Survey of Predictive Cruise Control for Vehicle Platooning

doi:10.3901/JME.2024.18.218

Abstract

Abstract: Vehicle platoon cruise control is mainly based on local limited traffic environment information. However, high uncertainty of the environment can affect vehicle modeling accuracy and control performance. As an evolution of cruise control, predictive cruise control has become a current research hotspot. In order to comprehensively analyze the research progress of vehicle platoon predictive cruise control, there are summarized four aspects, i.e., traffic environment information prediction, platoon motion behavior decision-making, vehicle trajectory planning, and vehicle trajectory tracking control. Firstly, it is introduced the research progress of vehicle platoon prediction of traffic environment information, including geography and traffic information of its road ahead through vehicle-infrastructure cooperation, and predicting motion states of surrounding vehicles through on-board sensors. The status quo and its trends of trajectory prediction methods based on deep learning are mainly introduced; Secondly, it is introduced the progress of decision-making of cooperative vehicle motion behaviors, while emphatically introducing the important roles of game theory and machine learning in this field, and pointing out trends of motion behaviors decision-making using the combined optimization with physical model and data; Thirdly, aiming at the problem of vehicle cooperative trajectory planning, current researches are sorted out from the perspectives of model-driven and data-driven, while advantages of reinforcement learning in collaborative trajectory planning are illustrated; Then, the problem of vehicle trajectory tracking control is expounded from two aspects of predictive cruise control and vehicle tracking control, respectively, while it is pointed out that the vehicle control method jointly driven by model and data has great application potential; Finally, the status quo and shortcomings of vehicle platoon predictive cruise control are summarized, and future trends in this field are prospected to provide new ideas for its application.

Key words: autonomous driving, vehicle platooning, predictive cruise control, machine learning, jointly driven by model and data

CLC Number:

U469

CHU Duanfeng, LIU Hongxiang, GAO Bolin, WANG Jinxiang, YIN Guodong. Survey of Predictive Cruise Control for Vehicle Platooning[J]. Journal of Mechanical Engineering, 2024, 60(18): 218-246.

