Potential Functions as Superposition of Scoring Functions (PF)¶
Description¶
The general concept of the PF metric is to define a potential function for each static or dynamic object considered by the metric [Wolf2018]. This includes potentials for lane markings, the road geometry, other vehicles, or, in more urban areas, pedestrians and bicyclists. Once a potential function for each object in the scene, denoted by \(U_i(A, S)\), is chosen, one can apply e.g. gradient descent for a given scene \(S\) to the combined potential function \(U(A, S) = U_1(A, S) + \dots + U_k(A, S)\), where \(A\) is an actor and \(k\) denotes the number of objects. A simple example of how to evaluate this metric for an actor \(A_1\) and a given scene \(S'\) is by inserting the values into \(U\), i.e.
However, methods involving the mentioned gradient descent to assess the criticality can improve precision and also provide a suggestion for criticality-reducing vehicle movement.
Due to the way this metric is defined, almost all properties depend on the specified potential functions. Furthermore, while ethical questions play a role when defining any safety surrogate, it becomes more evident for potential functions, as an active decision making in the definition of the potentials is required.
Properties¶
Run-time capability¶
Yes
Target values¶
None found, also highly dependent on the used potential functions
Subject type¶
Any, but requires a potential function for each considered subject type
Scenario type¶
Depends on specified potential functions
Inputs¶
Potential function for each static/dynamic object in the scene that is supposed to be considered, other inputs depend entirely on said potential functions
Output scale¶
\([-\infty, \infty]\), number (negative values are possible if goal locations are defined), ratio scale
Reliability¶
Largely depends on the used potential functions
Validity¶
Largely depends on the used potential functions; no empirical analysis identified
Sensitivity¶
Largely depends on the used potential functions
Specificity¶
Largely depends on the used potential functions
Prediction model¶
Time window¶
Depends on quality of potential functions and reliability of computation of the solution to the potential equation problem
Time mode¶
Branching time