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冀杰 , 唐志荣 , 吴明阳 , 等 . 面向车道变换的路径规划及模型预测轨迹跟踪 [J ] . 中国公路学报 , 2018 , 31 ( 4 ): 172 - 179 . 为解决智能车辆在车道变换过程中的路径规划和路径跟踪问题，首先，利用梯形加速度法设计了车道变换虚拟理想轨迹，该路径规划方法的适应性取决于车道变换时间、横向加速度及变化率等关键变量的约束条件，因而对各关键变量之间的数学关系进行了定量计算，并绘制了不同工况下的车道变换虚拟理想轨迹，用于分析各关键变量对路径规划的影响；其次，建立了线性离散的车辆动力学预测模型，综合分析了车辆模型的控制输入、状态变量以及道路结构参数等约束条件，构建了多约束模型预测控制（MMPC）系统用于车道变换路径跟踪，并基于Hildreth二次规划算法对其目标函数进行了求解，获得前轮转向角控制量，从而保证智能车辆在车道变换过程中的路径跟踪性能及操纵稳定性能；最后，利用MATLAB和Carsim软件对提出的多约束模型预测控制系统进行联合仿真，并构建单约束模型预测控制（SMPC）系统与其进行性能比较，分别对车道变换时间为3 s和6 s时的车道变换性能进行比较分析。结果表明：当车道变换时间为6 s时，2种控制系统都能较好地实现车道变换功能；当车道变换时间为3 s时，与SMPC控制系统相比较，MMPC控制系统能够在有效跟踪期望行驶路径的同时改善车辆的操纵稳定性，从而提高车辆在路径跟踪过程中的主动安全性能。

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李立 , 徐志刚 , 赵祥模 , 等 . 智能网联汽车运动规划方法研究综述 [J ] . 中国公路学报 , 2019 , 32 ( 6 ): 20 - 33 . DOI: 10.19721/j.cnki.1001-7372.2019.06.002 http://doi.org/10.19721/j.cnki.1001-7372.2019.06.002 分析了近年来智能网联汽车（Intelligent Connected Vehicle，ICV）运动规划方法的研究，根据规划时空尺度和任务目标，将ICV运动规划细分为路径规划、路线规划、动作规划和轨迹规划4级子任务，回顾了各级子任务中智能网联技术的研究和应用现状；探讨了ICV中驾驶人行为特性及其对运动规划结果的影响；从技术背景、研究场景、算法流程和应用理论4个方面，提出ICV运动规划方法研究的未来发展方向。结果表明：由于ICV主要依赖车辆网联信息规划运动路径，而路网中同时存在不同网联等级的ICV，这将增加路径规划问题的求解难度；现有ICV路线规划模型较少考虑周边多车运动状态以及路段车道设置情况，将现有算法与微观交通流模型相结合有助于解决此问题；ICV中人机协同及任务切换领域已出现诸多研究热点，如城市道路上换道与转弯动作规划、ICV引导非网联车辆行驶等问题；借鉴驾驶人行为模式规划ICV运动轨迹已成为研究共识，但是车-车、车-路网联信息在此领域的应用仍然有限；采用反馈-迭代的方法进行ICV运动路线和动作协同规划、运动规划和轨迹跟踪控制有助于获得全局最优的运动规划结果和车辆控制策略；根据具体规划任务特点选择构建ICV运动规划模型的基础理论，有助于发挥各类理论的优势，提升规划算法的灵活性和适用性。

