Auditing Latent-Space Monitors for Autonomous Driving

arXiv:2609.30557 · cs.RO, cs.AI · Submitted 2026-09-24 · Read on arXiv

cs.RO, cs.AI

Submitted: 2026-09-24

Updated: 2026-09-24

Terminology

• AUROC — 22× in this paper · explained in 2 episodes
• Latent Representation — 9× in this paper · explained in 2 episodes
• Calibration — 6× in this paper · explained in 7 episodes
• Generalization — 3× in this paper · explained in 13 episodes
• Latent Representations — 3× in this paper · explained in 3 episodes
• Benchmark — 2× in this paper · explained in 2 episodes
• Conference on Computer Vision and Pattern Recognition — 2× in this paper
• IEEE Intelligent Vehicles Symposium — 2× in this paper
• In International Conference on Learning Representations — 2× in this paper
• Introspection — 2× in this paper · explained in 2 episodes
• Robustness — 2× in this paper · explained in 5 episodes
• Accuracy — 1× in this paper · explained in 1 episode
• Benchmarking — 1× in this paper · explained in 3 episodes
• Consistency — 1× in this paper · explained in 3 episodes
• Decoupling — 1× in this paper · explained in 2 episodes
• Distribution Shift — 1× in this paper · explained in 6 episodes
• Failure Modes — 1× in this paper · explained in 3 episodes
• Gaussian Mixture Model (GMM) — 1× in this paper · explained in 2 episodes
• Latent Space — 1× in this paper · explained in 7 episodes
• Multimodal Dataset — 1× in this paper · explained in 2 episodes

Sources

• GeoReFormer: Geometry-Aware Refinement for Lane Segment Detection and Topology Reasoning
• Collision Risk Estimation via Loss Prediction in End-to-End Autonomous Driving
• Rethinking the Open-Loop Evaluation of End-to-End Autonomous Driving in nuScenes

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• Fine-tuning is Not Enough: A Parallel Framework for Collaborative Imitation and Reinforcement Learning in End-to-end Autonomous Driving