Steer Threat Number (STN)

Description

Similar to the BTN, for two actors at time \(t\), the STN [Jansson2005] [Eidehall2011] is defined as the required lateral acceleration divided by the lateral acceleration that is at most available to \(A_1\) in that direction in that scene, i.e.

\[\mathit{STN}(A_1,A_2,t) = \frac{{a}_{\mathit{lat,req}}(A_1,A_2,t)}{a_{1,\mathit{lat,min}}(t)}.\]

By definition, an STN \(\ge 1\) indicates that a lateral maneuver performed by the actor cannot avoid an impeding accident.

Properties

Run-time capability

Yes

Target values

\(\ge 1\) (point of no return)

Subject type

Road vehicles (automated and human)

Scenario type

Same as \({a}_{\mathit{lat,req}}\)

Inputs

\({a}_{\mathit{lat,req}}\), \(a_{\mathit{lat,min}}\)

Output scale

\((-\infty,\infty)\), number, ratio scale

Reliability

High, under the assumption that the non-collision condition can be reliably predicted

Validity

Strongly depends on \({a}_{\mathit{lat,req}}\) and \(a_{\text{lat,min}}\) estimate; suited for inter-vehicle comparisons; no empirical analysis available

Sensitivity

High for non-humans, as steering is often optimal, but seldomly executed by humans [Adams1994]; strongly depends on \({a}_{\mathit{lat,req}}\) and direction of \(a_{\mathit{lat,min}}\) estimation

Specificity

High, but strongly depends on \({a}_{\mathit{lat,req}}\) and direction of \(a_{\mathit{lat,min}}\) estimation

Prediction model

Time window

Unbound, but usefulness depends on DMM

Time mode

Linear time