arXiv (All)
open access
Injecting Hallucinations in Autonomous Vehicles: A Component-Agnostic Safety Evaluation Framework
Perception failures in autonomous vehicles (AV) remain a major safety concern because they can directly compromise decision-making and contribute to unsafe behavior. To study how these failures affect safety, researchers typically inject artificial faults into real-world or simulated components and
Conceptio Open Archive
Analyze & explore related research in Conceptio
Connect this record directly into your research workflow or AI agent. Explore citations across open repositories, query full text, and export structured proof bundles.
This document is indexed with metadata only: full text is not available in the archive for this record. Open the official source ↗
Related documents
- Benchmarking MLLMs via Cognitive Expected Scene Graph for Safety-Critical Visual Negation Understanding#1014353
- Risk Assessment of Autonomous Driving: Integrating Technical Failures, Ethical Dilemmas, and Policy Frameworks#259510
- LLMSec-AV: A Vulnerability Taxonomy and LLM-Driven Software Weakness Discovery Framework for Autonomous Vehicles#987139
- Scenario-Based Compositional Statistical Model Checking for Safety Specifications#1210382
- Scenario-Based Compositional Statistical Model Checking for Safety Specifications#1201584
Record · ID 1296983
Retrieved via Conceptio: every document is proof-bundled with source, license, and retrieval metadata.
Retrieved via Conceptio: every document is proof-bundled with source, license, and retrieval metadata.
Bring this data into your agent: Connect via MCP or API · 20 free credits to start · no card required
Read the Docs → · View CLI source on GitHub · Conceptio Open Knowledge Archive
Read the Docs → · View CLI source on GitHub · Conceptio Open Knowledge Archive