend using angle measurements from the video to quantify the amount of drift in integrated sensor data if accurate knowledge of the vehicle’s orientation as a function of time is important.

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SUV Kinematics during a Steer-Induced Rollover Resolved Using Consumer-Grade Video, Laser Scans and Match-Moving Techniques

 

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