LI L , XU Z G , ZHAO X M , et al. Review of motion planning methods of intelligent connected vehicles [J ] . China Journal of Highway and Transport , 2019 , 32 ( 6 ): 20 - 33 . (in Chinese) DOI: 10.19721/j.cnki.1001-7372.2019.06.002 http://doi.org/10.19721/j.cnki.1001-7372.2019.06.002 Recent studies on motion planning methods of intelligent connected vehicle (ICV) are analyzed in this paper. In terms of working space, time, and objective, ICV's motion planning is divided into four subtasks:route planning, path planning, maneuver planning, and trajectory planning. Past research and applications of the techniques of vehicle intelligence and connection in each subtask are reviewed. Behavioral characteristics of the ICV driver and their impact on the outcome of ICV motion planning are discussed. Four aspects of the current trend in ICV motion planning research are discussed:technical background, research scenario, algorithm flow and applied theory. As an ICV mainly depends on vehicle connecting information to plan travelling route, this survey finds that the difficulty of ICV route planning increases when ICV's with different connecting functions coexist in the road network. Dynamics of multiple surrounding vehicles and lane configuration are rarely considered in ICV's path planning. This is likely to be addressed by integrating the existing path planning algorithms with microscopic traffic flow models. The issues of human-machine cooperation and task transfer in ICV have recently become hot topics of research. These issues include lane changing and turning maneuver planning in urban arterial roads, maneuver guidance of ICV for non-connecting vehicle and others. There is academic consensus that the behavior of the driver in an ICV should be considered in trajectory planning. However, there is limited application of vehicle-to-vehicle and vehicle-to-infrastructure connecting information. We propose that applying feedback-iteration to coordinate ICV's path and maneuver planning as well as its motion planning and trajectory tracking control could help in globally optimized motion planning and vehicle control. Furthermore, formulating a model for ICV's motion planning on a theoretical foundation that is appropriate for the specific motion-planning task could not only take advantage of the merits of the theory but also increase flexibility and adaptability of the motion planning algorithms.

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刘志飞 , 曹雷 , 赖俊 , 等 . 多智能体路径规划综述 [J ] . 计算机工程与应用 , 2022 , 58 ( 20 ): 43 - 62 . DOI: 10.3778/j.issn.1002-8331.2203-0467 http://doi.org/10.3778/j.issn.1002-8331.2203-0467 多智能体路径规划（multi-agent path finding，MAPF）是为多个智能体规划路径的问题，关键约束是多个智能体同时沿着规划路径行进而不会发生冲突。MAPF在物流、军事、安防等领域有着大量应用。对国内外关于MAPF的主要研究成果进行系统整理和分类，按照规划方式不同，MAPF算法分为集中式规划算法和分布式执行算法。集中式规划算法是最经典和最常用的MAPF算法，主要分为基于[A*]搜索、基于冲突搜索、基于代价增长树和基于规约四种算法。分布式执行算法是人工智能领域兴起的基于强化学习的MAPF算法，按照改进技术不同，分布式执行算法分为专家演示型、改进通信型和任务分解型三种算法。基于上述分类，比较MAPF各种算法的特点和适用性，分析现有算法的优点和不足，指出现有算法面临的挑战并对未来工作进行了展望。

LIU Z F , CAO L , LAI J , et al. Overview of Multi-Agent Path Finding . Computer Engineering and Applications , 2022 , 58 ( 20 ): 43 - 62 . (in Chinese) DOI: 10.3778/j.issn.1002-8331.2203-0467 http://doi.org/10.3778/j.issn.1002-8331.2203-0467 The multi-agent path finding（MAPF） problem is the fundamental problem of planning paths for multiple agents, where the key constraint is that the agents will be able to follow these paths concurrently without colliding with each other. MAPF is widely used in logistics, military, security and other fields. MAPF algorithm can be divided into the centralized planning algorithm and the distributed execution algorithm when the main research results of MAPF at home and abroad are systematically sorted and classified according to different planning methods. The centralized programming algorithm is not only the most classical but also the most commonly used MAPF algorithm. It is mainly divided into four algorithms based on [A*] search, conflict search, cost growth tree and protocol. The other part of MAPF which is the distributed execution algorithm is based on reinforcement learning. According to different improved techniques, the distributed execution algorithm can be divided into three types：the expert demonstration, the improved communication and the task decomposition. The challenges of existing algorithms are pointed out and the future work is forecasted based on the above classification by comparing the characteristics and applicability of MAPF algorithms and analyzing the advantages and disadvantages of existing algorithms.

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