Autonomous Vehicle Safety & Simulation
Quantifying and Bridging the Fidelity Gap: A Decisive-Feature Approach to Comparing Synthetic and Real Imagery
Danial Safaei, Siddartha Khastgir, Mohsen Alirezaei, Jeroen Ploeg, Son Tong, Xingyu Zhao
WMG, University of Warwick & Siemens Digital Industries Software
This analysis explores a critical advancement in autonomous vehicle safety assurance: Decisive Feature Fidelity (DFF). Traditional simulation fidelity metrics often fail to ensure reliable transfer from virtual testing to the real world, as they overlook whether the System-Under-Test (SUT) bases its decisions on the same causal evidence in both environments. DFF addresses this by using explainable AI to compare the decisive features influencing SUT outputs for matched real-synthetic pairs.
Executive Impact & Key Findings
DFF provides a behavior-grounded fidelity measure, crucial for high-stakes autonomous systems. By quantifying and bridging the "fidelity gap," it ensures that AVs learn and operate from consistent causal evidence, significantly enhancing trust and safety in virtual testing.
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Fidelity Threshold for Calibration Set
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Matched Real-Synthetic Pairs Evaluated
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Diverse SUTs Demonstrated
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Output Value Improvement (OVF-DA)
Deep Analysis & Enterprise Applications
Select a topic to dive deeper, then explore the specific findings from the research, rebuilt as interactive, enterprise-focused modules.
The Fidelity Gap
DFF Methodology
DFF vs. Traditional Metrics
Calibration Impact
Mechanism Parity
The critical factor for reliable AV simulation, often missed by pixel-level metrics.
Recent studies reveal that pixel-level fidelity alone does not ensure reliable transfer from simulation to the real world. What truly matters is whether the System-Under-Test (SUT) bases its decisions on the same causal evidence in both real and simulated environments—not just whether images "look real" to humans.
Enterprise Process Flow: DFF Methodology
Paired Real & Synthetic Inputs
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XAI Extracts Decisive Features (SUT-specific)
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Compare Decisive Feature Maps (DFF Estimate)
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Feedback to Simulator Calibrator
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Adjust Generator Parameters
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Close Mechanism Gaps
| Notion | Comparison Target | SUT-Specific? | Blind spot |
|---|---|---|---|
| IV (Input Value) Fidelity | pixels/appearance | No | unrobust F |
| OV (Output Value) Fidelity | task outputs | Yes | spurious evidence |
| LF (Latent Feature) Fidelity | broad internals | Yes | non-decisive channels |
| DFF (Decisive Feature Fidelity) | decisive features | Yes | unrobust XAI (mitigated) |
DFF-Guided Calibration: A Case Study in Preventing Misleading Fidelity
Optimizing for output value alone can mask critical mechanism shifts, as demonstrated by the YOLOP Drivable Area (DA) System Under Test (SUT) from our research.
Scenario: When calibrated only for Output Value (OVF-DA), the system's OV loss improved by 74%, indicating more similar outputs. However, the Decisive Feature Fidelity (DFF) simultaneously increased by 16%, revealing that the SUT achieved this output similarity based on divergent decisive features.
Outcome: This highlights a critical failure mode: achieving the 'right' output for the 'wrong' reasons. DFF-guided calibration explicitly prevents such misleading scenarios by aligning the underlying causal evidence, leading to more robust and reliable AV testing results.
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