References

[1] BIAN Yonggang，ZHENG Yang，REN Wei，et al. Reducing time headway for platooning of connected vehicles via V2V communication[J]. Transportation Research Part C：Emerging Technologies，2019，102：87-105.
[2] HIDALGO C，LATTARULO R，FLORES C，et al. Platoon merging approach based on hybrid trajectory planning and CACC strategies[J]. Sensors，2021，21(8)：2626.
[3] SHLADOVER S E. Path at 20-history and major milestones[J]. IEEE Transactions on Intelligent Transportation Systems，2007，8(4)：584-592.
[4] COELINGH E，SOLYOM S. All aboard the robotic road train[J]. IEEE Spectrum，2012，49(11)：34-39.
[5] TSUGAWA S，KATO S，AOKI K. An automated truck platoon for energy saving[C]// 2011 IEEE/RSJ International Conference on Intelligent Robots and Systems. IEEE，2011：4109-4114.
[6] KIANFAR R，AUGUSTO B，EBADIGHAJARI A，et al. Design and experimental validation of cooperative driving system in grand cooperative driving challenge[J]. IEEE Transactions on Intelligent Transportation Systems，2012，13(3)：994-1007.
[7] NOWAKOWSKI C，SHLADOVER S E，LU Xiaoyun，et al. Cooperative adaptive cruise control (CACC) for truck platooning：Operational concept alternatives[R]. Research Reports No. UCB-ITS-PRR-2015-05：UC Berkeley：California Partners for Advanced Transportation Technology，2015.
[8] MILANÉS V，SHLADOVER S E，SPRING J，et al. Cooperative adaptive cruise control in real traffic situations[J]. IEEE Transactions on Intelligent Transportation Systems，2014，15(1)：296-305.
[9] DEY K C，YAN Li，WANG Xujie，et al. A review of communication，driver characteristics，and controls aspects of cooperative adaptive cruise control (CACC)[J]. IEEE Transactions on Intelligent Transportation Systems，2016，17(2)：491-509.
[10] MILANÉS V，SHLADOVER S E. Modeling cooperative and autonomous adaptive cruise control dynamic responses using experimental data[J]. Transportation Research Part C：Emerging Technologies，2014，48：285-300.
[11] JIA Dongyao，CHEN Haibo，ZHENG Zuduo，et al. An enhanced predictive cruise control system design with data-driven traffic prediction[J]. IEEE Transactions on Intelligent Transportation Systems，2021，23(7)：8170-8183.
[12] ASADI B，VAHIDI A. Predictive cruise control：utilizing upcoming traffic signal information for improving fuel economy and reducing trip time[J]. IEEE Transactions on Control Systems Technology，2011，19(3)：707-714.
[13] TURRI V，BESSELINK B，JOHANSSON K H. Cooperative look-ahead control for fuel-efficient and safe heavy-duty vehicle platooning[J]. IEEE Transactions on Control Systems Technology，2017，25(1)：12-28.
[14] GAO Weinan，ODEKUNLE A，CHEN Yunfeng，et al. Predictive cruise control of connected and autonomous vehicles via reinforcement learning[J]. IET Control Theory & Applications，2019，13(17)：2849-2855.
[15] ZHAI Chunjie，LUO Fei，LIU Yonggui. Cooperative look-ahead control of vehicle platoon for maximizing fuel efficiency under system constraints[J]. IEEE Access，2018，6：37700-37714.
[16] NAIK G，CHOUDHURY B，PARK J M. IEEE 802.11bd & 5G NR V2X：Evolution of radio access technologies for V2X communications[J]. IEEE Access，2019，7：70169-70184.
[17] ZHAI Chunjie，LIU Yonggui，LUO Fei. A switched control strategy of heterogeneous vehicle platoon for multiple objectives with state constraints[J]. IEEE Transactions on Intelligent Transportation Systems，2019，20(5)：1883-1896.
[18] 李克强，常雪阳，李家文，等. 智能网联汽车云控系统及其实现[J]. 汽车工程，2020，42(12)：1595-1605. LI Keqiang，CHANG Xueyang，LI Jiawen，et al. Cloud control system for intelligent and connected vehicles and its application[J]. Automotive Engineering，2020，42(12)：1595-1605.
[19] XIE Guotao，GAO Hongbo，QIAN Lijun，et al. Vehicle trajectory prediction by integrating physics-and maneuver-based approaches using interactive multiple models[J]. IEEE Transactions on Industrial Electronics，2018，65(7)：5999-6008.
[20] HOUENOU A，BONNIFAIT P，CHERFAOUI V，et al. Vehicle trajectory prediction based on motion model and maneuver recognition[C]// 2013 IEEE/RSJ International Conference on Intelligent Robots and Systems. IEEE，2013：4363-4369.
[21] LEFÈVRE S V D，LAUGIER C. A survey on motion prediction and risk assessment for intelligent vehicles[J]. ROBOMECH Journal，2014，1(1)：1-14.
