Representative cyclist collision injury risk distributions for a dense-urban US ODD using naturalistic dash camera data

Automated driving systems (ADS) are designed toward safely navigating the roadway environment, which also includes consideration of potential conflict with other road users. Of particular concern is understanding the cumulative risk associated with vulnerable road users (VRUs) conflicts and collisions. VRUs represent a population of road users that have limited protection compared to vehicle occupants. These severity distributions are particularly useful in evaluating ADS real-world performance with respect to the existing fleet of vehicles. The objective of this study was to present event severity distributions associated with vehicle-cyclist collisions within an urban naturalistic driving environment by leveraging data from third-party vehicles instrumented with forward-facing cameras and a sensor suite (accelerometer sampling at 20 Hz and GPS, variable sampling frequency). From over 66 million miles of driving, 30 collision events were identified. A global optimization routine was used on the accelerometer and GPS data to correct for sensor orientation and asynchronicity in data sampling. For each event, two key video frames were identified: the frame associated with impact and a frame associated with key vehicle kinematics (e.g. vehicle start/stop). These key frames were then mapped to the accelerometer and GPS data to determine vehicle speed at impact. For the events included in this dataset, impact speeds ranged from approximately 3.2 kph (2 mph) to 53.1 kph (33 mph). In 82% of events, the front of the vehicle struck the cyclist. Existing cyclist injury risk curves were then used to calculate the level of risk associated with the reconstructed impacts, and the probability of AIS3+ injury risk was observed to vary from minimal risk to approximately 30%. These data highlight the wide range of impact speeds and injury risk that may occur during vehicle-cyclist collisions.