ORION: A Holistic End-to-End Autonomous Driving Framework by Vision-Language Instructed Action Generation
This addresses the challenge of improving decision-making in autonomous driving for safer and more reliable systems, representing a strong specific gain rather than a foundational breakthrough.
The paper tackles the problem of end-to-end autonomous driving methods struggling with causal reasoning in interactive closed-loop evaluation by proposing ORION, a framework that combines vision-language models and generative planning to align reasoning and action spaces, achieving a Driving Score of 77.74 and Success Rate of 54.62% on Bench2Drive datasets, outperforming SOTA by 14.28 DS and 19.61% SR.
End-to-end (E2E) autonomous driving methods still struggle to make correct decisions in interactive closed-loop evaluation due to limited causal reasoning capability. Current methods attempt to leverage the powerful understanding and reasoning abilities of Vision-Language Models (VLMs) to resolve this dilemma. However, the problem is still open that few VLMs for E2E methods perform well in the closed-loop evaluation due to the gap between the semantic reasoning space and the purely numerical trajectory output in the action space. To tackle this issue, we propose ORION, a holistic E2E autonomous driving framework by vision-language instructed action generation. ORION uniquely combines a QT-Former to aggregate long-term history context, a Large Language Model (LLM) for driving scenario reasoning, and a generative planner for precision trajectory prediction. ORION further aligns the reasoning space and the action space to implement a unified E2E optimization for both visual question-answering (VQA) and planning tasks. Our method achieves an impressive closed-loop performance of 77.74 Driving Score (DS) and 54.62% Success Rate (SR) on the challenge Bench2Drive datasets, which outperforms state-of-the-art (SOTA) methods by a large margin of 14.28 DS and 19.61% SR.