arXiv:2606.17362v1 Announce Type: cross Abstract: Autonomous driving has shifted towards end-to-end policy learning, where reliable, interpretable policy evaluation is a fundamental challenge as driving quality is highly context-dependent. Commonly used rule-based driving metrics like EPDMS are interpretable but lack context-awareness, while recent VLMbased evaluations are context-aware but limited by ambiguous VLM outputs and weak physical grounding. To evaluate driving in a manner that is both interpretable and context-aware, we introduce DriveJudge. DriveJudge is a driving evaluation agent
The shift towards end-to-end policy learning in autonomous driving necessitates more sophisticated and interpretable evaluation methods, which traditional metrics lack.
Reliable and context-aware evaluation is critical for the development, deployment, and public acceptance of autonomous driving systems, impacting safety and regulatory frameworks.
The introduction of DriveJudge, a VLM-based agent for evaluating autonomous driving, signifies a move towards more nuanced and interpretable assessment beyond rule-based metrics.
- · Autonomous driving developers
- · AI safety researchers
- · Regulators of autonomous vehicles
- · Vision-language model developers
- · Developers relying solely on rule-based metrics
- · Traditional driving evaluation methods
Improved reliability and safety assessments for autonomous vehicles will accelerate their development and market readiness.
Enhanced evaluation methodologies could lead to new certification standards for self-driving cars, influencing market barriers to entry.
More explainable AI in autonomous driving may increase public trust and accelerate wider adoption of autonomous transportation systems.
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