The invention relates to the field of computer vision, in particular to an automatic driving track prediction method and system based on a novel suggestion-optimization framework. The method comprises the following steps: constructing a novel suggestion-optimization framework; obtaining a learnable feature code, inputting the learnable feature code into a preset baseline model, and outputting decoded data through the baseline model; inputting the decoded data into a novel suggestion-optimization framework, and performing trajectory prediction and trajectory optimization on the decoded data; outputting trajectory prediction and trajectory optimization data, and completing automatic driving trajectory prediction based on the novel suggestion-optimization framework. According to the suggestion-optimization framework provided by the invention, a clustering-based suggestion trajectory generation module is introduced, region division prediction and a region trajectory prototype are used as a refined prior anchor frame, and a complexity-based optimization module is used for distinguishing simple and complex trajectories, so that the optimization module can focus on learning features of the complex trajectories, and the optimization efficiency is improved. Therefore, the prediction accuracy of the long-tail scene is improved.
本发明涉及计算机视觉领域,特别涉及一种基于新型建议‑优化框架的自动驾驶轨迹预测方法及系统。方法包括:构建新型建议‑优化框架;获取可学习的特征编码,将可学习的特征编码输入至预设的基线模型中,通过基线模型输出解码数据;将解码数据输入至新型建议‑优化框架,对解码数据进行轨迹预测和轨迹优化;输出轨迹预测和轨迹优化数据,完成基于新型建议‑优化框架的自动驾驶轨迹预测。本发明提供的建议‑优化框架引入了基于聚类的建议轨迹生成模块,利用区域划分预测和区域轨迹原型作为细化的先验锚框,以及一种基于复杂度的优化模块,用于区分简单和复杂轨迹,使得优化模块能够专注于学习复杂轨迹的特征,从而提高长尾场景的预测准确性。
Automatic driving track prediction method and system based on novel suggestion-optimization framework
基于新型建议-优化框架的自动驾驶轨迹预测方法及系统
2024-09-17
Patent
Electronic Resource
Chinese
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