Safety Concerns and Mitigation Approaches Regarding the Use of Deep Learning in Safety-Critical Perception Tasks (2001.08001v1)

Abstract: Deep learning methods are widely regarded as indispensable when it comes to designing perception pipelines for autonomous agents such as robots, drones or automated vehicles. The main reasons, however, for deep learning not being used for autonomous agents at large scale already are safety concerns. Deep learning approaches typically exhibit a black-box behavior which makes it hard for them to be evaluated with respect to safety-critical aspects. While there have been some work on safety in deep learning, most papers typically focus on high-level safety concerns. In this work, we seek to dive into the safety concerns of deep learning methods and present a concise enumeration on a deeply technical level. Additionally, we present extensive discussions on possible mitigation methods and give an outlook regarding what mitigation methods are still missing in order to facilitate an argumentation for the safety of a deep learning method.

• The paper identifies that DNN safety in perception is challenged by data mismatches, black-box behavior, and adversarial vulnerabilities.
• The paper proposes mitigation strategies such as comprehensive data acquisition, calibration of confidence outputs, and adversarial training techniques.
• The paper emphasizes continuous learning and the development of standardization frameworks to address evolving real-world conditions.

Analysis of Safety Concerns and Mitigation Approaches in Deep Learning for Safety-Critical Perception Tasks

The paper "Safety Concerns and Mitigation Approaches Regarding the Use of Deep Learning in Safety-Critical Perception Tasks" addresses critical issues in the adoption of deep learning (DL) models for autonomous systems. The use of deep neural networks (DNNs) in perception pipelines for automated driving (AD) or advanced driver-assistance systems (ADAS) is becoming increasingly essential due to their ability to handle ill-specified problems. However, safety concerns remain a significant hindrance to their widespread deployment.

Key Safety Concerns

The document methodically enumerates the safety concerns inherent in deploying DNNs in safety-critical applications:

1. Data Distribution Concerns: One primary issue is the mismatch between training data distributions and real-world scenarios. This is exacerbated by the non-intuitive nature of DL models which operate on data representations rather than semantic content. Furthermore, temporal distribution shifts pose challenges as real-world conditions evolve post-deployment.
2. Black-Box Nature: The incomprehensible behavior of DNNs, often referred to as their "black-box" characteristic, complicates the safety validation process. The inability to explain decisions or derive causal relations from high-dimensional feature spaces is a significant barrier.
3. Robustness and Reliability: The brittleness of DNNs to adversarial perturbations and novel scenarios is a recognized safety risk, alongside the challenge of generating reliable confidence estimates which are crucial for decision-making in complex dynamic environments.
4. Testing and Validation Challenges: Traditional data partitioning and metric evaluation methods may not adequately capture safety requirements, particularly in AD applications where scenario complexity and variability are extremely high.

Mitigation Strategies

The paper outlines a range of mitigation strategies designed to address these concerns:

• Data Acquisition and Strategy: Establishing a justified strategy to ensure data comprehensively reflects operational design domains (ODDs) is essential. This approach is supported by leveraging techniques like variational autoencoders to bridge gaps in data representativeness.
• Reliable Confidence Output: Calibration of DNN outputs to produce reliable confidence measures can enable more informed safety argumentation and integration with parallel safety systems. Bayesian techniques such as Monte Carlo Dropout are suggested to model and mitigate inherent uncertainties.
• Dealing with Adversarial Robustness: Specification of threat models and incorporation of robust defense mechanisms, such as adversarial training and convex relaxation techniques, are proposed to enhance DNN resilience.
• Iterative Testing and Analysis: Continuous iterative testing and analysis in development allow for ongoing identification and rectification of weaknesses, supplemented by targeted field tests. The use of tailored evaluation metrics that account for specific safety-critical outcomes, rather than average performance, is emphasized.
• Continuous Learning and Updating: Given the persistence of open-world and temporal distribution challenges, an ongoing learning framework is recommended whereby DNN updates are informed by field data to accommodate new environmental variations.

Implications and Future Direction

The implications of these findings resonate across both theoretical and practical domains. The complexity inherent in aligning data and real-world distributions challenges conventional validation paradigms and necessitates a multi-faceted approach to safety assurance. The paper suggests that until comprehensive standardization frameworks are developed, the judgment on adequate safety mitigation remains largely empirical and domain-specific.

In conclusion, as DL methodologies become further ingrained in safety-critical systems, the ability to transparently argue their safety while addressing known vulnerabilities will remain a decisive factor in their adoption. The comprehensive enumeration of safety concerns and mitigation approaches presented serves as a foundation for advancing safe and reliable AI-driven autonomous systems. Future efforts should focus on refining these mitigation techniques and embedding them within standardized safety frameworks.