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BEVDriver: Leveraging BEV Maps in LLMs for Robust Closed-Loop Driving
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Autonomous driving has the potential to set the stage for more efficient future mobility, requiring the research domain to establish trust through safe, reliable and transparent driving. Large Language Models (LLMs) possess reasoning capabilities and natural language understanding, presenting the potential to serve as generalized decision-makers for ego-motion planning that can interact with humans and navigate environments designed for human drivers. While this research avenue is promising, current autonomous driving approaches are challenged by combining 3D spatial grounding and the reasoning and language capabilities of LLMs. We introduce BEVDriver, an LLM-based model for end-to-end closed-loop driving in CARLA that utilizes latent BEV features as perception input. BEVDriver includes a BEV encoder to efficiently process multi-view images and 3D LiDAR point clouds. Within a common latent space, the BEV features are propagated through a Q-Former to align with natural language instructions and passed to the LLM that predicts and plans precise future trajectories while considering navigation instructions and critical scenarios. On the LangAuto benchmark, our model reaches up to 18.9% higher performance on the Driving Score compared to SoTA methods.
Forward citations
Cited by 5 Pith papers
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STSBench: A Spatio-temporal Scenario Benchmark for Multi-modal Large Language Models in Autonomous Driving
A new benchmark, STSnu, uses 971 verified multiple-choice questions from NuScenes to test driving vision-language models' spatio-temporal reasoning, and shows they lag far behind text-only LLMs given perfect trajectories.
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BEVLM: Distilling Semantic Knowledge from LLMs into Bird's-Eye View Representations
BEV tokens give LLMs stronger cross-view spatial reasoning than multi-view image tokens, and reverse-distilling LLM semantics into BEV encoders measurably improves closed-loop safety-critical driving.
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TGRIP: A Text-Guided Approach to Vehicle Instance Prediction in Autonomous Driving
Auxiliary CLIP-derived BEV semantic supervision during training improves nuScenes end-to-end vehicle instance prediction over a geometric-only baseline, with the semantic head removed at inference.
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A Survey on Vision-Language-Action Models for Autonomous Driving
A survey organizes vision-language-action models for autonomous driving into four stages, compares over 20 systems, and catalogs datasets, benchmarks, and open challenges.
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Generative AI for Autonomous Driving: A Review
A review of generative models (VAEs, GANs, diffusion, transformers, LLMs) applied to map generation, scenario generation, trajectory prediction, and motion planning for autonomous driving.
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