Hierarchical-level rain image generative model based on GAN

Autonomous vehicles are exposed to various weather during operation, which is likely to trigger the performance limitations of the perception system, leading to the safety of the intended functionality (SOTIF) problems. To efficiently generate data for testing the performance of visual perception algorithms under various weather conditions, a hierarchical-level rain image generative model, rain conditional CycleGAN (RCCycleGAN), is constructed. RCCycleGAN is based on the generative adversarial network (GAN) and can generate images of light, medium, and heavy rain. Different rain intensities are introduced as labels in conditional GAN (CGAN). Meanwhile, the model structure is optimized and the training strategy is adjusted to alleviate the problem of mode collapse. In addition, natural rain images of different intensities are collected and processed for model training and validation. Compared with the two baseline models, CycleGAN and DerainCycleGAN, the peak signal-to-noise ratio (PSNR) of RCCycleGAN on the test dataset is improved by 2.58 dB and 0.74 dB, and the structural similarity (SSIM) is improved by 18% and 8%, respectively. The ablation experiments are also carried out to validate the effectiveness of the model tuning.

An Ontology-based Method to Identify Triggering Conditions for Perception Insufficiency of Autonomous Vehicles

The autonomous vehicle (AV) is a safety-critical system relying on complex sensors and algorithms. The AV may confront risk conditions if these sensors and algorithms misunderstand the environment and situation, even though all components are fault-free. The ISO 21448 defined the safety of the intended functionality (SOTIF), aiming to enhance the AV's safety by specifying AV's development and validation process. As required in the ISO 21448, the triggering conditions, which may lead to the vehicle's functional insufficiencies, should be analyzed and verified. However, there is not yet a method to realize a comprehensive and systematic identification of triggering conditions so far. This paper proposed an analysis framework of triggering conditions for the perception system based on the propagation chain of events model, which consists of triggering source, influenced perception stage, and triggering effect. According to the analysis framework, ontologies of triggering source and perception stage were constructed, and the relationships between concepts in ontologies are defined. According to these ontologies, triggering conditions can be generated comprehensively and systematically. The proposed method was applied on an L3 autonomous vehicle, and 20 from 87 triggering conditions identified were tested in the field, among which eight triggering conditions triggered risky behaviors of the vehicle.

LAMBDA: Covering the Solution Set of Black-Box Inequality by Search Space Quantization

Black-box functions are broadly used to model complex problems that provide no explicit information but the input and output. Despite existing studies of black-box function optimization, the solution set satisfying an inequality with a black-box function plays a more significant role than only one optimum in many practical situations. Covering as much as possible of the solution set through limited evaluations to the black-box objective function is defined as the Black-Box Coverage (BBC) problem in this paper. We formalized this problem in a sample-based search paradigm and constructed a coverage criterion with Confusion Matrix Analysis. Further, we propose LAMBDA (Latent-Action Monte-Carlo Beam Search with Density Adaption) to solve BBC problems. LAMBDA can focus around the solution set quickly by recursively partitioning the search space into accepted and rejected sub-spaces. Compared with La-MCTS, LAMBDA introduces density information to overcome the sampling bias of optimization and obtain more exploration. Benchmarking shows, LAMBDA achieved state-of-the-art performance among all baselines and was at most 33x faster to get 95% coverage than Random Search. Experiments also demonstrate that LAMBDA has a promising future in the verification of autonomous systems in virtual tests.