基于视觉感兴趣区的概率风险评估多模态轨迹预测
Visual Area of Interests based Multimodal Trajectory Prediction for Probabilistic Risk Assessment
摘要：
准确可靠地预测驾驶意图和未来轨迹有助于人类驾驶员和ADAS在复杂交通环境中的合作。提出了一种基于感兴趣区域（Area of Interest，AOI）的交叉口概率风险评估多模式轨迹预测模型。在本研究中，我们发现，视觉AOI暗示了驾驶意图，并提前约0.6-2.1秒的操作。因此，我们设计了一个轨迹预测模型，集成了驾驶意图（DI）和多模态轨迹（MT）预测。DI模型独立地进行预训练，以使用包括视觉AOI、历史车辆状态和环境背景在内的特征来提取驾驶意图。意向预测实验验证了基于视觉AOI的DI模型预测转向意向比实际转向操作提前0.925 s。然后将训练好的DI模型集成到轨迹预测模型中以过滤多模式轨迹。弹道预测实验表明，该模型的性能优于最先进的模型。交叉口交通风险评估结果表明，该方法具有较高的准确率和较低的误报率，并在冲突发生前3 s左右识别出潜在的风险。
英文摘要：
Accurate and reliable prediction of driving intentions and future trajectories contributes to cooperation between human drivers and ADAS in complex traffic environments. This paper proposes a visual AOI (Area of Interest) based multimodal trajectory prediction model for probabilistic risk assessment at intersections. In this study, we find that the visual AOI implies the driving intention and is about 0.6-2.1 s ahead of the operation. Therefore, we designed a trajectory prediction model that integrates the driving intention (DI) and the multimodal trajectory (MT) predictions. The DI model was pre-trained independently to extract the driving intention using features including the visual AOI, historical vehicle states, and environmental context. The intention prediction experiments verify that the visual AOI-based DI model predicts steering intention 0.925 s ahead of the actual steering operation. The trained DI model is then integrated into the trajectory prediction model to filter multimodal trajectories. The trajectory prediction experiments show that the proposed model outperforms the state-of-the-art models. Risk assessment for traffics at intersections verifies that the proposed method achieves high accuracy and a low false alarm rate, and identifies the potential risk about 3 s before a conflict occurs.
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。
