Predictive Encroachment Time (PrET)#
Description#
Here, we summarize the various predictive versions of the PET. The PrET [Neurohr2021] is the anticipated PET relative to an intersection point as predicted by the employed DMM, hence
The Time Advantage (TA) metric [Hansson1975] can be interpreted as a special case of PrET for a constant velocity model, i.e. \(p_i(s+t) = p_i(t) + s v_i(t)\). A scaled variant of the PrET, labeled Scaled Predictive Encroachment Time (SPrET), modifies the value of PrET by multiplication with the factor \((\tilde{t}_1+\tilde{t}_2)\), i.e.
in order to decrease the weight of situations long before the predicted intersection [Neurohr2021]. Therefore, the SPrET incorporates prediction uncertainty.
Properties#
Note
Includes properties for SPrET and TA.
Run-time capability#
Yes
Target values#
2 s (threshold for critical) and \([2,3]\) s (normal traffic) for TA [Laureshyn2010], 3 s (threshold for critical) for SPrET [Neurohr2021]
Subject type#
Any two actors
Scenario type#
Any scenario with a conflict area (containing a potential intersection point)
Inputs#
Static/dynamic objects and their state at time t, DMM for each object
Output scale#
\([0,\infty]\), time (s), ratio scale
Reliability#
Comparable to PET, but additionally dependent on DMM
Validity#
Comparable to PET, but additionally dependent on DMM, increased validity for run-time applications; no empirical analysis available
Sensitivity#
Comparable to PET, but additionally dependent on DMM
Specificity#
Comparable to PET, but specificity decreases with increasing distance to intersection
Prediction model#
Time window#
Unbound, but usefulness depends on DMM
Time mode#
Linear time