标题： LightEMMA：用于自动驾驶的轻量级端到端多模态模型 显示英文标题

标题： LightEMMA: Lightweight End-to-End Multimodal Model for Autonomous Driving

摘要： 视觉-语言模型（VLMs）在端到端自动驾驶方面展示了显著的潜力。然而，充分利用它们的能力以实现安全可靠的车辆控制仍然是一个开放的研究挑战。为了系统地检查VLMs在驾驶任务中的进展和局限性，我们介绍了LightEMMA，一种用于自动驾驶的轻量级端到端多模态模型。LightEMMA提供了一个统一的、基于VLM的自动驾驶框架，无需临时定制，从而能够轻松集成和评估不断发展的最先进的商业和开源模型。我们使用各种VLM构建了十二个自动驾驶代理，并在nuScenes预测任务上评估了它们的性能，全面评估了诸如推理时间、计算成本和预测准确性等指标。示例表明，尽管VLMs具有强大的场景理解能力，但它们在自动驾驶任务中的实际表现仍然令人担忧，强调了进一步改进的必要性。代码可在 https://github.com/michigan-traffic-lab/LightEMMA 获取。 显示英文摘要

摘要： Vision-Language Models (VLMs) have demonstrated significant potential for end-to-end autonomous driving. However, fully exploiting their capabilities for safe and reliable vehicle control remains an open research challenge. To systematically examine advances and limitations of VLMs in driving tasks, we introduce LightEMMA, a Lightweight End-to-End Multimodal Model for Autonomous driving. LightEMMA provides a unified, VLM-based autonomous driving framework without ad hoc customizations, enabling easy integration and evaluation of evolving state-of-the-art commercial and open-source models. We construct twelve autonomous driving agents using various VLMs and evaluate their performance on the nuScenes prediction task, comprehensively assessing metrics such as inference time, computational cost, and predictive accuracy. Illustrative examples highlight that, despite their strong scenario interpretation capabilities, VLMs' practical performance in autonomous driving tasks remains concerning, emphasizing the need for further improvements. The code is available at https://github.com/michigan-traffic-lab/LightEMMA.