Crash Potential Index (CPI)#
Description#
The CPI is a scenario-level metric and calculates the average probability that a vehicle can not avoid a collision by deceleration. It sums over the probabilities that a given vehicle’s \({a}_{\mathit{long,req}}\) exceeds its \(a_{\mathit{long},\mathit{min}}\) for each time point and normalizes the value over the length of the scenario [Cunto2007] [Cunto2008]. The target value \(a_{\mathit{long},\mathit{min}}\) is assumed to be normally distributed and dependent on factors such as road surface material and vehicle brakes. While originally defined in discrete time, the CPI for a scenario can be defined in continuous time as
Note that this concept of aggregation over time can be generalized to be applicable to other metrics, assuming that a valid probability distribution of the target value can be given. This potentially enables a more precise identification of criticality within a scenario.
Properties#
Run-time capability#
No
Target values#
Average CPI was found to be 0.00491% in simulation, suggesting higher values as target values, e.g. 0.0072% (upper limit of 95%-confidence interval) [Cunto2008]
Subject type#
Road vehicles (automated and human)
Scenario type#
Intersecting predicted paths for a significant time span in the scenario
Inputs#
\(a_{\mathit{long,req}}\), \(a_{\mathit{long,min}}\) probability distribution
Output scale#
\([0,1]\), probability, ratio scale
Reliability#
Depends on reliability of \(a_{\mathit{long,req}}\), but is potentially increased due to integration over time
Validity#
Comparable to BTN, potentially increased due to comparison with a normally distributed target value, but depends on validity of distribution [Guido2011], initially validated [Cunto2008]
Sensitivity#
Potentially high, but strongly depends on \(a_{\mathit{long,req}}\) and validity of \(a_{\mathit{long,min}}\) distribution for the given scenario
Specificity#
Potentially high, but strongly depends on \(a_{\mathit{long,req}}\) and validity of \(a_{\mathit{long,min}}\) distribution for the given scenario
Prediction model#
Time window#
Unbound, but usefulness depends on DMM
Time mode#
Linear time