Reliability Validation of Learning Enabled Vehicle Tracking
This work addresses reliability validation for learning-enabled systems in real-world applications like vehicle tracking, but it is incremental as it builds on existing testing tools.
The paper investigates how adversarial examples affect the reliability of a learning-enabled vehicle tracking system, finding that the system can be resilient due to other components but also introduces additional uncertainty not detectable by analyzing deep learning components alone.
This paper studies the reliability of a real-world learning-enabled system, which conducts dynamic vehicle tracking based on a high-resolution wide-area motion imagery input. The system consists of multiple neural network components -- to process the imagery inputs -- and multiple symbolic (Kalman filter) components -- to analyse the processed information for vehicle tracking. It is known that neural networks suffer from adversarial examples, which make them lack robustness. However, it is unclear if and how the adversarial examples over learning components can affect the overall system-level reliability. By integrating a coverage-guided neural network testing tool, DeepConcolic, with the vehicle tracking system, we found that (1) the overall system can be resilient to some adversarial examples thanks to the existence of other components, and (2) the overall system presents an extra level of uncertainty which cannot be determined by analysing the deep learning components only. This research suggests the need for novel verification and validation methods for learning-enabled systems.