Within the simulated world we have the ability to act as an omnipotent god, all knowing and all seeing, able to respond as needed in any situation. This is appropriate when we model physical systems; however when we model human reactions the question of “how would they respond” becomes an important question.
The human condition
When it comes to responses, people make a lot of mistakes. It could be a simple delayed response, a missed event or an incorrect interpretation of the information. Regardless of the type of error it manifests in your simulation as a “non-optimal” response.
Fortunately in our efforts to simulate humans we only have to emulate a limited set of response types. This effort looks much like our earlier post on “noise,” e.g., we can model delay, error and crossed signals.
There is a last type of human error “wrong response” and it is the trickiest to model. For every input event there are multiple incorrect responses, and there are secondary responses after the initial response. Take for example the case of a kid running out into traffic. The correct response is to apply the breaks and steer away from the child (assuming there is no one in the other lanes). Incorrect responses could include.
- Applying the accelerator (1% chance)
- swerving into oncoming traffic (1% chance)
- Asking Siri what to do (0.01% chance)
- Since car talk is no longer on the air
Once we know what sort of incorrect responses people can make, we need to determine when we should include them in our test scenarios…
My heater doesn’t care about your brakes
The obvious answer is “include human error in the applicable systems.” In the case above I would want to model human error when I am working on adaptive braking for an ADAS level 2 system. I may want to include incorrect acceleration decision in a fuel economy study, but I would not include it in an EPA fuel cycle test as it would add unneeded test cases to the system.