[22] RAJAMANI R. Vehicle dynamics and control[M]. Borlin：Springer Science & Business Media，2011.
[23] KAEMPCHEN N，SCHIELE B，DIETMAYER K. Situation assessment of an autonomous emergency brake for arbitrary vehicle-to-vehicle collision scenarios[J]. IEEE Transactions on Intelligent Transportation Systems，2009，10(4)：678-687.
[24] BRÄNNSTRÖM M，COELINGH E，SJÖBERG J. Model-based threat assessment for avoiding arbitrary vehicle collisions[J]. IEEE Transactions on Intelligent Transportation Systems，2010，11(3)：658-669.
[25] SCHUBERT R R E，WANIELIK G. Comparison and evalution of advanced motion models for vehicle tracking[C]// 200811th International Conference on Information Fusion. IEEE，2008：1-6.
[26] HILLENBRAND J，SPIEKER A M，KROSCHEL K. A multilevel collision mitigation approach—Its situation assessment，decision making，and performance tradeoffs[J]. IEEE Transactions on Intelligent Transportation Systems，2006，7(4)：528-540.
[27] BATZ T，WATSON K，BEYERER J. Recognition of dangerous situations within a cooperative group of vehicles[C]// 2009 IEEE Intelligent Vehicles Symposium. IEEE，2009：907-912.
[28] LEON F，GAVRILESCU M. A review of tracking and trajectory prediction methods for autonomous driving[J]. Mathematics，2021，9(6)：660.
[29] BAHRAM M，HUBMANN C，LAWITZKY A，et al. A combined model-and learning-based framework for interaction-aware maneuver prediction[J]. IEEE Transactions on Intelligent Transportation Systems，2016，17(6)：1538-1550.
[30] CHEN Badong，DANG Lujuan，GU Yuantao，et al. Minimum error entropy Kalman filter[J]. IEEE Transactions on Systems，Man，and Cybernetics：Systems，2021，51(9)：5819-5829.
[31] MOFFAT E，MELNITCHOUK W，ROGERS T C，et al. Simultaneous monte carlo analysis of parton densities and fragmentation functions[J]. Physical Review D，2021，104(1)：016015.
[32] HU Weiming，XIAO Xuejuan，FU Zhouyu，et al. A system for learning statistical motion patterns[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence，2006，28(9)：1450-1464.
[33] ATEV S，MILLER G，PAPANIKOLOPOULOS N P. Clustering of vehicle trajectories[J]. IEEE Transactions on Intelligent Transportation Systems，2010，11(3)：647-657.
[34] BUZAN D，SCLAROFF S，KOLLIOS G. Extraction and clustering of motion trajectories in video[C]// Proceedings of the 17th International Conference on Pattern Recognition，2004. ICPR 2004. IEEE，2004：521-524.
[35] HERMES C，WOHLER C，SCHENK K，et al. Long-term vehicle motion prediction[C]// 2009 IEEE Intelligent Vehicles Symposium. IEEE，2010：652-657.
[36] LAUGIER C，PAROMTCHIK I E，PERROLLAZ M，et al. Probabilistic analysis of dynamic scenes and collision risk assessment to improve driving safety[J]. IEEE Intelligent Transportation Systems Magazine，2011，3(4)：4-19.
[37] LIU A，PENTLAND A. Towards real-time recognition of driver intentions[C]// Proceedings of Conference on Intelligent Transportation Systems. IEEE，1997：236-241.
[38] PENTLAND A，LIU A. Modeling and prediction of human behavior[J]. Neural Computation，1999，11(1)：229-242.
[39] LIU Zhiqiang，PENG Mancai，SUN Yuechen. Estimation of driver lane change intention based on the LSTM and dempster-shafer evidence theory[J]. Journal of advanced transportat
[40] LETHAUS F，BAUMANN M R K，KÖSTER F，et al. A comparison of selected simple supervised learning algorithms to predict driver intent based on gaze data[J]. Neurocomputing，2013，121：108-130.
[41] GAO Jun，MURPHEY Y L，ZHU Honghui. Multivariate time series prediction of lane changing behavior using deep neural network[J]. Applied Intelligence，2018，48(10)：3523-3537.
[42] WANG Xiaoyuan，GUO Yongqing，BAI Chenglin，et al. Driver’s intention identification with the involvement of emotional factors in two-lane roads[J]. IEEE Transactions on Intelligent Transportation Systems，2021，22(11)：6866-6874.
[43] DOSHI A，MORRIS B，TRIVEDI M. On-road prediction of driver's intent with multimodal sensory cues[J]. IEEE Pervasive Computing，2011，10(3)：22-34.
[44] XING Yang，LÜ Chen，WANG Huaji，et al. Driver lane change intention inference for intelligent vehicles：framework，survey，and challenges[J]. IEEE Transactions on Vehicular Technology，2019，68(5)：4377-4390.
