Springer Nature OA
open access
Compositionally Safe Construction of Autonomous Driving Systems
Developing safe autonomous driving systems (ADS) is a major scientific and technical challenge. ADS are real-time distributed systems involving agents/vehicles with partial knowledge of their environment, pursuing specific goals while the collective behavior must meet given global goals. We study a
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
- PlannerForge: LLM Agents for Scenario-Based Testing of Motion Planners in Autonomous Driving#986584
- Behavioral Safety Assessment towards Large-scale Deployment of Autonomous Vehicles, Part I: Methodology#1218185
- VehicleArena: A Realistic Urban Environment for Multi-Agent Driving#1130758
- CrashDiffuser: VLM-Guided Collision Intent Reasoning for Fine-Grained Safety-Critical Traffic Scenario Generation#924169
- CrashDiffuser: VLM-Guided Collision Intent Reasoning for Fine-Grained Safety-Critical Traffic Scenario Generation#919938
Record · ID 1347992
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