Autonomous Driving AI Comparison Hub
3 papers - avg viability 3.7
Top Papers
- LLM-MLFFN: Multi-Level Autonomous Driving Behavior Feature Fusion via Large Language Model(7.0)
LLM-MLFFN uses large language models for enhanced interpretation and classification of autonomous driving behaviors, boosting safety and robustness.
- Drive-KD: Multi-Teacher Distillation for VLMs in Autonomous Driving(2.0)
Drive-KD optimizes autonomous driving VLMs through multi-teacher distillation to enhance efficiency and performance.
- BEVLM: Distilling Semantic Knowledge from LLMs into Bird's-Eye View Representations(2.0)
BEVLM enhances autonomous driving by integrating LLMs with spatially consistent BEV representations for improved semantic reasoning and safety.