Trevor Ross

Every one of these dots is an actual crash from the fleet. Real world speeds, collisions, and people. Not just the regulatory test cases. The richness of this data is what enabled the result. With simulation we can replay the crashes and measure the forces on the human body model. Then sweep through restraint deployment times to find that deploying earlier gives the time for the bag to be inflated optimally and seat belt pretension before the occupant has moved out of position. But it takes time for crash accelerometers to be certain. Lowering that time threshold risks unwanted deployments. Using vision gives the vehicle confidence to reduce that timing. The camera sees the impending impact and together with the sensors tell the restraint controller to reduce the filter and act sooner. The Y Axis shift in Predicted Injury Severity is based on sensors in the human body models from rerunning the crash simulations with the faster detection threshold. Such a reduction in injury severity across the spectrum is unheard of, let alone doing this via an update over the air. I'm extremely proud of the analysis team's work and dedication. Going above and beyond to ask "we have the safest car on the road but can we make it even safer?" And then working with the vision team to build the predictions needed to make it happen. Rigorously tested in simulation and then in physical crash testing. Now deployed and improving lives. I watch the video on loop and just imagine each dot, a person.