LSRE: Real-Time Semantic Risk Detection in Autonomous Driving
Analysis
This paper addresses the critical challenge of incorporating complex human social rules into autonomous driving systems. It proposes a novel framework, LSRE, that leverages the power of large vision-language models (VLMs) for semantic understanding while maintaining real-time performance. The core innovation lies in encoding VLM judgments into a lightweight latent classifier within a recurrent world model, enabling efficient and accurate semantic risk assessment. This is significant because it bridges the gap between the semantic understanding capabilities of VLMs and the real-time constraints of autonomous driving.
Key Takeaways
- •LSRE enables real-time semantic risk assessment in autonomous driving.
- •It leverages VLM for semantic understanding but avoids per-frame queries for efficiency.
- •The framework encodes language-defined safety semantics into a lightweight latent classifier.
- •LSRE achieves accuracy comparable to a VLM baseline with earlier hazard anticipation and low latency.
- •It demonstrates generalization to unseen semantic-similar test cases.
“LSRE attains semantic risk detection accuracy comparable to a large VLM baseline, while providing substantially earlier hazard anticipation and maintaining low computational latency.”