Worst Time To Collision (WTTC)#
Description#
The WTTC metric extends the usual TTC by considering multiple traces of actors as predicted by an over-approximating DMM, i.e.
where \(\mathit{Tr}_1(t)\) resp. \(\mathit{Tr}_2(t)\) denotes the set of all possible trajectories available to actor \(A_1\) resp. \(A_2\) at time \(t\), as constraint by the employed DMM. Similar to the TTC, the WTTC can be extended to multi-actor scenarios. Defined by Wachenfeld et al. [Wachenfeld2016], it excels in selective data recording and data filtering applications.
Properties#
Run-time capability#
Yes
Target values#
1 s (scenario classification) [Huber2020], comparison with ACC \(\tau\) (gap time) made in [Wachenfeld2016]
Subject type#
Optimal for road vehicles (automated and human), sub-optimal for VRUs
Scenario type#
Overlapping predicted trajectories for a significant time span in the scenario
Inputs#
Static/dynamic objects and their state (pose, shape, etc.) at time t
Output scale#
\([0,\infty]\), time (s), ratio scale
Reliability#
Medium, as over-approximating DMM robustly assesses criticality increases (expert-based evaluation [Wachenfeld2016]), but decreases potentially not reliably reflected
Validity#
Medium, as most critical scenarios can be detected depending on the DMM, but also not able to distinguish many uncritical ones, initial expert-based evaluation on four scenarios has been published [Wachenfeld2016]
Sensitivity#
Almost maximal, due to over-approximation of possible trajectories, depends on DMM (e.g. whether unstable dynamics are considered)
Specificity#
Low, due to over-approximation of possible trajectories, depends on DMM
Prediction model#
Time window#
Unbound, but usefulness depends on DMM
Time mode#
Branching time