Deep Learning System Boundary Testing through Latent Space Style Mixing

Evaluating the behavioral frontier of deep learning (DL) systems is crucial for understanding their generalizability and robustness. However, boundary testing is challenging due to their high-dimensional input space. Generative artificial intelligence offers a promising solution by modeling data distribution within compact latent space representations, thereby facilitating finer-grained explorations. In this work, we introduce MIMICRY, a novel black-box system-agnostic test generator that leverages these latent representations to generate frontier inputs for the DL systems under test. Specifically, MIMICRY uses style-based generative adversarial networks trained to learn the representation of inputs with disentangled features. This representation enables embedding style-mixing operations between a source and a target input, combining their features to explore the boundary between them. We evaluated the effectiveness of different MIMICRY configurations in generating boundary inputs for four popular DL image classification systems. Our results show that manipulating the latent space allows for effective and efficient exploration of behavioral frontiers. As opposed to a model-based baseline, MIMICRY generates a higher quality frontier of behaviors which includes more and closer inputs. Additionally, we assessed the validity of these inputs, revealing a high validity rate according to human assessors.

System Safety Monitoring of Learned Components Using Temporal Metric Forecasting

In learning-enabled autonomous systems, safety monitoring of learned components is crucial to ensure their outputs do not lead to system safety violations, given the operational context of the system. However, developing a safety monitor for practical deployment in real-world applications is challenging. This is due to limited access to internal workings and training data of the learned component. Furthermore, safety monitors should predict safety violations with low latency, while consuming a reasonable amount of computation. To address the challenges, we propose a safety monitoring method based on probabilistic time series forecasting. Given the learned component outputs and an operational context, we empirically investigate different Deep Learning (DL)-based probabilistic forecasting to predict the objective measure capturing the satisfaction or violation of a safety requirement (safety metric). We empirically evaluate safety metric and violation prediction accuracy, and inference latency and resource usage of four state-of-the-art models, with varying horizons, using an autonomous aviation case study. Our results suggest that probabilistic forecasting of safety metrics, given learned component outputs and scenarios, is effective for safety monitoring. Furthermore, for the autonomous aviation case study, Temporal Fusion Transformer (TFT) was the most accurate model for predicting imminent safety violations, with acceptable latency and resource consumption.

Predicting Safety Misbehaviours in Autonomous Driving Systems using Uncertainty Quantification

The automated real-time recognition of unexpected situations plays a crucial role in the safety of autonomous vehicles, especially in unsupported and unpredictable scenarios. This paper evaluates different Bayesian uncertainty quantification methods from the deep learning domain for the anticipatory testing of safety-critical misbehaviours during system-level simulation-based testing. Specifically, we compute uncertainty scores as the vehicle executes, following the intuition that high uncertainty scores are indicative of unsupported runtime conditions that can be used to distinguish safe from failure-inducing driving behaviors. In our study, we conducted an evaluation of the effectiveness and computational overhead associated with two Bayesian uncertainty quantification methods, namely MC- Dropout and Deep Ensembles, for misbehaviour avoidance. Overall, for three benchmarks from the Udacity simulator comprising both out-of-distribution and unsafe conditions introduced via mutation testing, both methods successfully detected a high number of out-of-bounds episodes providing early warnings several seconds in advance, outperforming two state-of-the-art misbehaviour prediction methods based on autoencoders and attention maps in terms of effectiveness and efficiency. Notably, Deep Ensembles detected most misbehaviours without any false alarms and did so even when employing a relatively small number of models, making them computationally feasible for real-time detection. Our findings suggest that incorporating uncertainty quantification methods is a viable approach for building fail-safe mechanisms in deep neural network-based autonomous vehicles.

Two is Better Than One: Digital Siblings to Improve Autonomous Driving Testing

Simulation-based testing represents an important step to ensure the reliability of autonomous driving software. In practice, when companies rely on third-party general-purpose simulators, either for in-house or outsourced testing, the generalizability of testing results to real autonomous vehicles is at stake. In this paper, we strengthen simulation-based testing by introducing the notion of digital siblings, a novel framework in which the AV is tested on multiple general-purpose simulators, built with different technologies. First, test cases are automatically generated for each individual simulator. Then, tests are migrated between simulators, using feature maps to characterize of the exercised driving conditions. Finally, the joint predicted failure probability is computed and a failure is reported only in cases of agreement among the siblings. We implemented our framework using two open-source simulators and we empirically compared it against a digital twin of a physical scaled autonomous vehicle on a large set of test cases. Our study shows that the ensemble failure predictor by the digital siblings is superior to each individual simulator at predicting the failures of the digital twin. We discuss several ways in which our framework can help researchers interested in automated testing of autonomous driving software.

Mind the Gap! A Study on the Transferability of Virtual vs Physical-world Testing of Autonomous Driving Systems

Safe deployment of self-driving cars (SDC) necessitates thorough simulated and in-field testing. Most testing techniques consider virtualized SDCs within a simulation environment, whereas less effort has been directed towards assessing whether such techniques transfer to and are effective with a physical real-world vehicle. In this paper, we leverage the Donkey Car open-source framework to empirically compare testing of SDCs when deployed on a physical small-scale vehicle vs its virtual simulated counterpart. In our empirical study, we investigate the transferability of behavior and failure exposure between virtual and real-world environments on a vast set of corrupted and adversarial settings. While a large number of testing results do transfer between virtual and physical environments, we also identified critical shortcomings that contribute to the reality gap between the virtual and physical world, threatening the potential of existing testing solutions when applied to physical SDCs.

Taxonomy of Real Faults in Deep Learning Systems

The growing application of deep neural networks in safety-critical domains makes the analysis of faults that occur in such systems of enormous importance. In this paper we introduce a large taxonomy of faults in deep learning (DL) systems. We have manually analysed 1059 artefacts gathered from GitHub commits and issues of projects that use the most popular DL frameworks (TensorFlow, Keras and PyTorch) and from related Stack Overflow posts. Structured interviews with 20 researchers and practitioners describing the problems they have encountered in their experience have enriched our taxonomy with a variety of additional faults that did not emerge from the other two sources. Our final taxonomy was validated with a survey involving an additional set of 21 developers, confirming that almost all fault categories (13/15) were experienced by at least 50% of the survey participants.