UC1_R_1

Road users counting accuracy should be at least 99%

Requirement ID

Requirement Textual Description

Requirement Type

Requirement Test Cases

Requirement Test Cases List

VALU3S Framework

Evaluation Environment Type Type of environment where the method is applied. It can be more than one type of environment.(Dimension 1)

Evaluation Type Type of evaluation performed with that method. If a method is hybrid, more than one type can be selected.(Dimension 2)

Type of Component Under Evaluation Type of components that can be evaluated with that method. It can be more than one type.(Dimension 3)

Evaluation Stage The evaluation stage where the method is used.(Dimension 5)

Purpose of the Component Under Evaluation The type of purpose that can be evaluated by a method. It can be more than one type.(Dimension 6)

Type of Requirement Under Evaluation The type of requirements that a method is able to evaluate. It can be more than one type.(Dimension 7)

Evaluation Performance Indicator The type of evaluation criteria or KPI that a method is able to improve. It can be more than one type.(Dimension 8)

Contents

UC1_TC_1

* Preconditions: Radar detection + tracking algorithm (including ML based postprocessing/classification) together with video detection + recognition algorithm shall achieve 99% vehicle counting accuracy and speed measurement error within 2kph/2% (bigger of) when at most Y% rain/fog is added to the scene * Input conditions / steps: images from simulator/real-world with the precipitation rate Y including corresponding radar data * Expected result: The system detects and counts the object in the radar data (based on point cloud), or video images (based on LP detection), or their combination correctly, meeting the expected accuracy