What Probing Reveals about Autonomous Driving: Linking Internal Prediction Errors to Ego Planning
arXiv:2606.31106v1 Announce Type: cross Abstract: Large-scale datasets and fast simulators have enabled improvements in driving policies that appear safe and robust, yet strong performance in nominal scenarios can still mask flawed reasoning and unsafe heuristics. Summary scores from closed-loop simulators do not give significant insight into the policy, making it difficult to determine whether they truly predict the motion of surrounding vehicles, how the ego vehicle generates future plans, or whether they merely rely on brittle heuristics that happen to succeed in nominal scenarios. To bette
The increasing sophistication and widespread adoption of autonomous driving systems necessitate a deeper understanding of their internal reasoning for robust, real-world deployment.
Understanding the internal mechanisms of AI-driven autonomous systems is crucial for ensuring safety, developing reliable policies, and building public trust, moving beyond mere performance scores.
The focus in autonomous driving development shifts from mere performance metrics to an interpretable understanding of policy decision-making and error sources.
- · Autonomous vehicle developers with robust explainability tools
- · Simulation and testing platforms
- · Regulatory bodies
- · Autonomous vehicle developers relying on black-box heuristics
- · Companies with opaque AI systems
Improved diagnosis and mitigation of failure modes in autonomous driving systems.
Increased investor and public confidence in the safety and reliability of autonomous vehicles leading to faster adoption.
New certification standards emerge for AI system interpretability and explainability, impacting all safety-critical AI applications.
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Read at arXiv cs.LG