仿真到现实环境的自动驾驶决策技术综述  

Decision technologies of simulation to reality for autonomous driving:a survey

作　　者：胡学敏[1,2] 黄婷玉 余雅澜 任佳佳 谢微 陈龙 Hu Xuemin;Huang Tingyu;Yu Yalan;Ren Jiajia;Xie Wei;Chen Long(School of Artificial Intelligence,Hubei University,Wuhan 430062,China;Key Laboratory of Intelligent Sensing System and Security,Ministry of Education,Wuhan 430062,China;School of Computer Science and Information Engineering,Hubei University,Wuhan 430062,China;School of Computer Science and Information Engineering,Linyi University,Linyi 276000,China;Institute of Automation,Chinese Academy of Sciences,Beijing 100190,China)

机构地区：[1]湖北大学人工智能学院,武汉430062 [2]智能感知系统与安全教育部重点实验室,武汉430062 [3]湖北大学计算机与信息工程学院,武汉430062 [4]临沂大学计算机与信息工程学院,临沂276000 [5]中国科学院自动化研究所,北京100190

出　　处：《中国图象图形学报》2024年第11期3173-3194,共22页Journal of Image and Graphics

基　　金：国家自然科学基金项目(62273135);湖北省大学生创新创业训练计划基金资助项目(S202310512025,S202310512042);湖北大学研究生教育教学改革研究项目(1190017755);湖北省大学生原创探索种子专项项目(202416403000001)。

摘　　要：自动驾驶汽车作为未来交通的重要发展方向,决策技术是其进行安全高效行驶的关键。基于成本和安全性的考虑,最新的自动驾驶决策技术往往先在仿真环境中研究,再在现实世界中应用,故在自动驾驶决策领域,仿真到现实的方法能帮助自动驾驶系统更有效地进行学习、训练和验证。然而,仿真环境和现实环境之间的差距会在这些模型和技术转移到真实车辆时带来挑战,这种仿真到现实环境域差距的问题促使研究人员探索解决该问题的途径,并且提出各种有效的方法。本文将这些方法总结为两大类:虚实迁移和平行智能。前者通过不同方法将在模拟环境中训练的车辆决策迁移到现实环境中,以解决域差距问题;后者通过构建虚拟的人工系统和现实的物理系统,将二者进行交互、比较、学习和实验,从而解决自动驾驶决策在现实环境中的适配问题。本文首先从虚实迁移和平行智能的原理,以及自动驾驶决策领域应用的角度进行了详细综述,这也是首次从平行智能的角度来思考自动驾驶决策技术中仿真到现实环境的问题,然后总结了搭建仿真环境常用的自动驾驶模拟器,最后归纳了仿真到现实环境的自动驾驶面临的挑战和未来的发展趋势,既为自动驾驶在现实场景的应用与推广提供技术方案,也为自动驾驶研究人员提供新的想法和方向。Since the mid-1980s,numerous research institutions have been developing autonomous driving technologies.The main idea of autonomous driving technology is to perceive the ego-vehicle states and its surroundings in real time through sensors,utilize an intelligent system for decision-making planning,and execute the driving operation through the control system.The decision-making module,which is an important component in autonomous driving systems,bridges perception and vehicle control.This module is mainly responsible for finding optimal paths or correct and reliable behaviors for the ego-vehicle to effectively drive on the road.In the research process of autonomous driving decision-making technologies,which are remarkably strict for safety,if the training is performed directly in the real world,then it will not only lead to a considerable cost increment but will also miss some marginal driving scenarios.In this case,numerous studies are first conducted in the simulation world before applying new autonomous driving models in the real world.However,the simulation can only provide an approximate model of vehicle dynamics and its interaction with the surrounding environment,and the vehicle agent trained only in the simulation world cannot be generalized to the real world.A gap still exists between reality and simulation,which is called the reality gap(RG)and poses a challenge for the transfer of developed autonomous driving models from simulated vehicles to real vehicles.Researchers have proposed numerous approaches to addressing the reality gap.This paper presents the principles and state-of-the-art methods of transferring knowledge from simulation to reality(sim2real)and parallel intelligence(PI),as well as their applications in decision-making for autonomous driving.Sim2real approaches reduce RG by simply transferring the learned models from the simulation to the reality environment.In autonomous driving,the basic idea of sim2real is to train the vehicle agent in the simulation environment and then transfer it to th

关 键 词：自动驾驶 决策技术 域差距(RG) 虚实迁移(sim2real) 平行智能(PI) 

分 类 号：TP391.9[自动化与计算机技术—计算机应用技术]