Introduction

As the automotive industry moves toward higher levels of automation, the integration of Convolutional Neural Networks (CNNs) in perception systems has become critical. CNN-based soft Intellectual Property (IP) is widely used in Advanced Driver Assistance Systems (ADAS) and Autonomous Vehicles (AVs) for real-time image and sensor data processing. However, ensuring functional safety compliance for CNN Soft IP presents unique challenges, given the black-box nature of neural networks and their probabilistic behavior.

A safety case provides a structured argument, supported by evidence, demonstrating that a CNN-based soft IP meets the required safety standards. This blog explores key aspects of a safety case for CNN Soft IP, its challenges, and strategies to ensure compliance with ISO 26262 and ISO 21448 (SOTIF).

Key Challenges in CNN Soft IP Safety

1. Lack of Deterministic Behavior: Unlike traditional software, CNNs exhibit non-deterministic behavior due to probabilistic learning.
2. Data Dependency: CNN performance heavily depends on training data quality and coverage.
3. Explainability & Traceability: Understanding failure modes and providing justifiable safety arguments is difficult.
4. Verification Complexity: Traditional verification methods like MC/DC coverage are less applicable to CNN models.
5. Error Propagation: Incorrect CNN outputs can impact downstream decision-making systems.

Components of a CNN Soft IP Safety Case

A robust safety case for CNN Soft IP should include the following components:

1. Context & Scope Definition

• Define the role of CNN Soft IP within the safety-critical system.
• Identify safety goals and ASIL (Automotive Safety Integrity Level) classification.
• Clarify the operating environment and interaction with other system components.

2. Safety Mechanisms & Mitigations

• Redundancy & Diversity: Implement multiple models or sensors to cross-validate CNN outputs.
• Confidence Estimation: Use uncertainty quantification to assess model reliability.
• Fail-Safe Design: Define fallback strategies when CNN outputs are uncertain.
• Robust Training Data: Ensure diverse datasets covering edge cases and corner scenarios.
• Runtime Monitoring: Integrate anomaly detection and real-time verification mechanisms.

3. Verification & Validation Strategy

• Functional Verification: Develop safety-driven test cases covering edge cases.
• Coverage Metrics: Use neuron coverage, decision coverage, and adversarial testing.
• Fault Injection Testing: Simulate CNN misclassifications and assess system response.
• SOTIF Compliance: Ensure safety even in the absence of hardware faults.
• Explainability & Interpretability: Leverage tools like SHAP, LIME, or saliency maps for decision justification.

4. Safety Analysis Techniques

• FMEDA (Failure Modes, Effects, and Diagnostic Analysis): Assess potential failure modes and their impact.
• DFA (Dependent Failure Analysis): Evaluate dependencies between failures in the system.
• Systematic FMEA (Failure Modes and Effects Analysis): Identify systematic failure mechanisms and mitigation strategies.

5. Safety Evidence & Compliance Argumentation

• Provide traceability between safety requirements, architecture, and test results.
• Document CNN limitations and residual risks, with justifications.
• Ensure compliance with ISO 26262 Part 6 (Software Development) and Part 11 (AI & Machine Learning Guidelines).

Conclusion

A CNN-based soft IP must be developed and verified with a structured safety case to ensure compliance with functional safety standards. By addressing the non-deterministic nature, data dependency, and traceability concerns, organizations can build reliable, safety-compliant AI-driven perception systems for automotive applications.

For organizations working on CNN Soft IP, establishing a comprehensive safety case early in the development cycle is crucial to achieving regulatory approval, reducing safety risks, and ensuring trust in AI-powered systems.