PADriver：迈向个性化自动驾驶
PADriver: Towards Personalized Autonomous Driving
AI总结：
本文提出基于多模态大语言模型的个性化自动驾驶闭环框架PADriver，结合流式视频与个性化提示进行场景理解、危险估计与动作决策，并构建PAD-Highway基准验证其优于现有方法。
摘要：
在本文中，我们提出了PADriver，一个用于个性化自动驾驶（PAD）的新型闭环框架。PADriver建立在多模态大语言模型（MLLM）之上，以流式帧和个性化文本提示作为输入。它自动执行场景理解、危险等级估计和动作决策。预测的危险等级反映了潜在动作的风险，并为最终动作提供了明确的参考，该最终动作与预设的个性化提示相对应。此外，我们基于Highway-Env模拟器构建了一个名为PAD-Highway的闭环基准，以全面评估交通规则下的决策性能。该数据集包含250小时具有高质量标注的视频，以促进PAD行为分析的发展。在所构建基准上的实验结果表明，PADriver在不同的评估指标上优于最先进的方法，并支持多种驾驶模式。
英文摘要：
In this paper, we propose PADriver, a novel closed-loop framework for personalized autonomous driving (PAD). Built upon Multi-modal Large Language Model (MLLM), PADriver takes streaming frames and personalized textual prompts as inputs. It autoaggressively performs scene understanding, danger level estimation and action decision. The predicted danger level reflects the risk of the potential action and provides an explicit reference for the final action, which corresponds to the preset personalized prompt. Moreover, we construct a closed-loop benchmark named PAD-Highway based on Highway-Env simulator to comprehensively evaluate the decision performance under traffic rules. The dataset contains 250 hours videos with high-quality annotation to facilitate the development of PAD behavior analysis. Experimental results on the constructed benchmark show that PADriver outperforms state-of-the-art approaches on different evaluation metrics, and enables various driving modes.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
