OBJECTIVE Safety validation of autonomous driving systems (ADS) is constrained by the rarity of safety-critical interactions in naturalistic driving and the difficulty of generating realistic multi-agent trajectories from reconstructed crash data. This study focuses on a common urban safety-critical scenario in which t...
Safety-critical driving scenario generation has largely focused on manipulating the behavior of surrounding agents while starting from an initial scene from driving data. This assumption can limit the space of discoverable failures, since driving data can provide little opportunity for meaningful interaction. For examp...
The development of Maritime Autonomous Surface Ships (MASS) represents a transformative shift in maritime transportation, where digital testing systems play a pivotal role in evaluating autonomous behaviors under complex and safety critical conditions. Such systems, incorporating software simulation, scenario libraries...
The generation of safety-critical traffic scenarios is essential for training and evaluating autonomous vehicles. Prior approaches typically perturb the trajectories of existing agents in a traffic scenario using simplified adversarial objectives to induce safety-critical interactions, which can limit the plausibility...
The results demonstrate the value of behavior-centered benchmarking and establish SIREN as an extensible data-generation and evaluation platform for autonomous-driving and transportation safety research.
Generating corner-case scenarios with appropriate adversity in a simulation environment is critical for testing an autonomous vehicle (AV) software stack's safety performance before deployment. Existing autonomous-driving scenario generators can enforce specific behavior, adversity, or feasibility conditions, but they...
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