基于改进蚁群优化算法的自动驾驶多车协同运动规划

doi:10.19678/j.issn.1000-3428.0062824

摘要/Abstract

摘要： 当前面向多辆自动驾驶汽车的协同运动规划方法能有效保证运行车辆与障碍物及其他车辆之间避免发生碰撞并保持安全距离，但车辆间的在线协同与规划能力仍有待提升。为实现多辆自动驾驶汽车在运动过程中的协同控制，提出一种基于改进蚁群优化算法的多车在线协同规划方法。以空间协同与轨迹代价为优化目标，构造多目标优化函数，确保了多车行驶过程中的协同安全性与轨迹平滑性。将多目标优化函数引入蚁群优化算法的信息素更新过程中，根据自动驾驶车辆数量产生多个种群，使得种群之间相互独立的同时为每辆自动驾驶汽车规划可行路线。最终对蚁群优化算法中的挥发因子进行自适应调整，提升了算法全局搜索能力及收敛速度。实验结果表明，该方法能使多辆自动驾驶汽车在运动过程中保持协同控制并规划出无碰撞路线，相比于基于人工势场和模型预测的协同驾驶方法在复杂道路场景下车辆间的协同效果更好且适应性更强。

关键词: 自动驾驶, 运动规划, 协同控制, 多目标优化, 蚁群优化算法

Abstract: Current collaborative motion planning methods for multiple autonomous driving vehicles can effectively prevent collisions and maintain a safe distance between running vehicles and obstacles and other vehicles.However, the online collaboration and planning capabilities between vehicles still need to be improved.This study proposes a multi-vehicle online collaborative planning method based on an improved Ant Colony Optimization(ACO) algorithm to achieve the collaborative control of multiple autonomous driving vehicles during motion.By taking space collaboration and trajectory cost as optimization objectives, a multi-objective optimization function is constructed to ensure collaboration safety and trajectory smoothness during multi-vehicle driving.The multi-objective optimization function is introduced into the pheromone update process of the ACO algorithm, and multiple populations are generated based on the number of autonomous driving vehicles.The populations are independent and plan feasible routes for autonomous driving vehicles.The volatile factor in the ACO algorithm is adaptively adjusted, improving the global search ability and convergence speed of the algorithm.The experimental results show that the proposed method can maintain multiple autonomous driving vehicles in collaborative control during the movement process and plan a collision-free route.Compared with the collaborative driving method based on Artificial Potential Field(APF) and model prediction, the proposed method has a better collaborative effect between vehicles and more robust adaptability in complex road scenes.

Key words: autonomous driving, motion planning, collaborative control, multi-objective optimization, Ant Colony Optimization(ACO) algorithm

中图分类号:

TP18

宋佳艳, 苏圣超. 基于改进蚁群优化算法的自动驾驶多车协同运动规划[J]. 计算机工程, 2022, 48(11): 299-305,313.

SONG Jiayan, SU Shengchao. Multi-Vehicle Collaborative Motion Planning for Autonomous Driving Based on Improved Ant Colony Optimization Algorithm[J]. Computer Engineering, 2022, 48(11): 299-305,313.

