Validity Analysis of Simulation-based Testing concerning Free-space Detection in Autonomous Driving

Automated vehicles must perceive their environment and accordingly plan a safe trajectory to navigate. Camera sensors and image processing algorithms have been extensively used to detect free-space, which is an unoccupied area where a car can safely drive through. To reduce the effort and costs of real test drives, simulation has been increasingly used in the automotive industry to test such systems. In this work, an algorithm for free-space detection is evaluated across real and virtual domains under different environment conditions: daytime, night time and fog. For this purpose, an algorithm is implemented to ease the process of creating ground-truth data for this kind of test. Based on the evaluation of predictions against ground-truth, the test results from the real test scenario are compared with its corresponding virtual twin to analyze the validity of simulation-based testing of a free-space detection algorithm.