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