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This research investigates whether frozen vision-language models provide anticipatory safety signals in reinforcement learning, ultimately finding no evidence of collision anticipation despite reduced catastrophe rates. The study attributes the safety gains to reward shaping and augmented multiplier updates rather than predictive capabilities from CLIP signals. These findings clarify the mechanism behind VLM integration in safe RL, offering a more grounded understanding for developers working on autonomous driving systems.
Vision--Language Signals in Constrained RL: Safety Gains Without Anticipation
arXiv:2609.34041v1 Announce Type: new Abstract: Safe reinforcement learning seeks policies that maximise task performance while satisfying safety constraints. In driving benchmarks, however, collision costs typically appear only at the time of collision, providing no advance warning of an approaching hazard. Frozen vision--language models can provide dense semantic feedback, yet it remains unclear whether their scores anticipate collisions and which component drives an observed safety improvement. Episodic cost can also favour policies that make little task progress. To address these gaps, we propose VLM-Safe-RL, a framework that integrates frozen CLIP signals into PPO-Lagrangian through reward shaping and an augmented multiplier update. On MetaDrive Hard, which combines the densest traffic with the largest map, the catastrophe rate falls from 31.6\% to 19.4\%. FormulaOne-L2 analysis finds no evidence that the CLIP signals anticipate collisions and shows that the VLM term has a negligible effect on the Lagrange multiplier. These findings show a conditional reduction in observed catastrophe rate without evidence of collision anticipation.
Abstract:Safe reinforcement learning seeks policies that maximise task performance while satisfying safety constraints. In driving benchmarks, however, collision costs typically appear only at the time of collision, providing no advance warning of an approaching hazard. Frozen vision--language models can provide dense semantic feedback, yet it remains unclear whether their scores anticipate collisions and which component drives an observed safety improvement. Episodic cost can also favour policies that make little task progress. To address these gaps, we propose VLM-Safe-RL, a framework that integrates frozen CLIP signals into PPO-Lagrangian through reward shaping and an augmented multiplier update. On MetaDrive Hard, which combines the densest traffic with the largest map, the catastrophe rate falls from 31.6\% to 19.4\%. FormulaOne-L2 analysis finds no evidence that the CLIP signals anticipate collisions and shows that the VLM term has a negligible effect on the Lagrange multiplier. These findings show a conditional reduction in observed catastrophe rate without evidence of collision anticipation.
Machine Learning (cs.LG)
arXiv:2609.34041 [cs.LG]
(or arXiv:2609.34041v1 [cs.LG] for this version)
https://doi.org/10.48550/arXiv.2609.34041 Focus to learn more arXiv-issued DOI via DataCite (pending registration)
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