无人驾驶突发紧要场景下基于平行视觉的风险增强感知方法  

Enhanced risk perception method based on parallel vision for autonomous vehicles in safety-critical scenarios

作　　者：苟超 刘欣欣 郭子鹏 周昱臣 王飞跃[2] Gou Chao;Liu Xinxin;Guo Zipeng;Zhou Yuchen;Wang Feiyue(School of Intelligent Systems Engineering,Sun Yat-sen University,Shenzhen 518107,China;State Key Laboratory of Management and Control for Complex Systems,Institute of Automation,Chinese Academy of Sciences,Beijing 100190,China)

机构地区：[1]中山大学智能工程学院,深圳518107 [2]中国科学院自动化研究所复杂系统管理与控制国家重点实验室,北京100190

出　　处：《中国图象图形学报》2024年第11期3265-3279,共15页Journal of Image and Graphics

基　　金：国家自然科学基金项目(62373387);广东省自然科学基金项目(2023A1515030264)。

摘　　要：目的随着视觉感知技术的快速发展,无人驾驶已经可以应用于简单场景。但是在实际的复杂城市道路应用中,仍然存在一些挑战,尤其是在其他车辆的突然变道、行人的闯入、障碍物的出现等突发紧要场景中。然而,真实世界中此类紧要场景数据存在长尾分布问题,导致数据驱动为主的无人驾驶风险感知面临技术瓶颈,因此,本文提出一种基于平行视觉的风险增强感知方法。方法该方法基于交互式ACP(artificial societies,computational experiments,parallel execution)理论,在平行视觉框架下整合描述、指示、预测智能,实现基于视觉的风险增强感知。具体地,基于描述与指示学习,在人工图像系统中引入改进扩散模型,设计背景自适应模块以及特征融合编码器,通过控制生成行人等危险要素的具体位置,实现突发紧要场景风险序列的可控生成;其次,采用基于空间规则的方法,提取交通实体之间的空间关系和交互关系,实现认知场景图的构建;最后,在预测学习框架下,提出了一种新的基于图模型的风险增强感知方法,融合关系图注意力网络和Transformer编码器模块对场景图序列数据进行时空建模,最终实现风险的感知与预测。结果为验证提出方法的有效性,在MRSG-144(mixed reality scene graph)、IESG(interaction-enhanced scene graph)和1043-carla-sg(1043-carla-scenegraph)数据集上与5种主流风险感知方法进行了对比实验。提出的方法在3个数据集上分别取得了0.956、0.944、0.916的F1-score,均超越了现有主流方法,达到最优结果。结论本文是平行视觉在无人驾驶风险感知领域的实际应用,对于提高无人驾驶的复杂交通场景风险感知能力,保障无人驾驶系统的安全性具有重要意义。Objective With the rapid development of visual perception technology,autonomous driving can already be applied to simple scenarios.However,in actual complex urban road applications,especially in safety-critical scenarios such as sudden lane changes by other vehicles,the intrusion of pedestrians,and the appearance of obstacles,some challenges must still be resolved.First,most existing autonomous driving systems still use the vast majority of daily natural scenes or heuristically generated adversarial scenes for training and evaluation.Among them,safety-critical scenarios,which refer to a collection of scenes in areas where cars are in danger of collision,especially scenes involving vulnerable traffic groups such as pedestrians,play an important role in the safety performance evaluation of autonomous driving systems.However,this type of scenario generally has a low probability of occurring in the real world,and such critical scene data have long-tail distribution problems,causing data-driven autonomous driving risk perception to face technical bottlenecks.Second,creating new scenes using current scene generation methods or virtual simulation scene automatic generation frameworks based on certain rules is difficult,and the generated driving scenes are often insufficiently realistic and lack a certain degree of diversity.By contrast,the scene generation method based on the diffusion model not only fully explores the characteristics of real data and supplements the gaps in the existing collected real data,but also facilitates interpretable and controllable scene generation.In addition,the difficult problem of limited system risk perception capabilities is still encountered in safety-critical scenarios.For risk-aware safety assessment technology,traditional methods based on convolutional neural networks can achieve the simple extraction of features of each object in the scene but cannot obtain high-level semantic information,that is,the relationship between various traffic entities.Obtaining such high-level information

关 键 词：无人驾驶 平行视觉 认知场景图 扩散生成 风险感知 

分 类 号：TP391[自动化与计算机技术—计算机应用技术]