Exposure-Centric Robustness Analysis of Automated Driving Systems in Adversarial Multi-Agent Environments

Assessing the security and safety robustness of Automated Driving Systems (ADS) requires more than simulation mileage: rare semantic combinations of surrounding-agent behaviors and environmental context create blind spots that expose brittle planning decisions. We propose SPEC , an exposure metric characterizing how extensively an ADS's planning response is exercised across vehicle state, environment, and interaction criticality. SPEC is computed over a unified per-scene abstraction, HSR (Holistic Scene Representation) , a digital fingerprint for forensic analysis of behaviors and failure patterns. Across over one million scenario runs, HSR yields more consistent behavioral partitions and SPEC correlates strongly with the discovery of distinct failure manifestations. To operationalize exposure discovery, we introduce COAX ( C ollective O rchestration of A gents for e X ploration), an automated adversarial orchestrator that adaptively coordinates surrounding agents to steer the ADS into interaction regimes whose HSR fingerprints are absent from the accumulated corpus, thereby broadening SPEC-measured exposure. Under the same simulation budget, COAX uncovers up to 556% more distinct failure manifestations than baselines across multiple ADS platforms; fine-tuning downstream agents on the corpora it generates reduces collision rates by up to 75–78%. Together, SPEC and COAX provide a scalable framework for robustness assessment and vulnerability-oriented exposure discovery in ADS planning under multi-agent interaction.