ICST 2024
Mon 27 - Fri 31 May 2024 Canada
Wed 29 May 2024 16:10 - 16:30 at Room 2 & 3 - Testing Autonomous Driving Systems Chair(s): Nargiz Humbatova

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 misbehaviour 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 created in 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.

Wed 29 May

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15:30 - 17:00
Testing Autonomous Driving SystemsResearch Papers / Testing Tools and Demonstration at Room 2 & 3
Chair(s): Nargiz Humbatova USI Lugano
15:30
20m
Research paper
Adversarial Testing with Reinforcement Learning: A Case Study on Autonomous Driving
Research Papers
Andréa Doreste , Matteo Biagiola Università della Svizzera italiana, Paolo Tonella USI Lugano
15:50
20m
Research paper
Assessing Quality Metrics for Neural Reality Gap Input Mitigation in Autonomous Driving Testing
Research Papers
Stefano Carlo Lambertenghi Technische Universität München, fortiss GmbH, Andrea Stocco Technical University of Munich, fortiss
Pre-print
16:10
20m
Research paper
Predicting Safety Misbehaviours in Autonomous Driving Systems using Uncertainty Quantification
Research Papers
Ruben Grewal , Paolo Tonella USI Lugano, Andrea Stocco Technical University of Munich, fortiss
Pre-print
16:30
20m
Research paper
AURORA: Navigating UI Tarpits via Automated Neural Screen Understanding
Research Papers
Safwat Ali Khan George Mason University, Wenyu Wang University of Illinois Urbana-Champaign, Yiran Ren , Bin Zhu , Jiangfan Shi , Wing Lam George Mason University, Kevin Moran University of Central Florida
Pre-print
16:50
10m
Demonstration
U-Fuzz: A Tool for Stateful Fuzzing of IoT Protocols on COTS Devices
Testing Tools and Demonstration
Shang Zewen , Matheus Eduardo Garbelini , Sudipta Chattopadhyay Singapore University of Technology and Design