[45] 付锐，张海伦，刘文晓，等. 驾驶人意图识别综述[J]. 长安大学学报(自然科学版)，2022(1)：1-28. FU Rui，ZHANG Hailun，LIU Wenxiao，et al. Review on driver intention recognition[J]. Journal of Chang’an University (Natural Science Edition)，2022(1)：1-28.
[46] AOUDE G S，LUDERS B D，LEE K K H，et al. Threat assessment design for driver assistance system at intersections[C]// 13th International Ieee Conference on Intelligent Transportation Systems. IEEE,，2010：1855-1862.
[47] XIE Guotao，GAO Hongbo，HUANG Bin，et al. A driving behavior awareness model based on a dynamic bayesian network and distributed genetic algorithm[J]. International Journal of Computational Intelligence Systems，2018，11(1)：469-482.
[48] TANG Jinjun，LIU Fang，ZHANG Wenhui，et al. Lane-changes prediction based on adaptive fuzzy neural network[J]. Expert Systems with Applications，2018，91：452-463.
[49] ALAHI A，GOEL K，RAMANATHAN V，et al. Social lstm：human trajectory prediction in crowded spaces[C]// Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition，2016：961-971.
[50] HOCHREITER S，SCHMIDHUBER J. Long short-term memory[J]. Neural Computation，1997，9(8)：1735-1780.
[51] KIM B D，KANG C M，KIM J，et al. Probabilistic vehicle trajectory prediction over occupancy grid map via recurrent neural network[C]// 2017 IEEE 20th International Conference on Intelligent Transportation Systems (ITSC). IEEE，2017：399-404.
[52] DEO N，TRIVEDI M M. Multi-modal trajectory prediction of surrounding vehicles with maneuver based lstms[C]// 2018 IEEE Intelligent Vehicles Symposium (IV). IEEE，2018：1179-1184.
[53] DEO N，TRIVEDI M M. Convolutional social pooling for vehicle trajectory prediction[C]// Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition Workshops，2018：1468-1476.
[54] CHANDRA R，BHATTACHARYA U，BERA A，et al. TraPHic：Trajectory prediction in dense and heterogeneous traffic using weighted interactions[C]// Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition，2019：8483-8492.
[55] XIE G，SHANGGUAN A Q，FEI R，et al. Motion trajectory prediction based on a CNN-LSTM sequential model[J]. Science China-Information Sciences，2020，63(11)：1-21.
[56] LI Runmei，ZHONG Zherui，CHAI Jin，et al. Autonomous vehicle trajectory combined prediction model based on CC-LSTM[J]. International Journal of Fuzzy Systems，2022，24(8)：3798-3811.
[57] XIN Long，WANG Pin，CHAN Chingyao，et al. Intention-aware long horizon trajectory prediction of surrounding vehicles using dual lstm networks[C]// 201821st International Conference on Intelligent Transportation Systems (ITSC). IEEE，2018：1441-1446.
[58] 季学武，费聪，何祥坤，等. 基于LSTM网络的驾驶意图识别及车辆轨迹预测[J]. 中国公路学报，2019，32(6)：34-42. JI Xuewu，FEI Cong，HE Xiangkun，et al. Intention recognition and trajectory prediction for vehicle using LSTM network[J]. China Journal of Highway and Transport，2019，32(6)：34-42.
[59] CHOI D，YIM J，BAEK M，et al. Machine learning-based vehicle trajectory prediction using v2v communications and on-board sensors[J]. Electronics，2021，10(4)：420.
[60] HUANG M Y，ZHU M G，XIAO Y P，et al. Bayonet-corpus：A trajectory prediction method based on bayonet context and bidirectional GRU[J]. Digital Communications and Networks，2021，7(1)：72-81.
[61] ZHI Y S，BAO Z P，ZHANG S M，et al. BiGRU based online multi-modal driving maneuvers and trajectory prediction[J]. Proceedings of the Institution of Mechanical Engineers Part D-Journal of Automobile Engineering，2021，235(14)：3431-3441.
[62] SHENG Z H，XU Y M，XUE S B，et al. Graph-based spatial-temporal convolutional network for vehicle trajectory prediction in autonomous driving[J]. IEEE Transactions on Intelligent Transportation Systems，2021，23：17654-17665.
[63] MESSAOUD K，DEO N，TRIVEDI M M，et al. Trajectory prediction for autonomous driving based on multi-head attention with joint agent-map representation[C]// 2021 IEEE Intelligent Vehicles Symposium (IV). IEEE，2021：165-170.
[64] LAI Weicheng，XIA Zixiang，LIN H S，et al. Trajectory prediction in heterogeneous environment via attended ecology embedding[C]// Proceedings of the 28th ACM International Conference on Multimedia，2020：202-210.
