Abstract:

Testing performances of camera in adverse weather conditions （AWCs） is significant for improving intelligent vehicles adaptability. However， when using digital simulation to test cameras， there is the problem of poor fidelity， while physical testing suffers from high cost， long cycles， and difficulty in accurately and controllably reproducing scenarios. Therefor， in this paper， adverse weather condition Digital-Physical Fusion Simulation Camera-in-the-loop Simulation Test （DPF-CIL） method is proposed. The camera is embedded into a digital-physical fusion test environment composed of digital simulation targets and physical weather entities. Firstly， DPF-CIL platform is designed and constructed. Secondly， multi-level fidelity evaluation method for CIL platform is established at pixel， feature， and result level. The results show that when testing in AWCs such as rain， fog， and glare， the DPF-CIL is able to simulate the minimum Structural Similarity Index and Peak Signal-to-Noise Ratio of 0.571 1 and 27.991 1 dB， respectively， and retains the target object's contour information and the real environment with the maximum gap of 88 pixels. In addition， when testing the target recognition and ranging functions， the results vary the most at 10.10% and 13.39%， respectively. The comprehensive multi-level fidelity evaluation results are better than the pure digital simulation method under the same scenario conditions. Compared to physical testing DPF-CIL has advantages of lower cost， higher efficiency， and precise control over AWCs parameters.

Key words: intelligent vehicle, camera-in-the-loop test, adverse weather condition digital-physical fusion simulation, multi-level fidelity evaluation