http://www.ecice06.com/CN/Y2022/V48/I11/299

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参考文献

[1] GONZÁLEZ D, PÉREZ J, MILANÉS V, et al.A review of motion planning techniques for automated vehicles[J].IEEE Transactions on Intelligent Transportation Systems, 2016, 17(4):1135-1145.
[2] KATRAKAZAS C, QUDDUS M, CHEN W H, et al.Real-time motion planning methods for autonomous on-road driving:state-of-the-art and future research directions[J]. Transportation Research Part C:Emerging Technologies, 2015, 60:416-442.
[3] RIOS-TORRES J, MALIKOPOULOS A A.A survey on the coordination of connected and automated vehicles at intersections and merging at highway on-ramps[J].IEEE Transactions on Intelligent Transportation Systems, 2017, 18(5):1066-1077.
[4] KIM S W, QIN B X, CHONG Z J, et al.Multivehicle cooperative driving using cooperative perception:design and experimental validation[J].IEEE Transactions on Intelligent Transportation Systems, 2015, 16(2):663-680.
[5] 高士根.多列车协同运行的若干控制问题研究[D].北京:北京交通大学, 2016. GAO S G.On several control problems of multi-train cooperation[D].Beijing:Beijing Jiaotong University, 2016.(in Chinese)
[6] RASEKHIPOUR Y, KHAJEPOUR A, CHEN S K, et al.A potential field-based model predictive path-planning controller for autonomous road vehicles[J].IEEE Transactions on Intelligent Transportation Systems, 2017, 18(5):1255-1267.
[7] LI X H, SUN Z P, CAO D P, et al.Development of a new integrated local trajectory planning and tracking control framework for autonomous ground vehicles[J].Mechanical Systems and Signal Processing, 2017, 87:118-137.
[8] DÜRING M, FRANKE K, BALAGHIASEFI R, et al.Adaptive cooperative maneuver planning algorithm for conflict resolution in diverse traffic situations[C]//Proceedings of International Conference on Connected Vehicles and Expo.Washington D.C., USA:IEEE Press, 2015:242-249.
[9] CHEN M, FISAC J F, SASTRY S, et al.Safe sequential path planning of multi-vehicle systems via double-obstacle Hamilton-Jacobi-Isaacs variational inequality[C]//Proceedings of European Control Conference.Washington D.C., USA:IEEE Press, 2015:3304-3309.
[10] LI B, JIA N, LI P, et al.Incrementally constrained dynamic optimization:a computational framework for lane change motion planning of connected and automated vehicles[J].Journal of Intelligent Transportation Systems, 2019, 23(6):557-568.
[11] TAHIROVIC A, BRKIC M, BOSTAN A, et al.A receding horizon scheme for constrained multi-vehicle coverage problems[C]//Proceedings of IEEE International Conference on Systems, Man, and Cybernetics.Washington D.C., USA:IEEE Press, 2016:4652-4656.
[12] HUANG Z C, WU Q, MA J, et al.An APF and MPC combined collaborative driving controller using vehicular communication technologies[J].Chaos, Solitons & Fractals, 2016, 89:232-242.
[13] LI B, ACARMAN T, ZHANG Y M, et al.Tractor-trailer vehicle trajectory planning in narrow environments with a progressively constrained optimal control approach[J].IEEE Transactions on Intelligent Vehicles, 2020, 5(3):414-425.
[14] COLORNI A, DORIGO M.Distributed optimization by ant colonies[C]//Proceedings of the 1st European Conference on Artificial Life.Cambridge, USA:MIT Press, 1991:1-10.
[15] SUN G, ZHANG Y J, LIAO D, et al.Bus-trajectory-based street-centric routing for message delivery in urban vehicular ad hoc networks[J].IEEE Transactions on Vehicular Technology, 2018, 67(8):7550-7563.
[16] DORIGO M, MANIEZZO V, COLORNI A.Ant system:optimization by a colony of cooperating agents[J].IEEE Transactions on Systems, Man, and Cybernetics, 1996, 26(1):29-41.
[17] WANG L Z, WANG D W, WU S L, et al.Study on multi-depots vehicle transshipment scheduling problem and its genetic algorithm and ant colony algorithm hybrid optimization[M].[S.l.]:Atlantis Press, 2016.
[18] DORIGO M, GAMBARDELLA L M.Ant colony system:a cooperative learning approach to the traveling salesman problem[J].IEEE Transactions on Evolutionary Computation, 1997, 1(1):53-66.
[19] CHELOUAH R, BARON C.Ant colony algorithm hybridized with tabu and greedy searches as applied to multi-objective optimization in project management[J].Journal of Heuristics, 2007, 13(6):640.
[20] 李俊, 周虎, 李波.基于虚拟蚂蚁的局部优化蚁群算法[J].控制与决策, 2019, 34(11):2459-2468. LI J, ZHOU H, LI B.Local optimization ACO based on virtual ant colony algorithm[J].Control and Decision, 2019, 34(11):2459-2468.(in Chinese)
[21] ZHANG S J, ZHANG Y.A hybrid genetic and ant colony algorithm for finding the shortest path in dynamic traffic networks[J].Automatic Control and Computer Sciences, 2018, 52(1):67-76.
[22] YANG H, QI J, MIAO Y C, et al.A new robot navigation algorithm based on a double-layer ant algorithm and trajectory optimization[J].IEEE Transactions on Industrial Electronics, 2019, 66(11):8557-8566.
[23] REN J R, CHEN Y, LIU T Q.Ant colony algorithm based on chaos theory and ranking selection in reactive power optimization[J].Power System Technology, 2009, 33(11):49-54.

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