[65] 田彦涛，黄兴，卢辉遒，等. 基于注意力与深度交互的周车多模态行为轨迹预测[J]. 吉林大学学报(工学版)，2022，53(5)：1474-1480. TIAN Yantao，HUANG Xing，LU Huiqiu，et al. Vehicle multi-modal maneuvers and trajectory prediction based on attention mechanism with deep interaction[J]. Journal of Jilin University (Engineering and Technology Edition)，2023，53(5)：1474-1480.
[66] YE Luyao，WANG Zezhong，CHEN Xinhong，et al. GSAN：graph self-attention network for learning spatial-temporal interaction representation in autonomous driving[J]. IEEE Internet of Things Journal，2022，9(12)：9190-9204.
[67] VASWANI A，SHAZEER N，PARMAR N，et al. Attention is all you need[J]. Advances in Neural Information Processing Systems，2017(30) ：5998-6008.
[68] CHEN Weihuang，WANG Fangfang，SUN Hongbin. S2TNet：spatio-temporal transformer networks for trajectory prediction in autonomous driving[C]// Asian Conference on Machine Learning. PMLR，2021：454-469.
[69] LIU Yicheng，ZHANG Jinghuai，FANG Liangji，et al. Multimodal motion prediction with stacked transformers[C]// Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition，2021：7577-7586.
[70] LI Xiaolong，XIA Jing，CHEN Xiaoyong，et al. SIT：A spatial interaction-aware transformer-based model for freeway trajectory prediction[J]. ISPRS International Journal of Geo-Information，2022，11(2)：79.
[71] GOODFELLOW I P-A J，MIRZA M，et al. Generative Adversarial Networks[J]. Communications of the Acm，2020，63(11)：139-144.
[72] ZHAO L，LIU Y F，AL-DUBAI AY，et al. A novel generation adversarial network-based vehicle trajectory prediction method for intelligent vehicular networks[J]. IEEE Internet of Things Journal，2021，8(3)：2066-2077.
[73] 温惠英，张伟罡，赵胜. 基于生成对抗网络的车辆换道轨迹预测模型[J]. 华南理工大学学报(自然科学版)，2020，48(5)：32-40. WEN Huiying，ZHANG Weigang，ZHAO Sheng. Vehicle lane-change trajectory prediction model based on generative adversarial networks[J]. Journal of South China University of Technology (Natural Science Edition) ，2020，48(5)：32-40.
[74] HSU C C，KANG L W，CHEN S Y，et al. Deep learning-based vehicle trajectory prediction based on generative adversarial network for autonomous driving applications[J]. Multimedia Tools and Applications，2022，：1-18.
[75] LI X，YING X，CHUAH M C. GRIP：Graph-based interaction-aware trajectory prediction[C]// 2019 IEEE Intelligent Transportation Systems Conference (ITSC). IEEE，2019：3960-3966.
[76] LI Z G J，LU C，et al. Interactive behavior prediction for heterogeneous traffic participants in the urban road：A graph-neural-network-based multitask learning framework[J]. IEEE/ASME Transactions on Mechatronics，2021，26(3)：1339-1349.
[77] LI Xin，YING X，CHUAH M C. Grip++：enhanced graph-based interaction-aware trajectory prediction for autonomous driving[J]. arXiv preprint arXiv，2019，1907.07792.
[78] AMIRLOO E，RASOULI A，LAKNER P，et al. LatentFormer：multi-agent transformer-based interaction modeling and trajectory prediction[J]. arXiv preprint arXiv，2022，2203.01880.
[79] MO Xiaoyu，HUANG Zhiyu，XING Yang，et al. Multi-agent trajectory prediction with heterogeneous edge-enhanced graph attention network[J]. IEEE Transactions on Intelligent Transportation Systems，2022，23(7)：9554-9567.
[80] 侯廉. 面向自动驾驶应用的周车轨迹群体交互式预测[D]. 北京：清华大学，2019. HOU Lian. Distributed and interactive prediction of future trajectories of surrounding vehicles for autonomous driving[D]. Beijing：Tsinghua University，2019.
[81] DIJKSTRA E W. A note on two problems in connexion with graphs[J]. Numerische Mathematik，1959，1(1)：269-271.
[82] LI Yongbo，SOLEIMANI H，ZOHAL M. An improved ant colony optimization algorithm for the multi-depot green vehicle routing problem with multiple objectives[J]. Journal of Cleaner Production，2019，227：1161-1172.
[83] 张新艳，邹亚圣. 基于改进A^*算法的自动导引车无碰撞路径规划[J]. 系统工程理论与实践，2021，41(1)：240-246. ZHANG Xinyan，ZOU Yasheng. Collision-free path planning for automated guided vehicles based on improved A^* algorithm[J]. Systems Engineering-Theory & Practice，2021，41(1)：240-246.
[84] MU Chaoxu，NI Zhen，SUN Changyin，et al. Air-breathing hypersonic vehicle tracking control based on adaptive dynamic programming[J]. IEEE Transactions on Neural Networks and Learning Systems，2017，28(3)：584-598.