Today's automotive cyber-physical systems for autonomous driving aim to enhance driving safety by replacing the uncertainties posed by human drivers with standard procedures of automated systems. However, the accuracy of in-vehicle perception systems may significantly vary under different operational conditions (e.g., fog density, light condition, etc.) and consequently degrade the reliability of autonomous driving. A perception system for autonomous driving must be carefully validated with an extremely large dataset collected under all possible operational conditions in order to ensure its robustness. The aforementioned dataset required for validation, however, is expensive or even impossible to acquire in practice, since most operational corners rarely occur in a real-world environment. In this paper, we propose to generate synthetic datasets at a variety of operational corners by using a parameterized cycle-consistent generative adversarial network (PCGAN) . The proposed PCGAN is able to learn from an image dataset recorded at real-world operational conditions with only a few samples at corners and synthesize a large dataset at a given operational corner. By taking STOP sign detection as an example, our numerical experiments demonstrate that the proposed approach is able to generate high-quality synthetic datasets to facilitate accurate validation.

The use of synthetic (or simulated) data for training machine learning models has grown rapidly in recent years. Synthetic data can often be generated much faster and more cheaply than its real-world counterpart. One challenge of using synthetic imagery however is scene design: e.g., the choice of content and its features and spatial arrangement. To be effective, this design must not only be realistic, but appropriate for the target domain, which (by assumption) is unlabeled. In this work, we propose an approach to automatically choose the design of synthetic imagery based upon unlabeled real-world imagery. Our approach, termed Neural-Adjoint Meta-Simulation (NAMS), builds upon the seminal recent meta-simulation approaches. In contrast to the current state-of-the-art methods, our approach can be pre-trained once offline, and then provides fast design inference for new target imagery. Using both synthetic and real-world problems, we show that NAMS infers synthetic designs that match both the in-domain and out-of-domain target imagery, and that training segmentation models with NAMS-designed imagery yields superior results compared to na\"ive randomized designs and state-of-the-art meta-simulation methods.

Today’s automotive vehicles are often equipped with powerful data processing systems for driver assistance and/or autonomous driving. To meet the rigorous safety standard, ensuring extremely small failure probability over all possible operation conditions is one of the critical tasks for an autonomous driving system. In this chapter, we describe a novel methodology to validate vision-based autonomous driving systems over different circuit corners with consideration of temperature variation and circuit aging. Our approach seamlessly integrates the image data recorded under nominal conditions with comprehensive statistical circuit models to synthetically generate the critical corner cases for which an autonomous driving system is likely to fail. As such, a given automotive system can be robustly validated for these worst-case scenarios that cannot be easily captured by physical experiments. To efficiently …

Today's automotive vehicles are often equipped with powerful data processing systems for driver assistance and/or autonomous driving. To meet the rigorous safety standard, one critical task is to ensure extremely small failure rate over all possible operation conditions. Such a validation task requires a large amount of on-road testing data to cover all possible corners. In this paper, we describe a novel general-purpose methodology to synthetically and efficiently generate a broad spectrum of corner cases for validation purpose. Our proposed method is based upon cycle-consistent generative adversarial networks (CycleGANs) trained by a small set of image samples to mathematically map a nominal case to other corner cases. By taking STOP sign detection as an example, our numerical experiments demonstrate that the proposed approach is able to reduce the validation error by up to 100× given a limited data set for corner cases.

In this paper, a novel Asymptotic Probability Approximation (APA) method is proposed to estimate the overall rare probability of correlated failure events for complex circuits containing a large number of replicated cells (e.g., SRAM bit-cells). The key idea of APA is to approximate the overall circuit failure rate based on a set of carefully defined failure events. An efficient Hierarchal Subset Simulation (H-SUS) method is developed to calculate the aforementioned failure rate and a statistical methodology is further proposed to estimate the confidence interval of APA. Our numerical experiments demonstrate that APA can accurately and reliably estimates the overall failure rate of correlated rare failure events involving more than 20,000 independent random variables.

We describe a novel methodology to validate vision-based autonomous driving systems over different circuit corners with consideration of temperature variation and circuit aging. The proposed work is motivated by the fact that low-level circuit implementation may have a significant impact on system performance, even though such effects have not been appropriately taken into account today. Our approach seamlessly integrates the image data recorded under nominal conditions with comprehensive statistical circuit models to synthetically generate the critical corner cases for which an autonomous driving system is likely to fail. As such, a given automotive system can be robustly validated for these worst-case scenarios that cannot be easily captured by physical experiments.

Efficient performance modeling is an extremely important task for yield analysis and design optimization of analog circuits. In this paper, a novel regression modeling method based on hierarchical shrinkage priors is proposed to construct hybrid performance models with both high accuracy and low computational cost. In particular, the user-defined model templates derived from design equations and the general-purpose orthogonal polynomials are combined together to set up a hybrid dictionary. Next, in order to avoid over-shrinking large model coefficients, a novel regression method based on hierarchical shrinkage priors and variational Bayesian inference is adopted for model fitting. A rail-to-rail operational amplifier example demonstrates that the proposed method achieves up to 40% error reduction over other state-of-the-art approaches without increasing the modeling cost.