智能驾驶泊车路径规划算法研究综述  

Overview on Research on Path Planning Algorithms for Intelligent Driving Parking

作　　者：王燕燕 查云飞 Wang Yanyan;Zha Yunfei(Fujian University of Technology,Fuzhou 350118)

机构地区：[1]福建理工大学,福州350118

出　　处：《汽车文摘》2025年第4期29-36,共8页Automotive Digest

摘　　要：为了提高泊车路径规划的效率和质量,对智能驾驶泊车路径规划算法进行研究。通过梳理近年来国内外学术研究的泊车规划方法,根据规划算法特性将泊车规划算法分为图搜索算法、空间采样算法、智能算法、曲线插值算法和最优控制算法。分析5类算法的优缺点,并探讨融合算法在特定环境下的应用,结合规划求解效率和规划路径曲率等指标,对现有方案的合理有效性进行评价,并对泊车路径规划算法的未来发展进行展望。研究表明:(1)图搜索算法具备全局路径最优、实时性较好等优点,但存在路径连续性差、高维空间复杂度高的问题;(2)空间采样算法具有概率完备和高维空间搜索效率高的优势,但内存消耗大、随机性大且路径曲率无法保证;(3)智能算法利用样本学习能力强、迭代性强,但训练成本高、动态适应性差、实时性差;(4)基于优化的曲线插值算法路径易计算,但无法保证曲率的连续性;(5)基于优化的最优控制算法能够处理复杂优化问题,但时效性无法保证且易陷入局部最小值。融合算法通过优势互补,能够更好地适应车辆自身约束和环境约束,实现高效、安全的泊车路径规划。In order to improve the efficiency and quality of parking path planning,a study on intelligent driving parking path planning algorithms has been conducted.By reviewing the parking planning methods from recent domestic and international academic research,based on their characteristics the parking path planning algorithms are categorized into 5 types:graph search algorithms,sampling-based algorithms,intelligent algorithms,curve interpolation algorithms,and optimal control algorithms.The advantages and disadvantages of these 5 types of algorithms are analyzed,and the application of fusion algorithms in specific environments is explored.The rationality and effectiveness of existing solutions are evaluated in terms of planning efficiency and path curvature.Furthermore,future development trends in parking path planning algorithms are discussed.The study concludes the following:(1)Graph search algorithms offer the advantages of globally optimal paths and good real-time performance but suffer from poor path continuity and high complexity in high-dimensional spaces.(2)Sampling-based algorithms have the advantages of probabilistic completeness and high search efficiency in high-dimensional spaces but have large memory consumption,significant randomness,and cannot guarantee path curvature.(3)Intelligent algorithms have strong learning capabilities based on samples and strong iteration capabilities but have high training costs,poor dynamic adaptability,and poor real-time performance.(4)Curve interpolation algorithms based on optimization are easy to calculate but cannot guarantee the continuity of curvature.(5)Optimal control algorithms based on optimization can handle complex optimization problems but cannot guarantee timeliness and are prone to falling into local minima.Fusion algorithms through complementary advantages can better adapt to vehicle constraints and environmental constraints,achieving efficient and safe parking path planning.

关 键 词：智能驾驶 泊车路径规划 规划效率 路径曲率 

分 类 号：U471.15[机械工程—车辆工程]