Abstract
Advances in deep learning have revolutionized cyber-physical applications, including the development of autonomous vehicles. However, real-world collisions involving autonomous control of vehicles have raised significant safety concerns regarding the use of deep neural networks (DNNs) in safety-critical tasks, particularly perception. The inherent unverifiability of DNNs poses a key challenge in ensuring their safe and reliable operation. In this work, we propose perception simplex ((Formula presented.)), a fault-tolerant application architecture designed for obstacle detection and collision avoidance. We analyse an existing LiDAR-based classical obstacle detection algorithm to establish strict bounds on its capabilities and limitations. Such analysis and verification have not been possible for deep learning-based perception systems yet. By employing verifiable obstacle detection algorithms, (Formula presented.) identifies obstacle existence detection faults in the output of unverifiable DNN-based object detectors. When faults with potential collision risks are detected, appropriate corrective actions are initiated. Through extensive analysis and software-in-the-loop simulations, we demonstrate that (Formula presented.) provides deterministic fault tolerance against obstacle existence detection faults, establishing a robust safety guarantee.
Original language | English (US) |
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Article number | e1879 |
Journal | Software Testing Verification and Reliability |
Volume | 34 |
Issue number | 6 |
Early online date | May 28 2024 |
DOIs | |
State | Published - Sep 2024 |
Keywords
- autonomous vehicles
- cyber-physical systems
- fault tolerance
- obstacle detection
- software reliability
ASJC Scopus subject areas
- Software
- Safety, Risk, Reliability and Quality