arXiv (All)
open access
Auditing Latent-Space Monitors for Autonomous Driving
Runtime failure monitors can use a model's internal representations to anticipate failures. We audit this monitoring strategy across two autonomous-driving tasks: online vectorized map generation with LaneSegNet and end-to-end planning with VAD. We find that frame-level errors are predictable at inf
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
- Drive the Thoughts: Runtime Monitoring of VLA Reasoning-Trajectory Consistency#798224
- Drive the Thoughts: Runtime Monitoring of VLA Reasoning-Trajectory Consistency#796811
- ExceptionDrive: A Planning-Oriented Counterfactual Corner-Case Benchmark for Autonomous Driving#1142178
- Driving the Wrong Way: Leveraging Interpretability in End2End Autonomous Driving Models#346559
- PlannerForge: LLM Agents for Scenario-Based Testing of Motion Planners in Autonomous Driving#986584
Record · ID 1104756
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