← Back to all news
arXiv AI

Coverage-Driven Verification for Safety-by-Design in AI-Based Collision Avoidance Systems

The paper proposes a structured method for assessing the representativeness of Operational Design Domains (ODDs) in AI/ML-based aviation systems, focusing on safety assurance. It outlines a process flow from ODD definition to quantitative evaluation, recommending Kullback–Leibler divergence and Cramér’s V over chi‑squared tests for large datasets. The approach is illustrated with AI-based collision avoidance simulations, demonstrating how statistical distribution comparisons can support safety‑by‑design engineering aligned with EASA guidance.

Related stories

arXiv AI
Sep 3

From High-Dimensional Spaces to Verifiable ODD Coverage for Safety-Critical AI-based Systems

The paper proposes a structured method for verifying Operational Design Domain (ODD) coverage in safety‑critical AI systems, particularly for aviation. It combines parameter discretization, constraint‑based filtering, and criticality‑based dimension reduction to create a multi‑step verification process. Using simulation data from AI‑based mid‑air collision avoidance research, the authors demonstrate how this approach can meet EASA’s requirement for complete ODD coverage in high‑dimensional spaces.

By Thomas Stefani, Johann Maximilian Christensen, Elena Hoemann, Frank K\"oster, Sven Hallerbach
safety
More like this →
arXiv AI
Jun 17

Surrogate Assisted Pedestrian Protection Design via a Foundation Model Orchestrated Workflow

arXiv:2606. 17577v1 Announce Type: new Abstract: AI-driven engineering workflows face particular challenges in crash safety design: unlike aerodynamics, crash events involve highly nonlinear contact dynamics, material nonlinearity, and discrete state transitions that are difficult to capture with data-driven surrogate models.

By Osamu Ito, Akihiko Katagiri, Yoshikazu Nakagawa, Shin Saeki, Jun Shiraishi, Masato Sasaki
llmssafety
More like this →
arXiv Machine Learning
Sep 3

PRISM: An Agentic Multi-Model Architecture for Proactive Safety in Autonomous Transportation Systems

PRISM (Proactive Risk Intelligence and Safety Management) is an agentic multi-model architecture designed to shift autonomous transportation safety from reactive crash avoidance to proactive, continuous risk management. It uses inverse crash‑probability modeling to transform binary crash classifiers into dynamic safety scores, and runs three specialized models—trajectory kinematics, environmental risk, and VRU interaction—coordinated by a reinforcement‑learning reasoning layer. Across 1,296 naturalistic driving scenarios, PRISM achieved a mean safety score of 68/100, classified 77.6% of situations as advisory, and flagged 3.8% as near‑misses, with 11% requiring intervention or emergency response, highlighting trajectory risk and VRU proximity as key safety factors.

By Joyjit Roy, Samaresh Kumar Singh, Sushanta Das
agentsreinforcement-learningsafety
More like this →
arXiv AI
Aug 18

AeroCopilotBench: A Two-Tier Benchmark for Evaluating LLM Agents as Aviation Copilots in an Interactive Virtual Cockpit Environment

arXiv:2608. 16349v1 Announce Type: new Abstract: Large language model (LLM) agents may assist flight crews with complex decisions and task execution, but existing aviation evaluations centered on static knowledge do not support systematic testing of procedural execution and safety compliance in interactive environments.

By Yuchen Yuan, Zhenghuang Wu, Yuangan Li, Liang Ma, Ke Li
llmsagentsbenchmarkssafety
More like this →