标题： DriveBLIP2：复杂驾驶场景的注意力引导解释生成 显示英文标题

标题： DriveBLIP2: Attention-Guided Explanation Generation for Complex Driving Scenarios

摘要： 本文介绍了一种新的框架DriveBLIP2，该框架基于BLIP2-OPT架构，用于为新兴驾驶场景生成准确且上下文相关的解释。 尽管现有的视觉-语言模型在一般任务中表现良好，但在理解复杂、多对象环境方面遇到困难，尤其是在自动驾驶等实时应用中，快速识别关键物体至关重要。 为解决这一限制，提出了一个注意力图生成器，以突出显示关键视频帧中与驾驶决策相关的显著物体。 通过引导模型关注这些关键区域，生成的注意力图有助于产生清晰且相关的解释，使驾驶员更好地理解车辆在关键时刻的决策过程。 在DRAMA数据集上的评估表明，与基线模型相比，解释质量有显著提高，如BLEU、ROUGE、CIDEr和SPICE得分更高。 这些发现强调了定向注意力机制在视觉-语言模型中的潜力，以增强实时自动驾驶中的可解释性。 显示英文摘要

摘要： This paper introduces a new framework, DriveBLIP2, built upon the BLIP2-OPT architecture, to generate accurate and contextually relevant explanations for emerging driving scenarios. While existing vision-language models perform well in general tasks, they encounter difficulties in understanding complex, multi-object environments, particularly in real-time applications such as autonomous driving, where the rapid identification of key objects is crucial. To address this limitation, an Attention Map Generator is proposed to highlight significant objects relevant to driving decisions within critical video frames. By directing the model's focus to these key regions, the generated attention map helps produce clear and relevant explanations, enabling drivers to better understand the vehicle's decision-making process in critical situations. Evaluations on the DRAMA dataset reveal significant improvements in explanation quality, as indicated by higher BLEU, ROUGE, CIDEr, and SPICE scores compared to baseline models. These findings underscore the potential of targeted attention mechanisms in vision-language models for enhancing explainability in real-time autonomous driving.