VLADriver-RAG reaches a new state-of-the-art Driving Score of 89.12 on Bench2Drive by retrieving structure-aware historical knowledge through spatiotemporal semantic graphs and Graph-DTW alignment.
Spatial retrieval augmented autonomous driving
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PriorEye augments end-to-end driving models with a dual-memory architecture that stores and gates geospatial visual priors to improve performance and robustness to sensor corruption on NAVSIM-v2.
EponaV2 advances perception-free driving world models by forecasting comprehensive future 3D geometry and semantic representations, achieving SOTA planning performance on NAVSIM benchmarks.
citing papers explorer
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VLADriver-RAG: Retrieval-Augmented Vision-Language-Action Models for Autonomous Driving
VLADriver-RAG reaches a new state-of-the-art Driving Score of 89.12 on Bench2Drive by retrieving structure-aware historical knowledge through spatiotemporal semantic graphs and Graph-DTW alignment.
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PriorEye: Geospatial Visual Priors for End-to-End Autonomous Driving
PriorEye augments end-to-end driving models with a dual-memory architecture that stores and gates geospatial visual priors to improve performance and robustness to sensor corruption on NAVSIM-v2.
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EponaV2: Driving World Model with Comprehensive Future Reasoning
EponaV2 advances perception-free driving world models by forecasting comprehensive future 3D geometry and semantic representations, achieving SOTA planning performance on NAVSIM benchmarks.