Multi-objective adaptive cruise control (ACC) algorithm for cooperative ACC platooning
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摘要: 針對自動化高速公路（Automated highway system，AHS）車隊穩定性問題，發展了一種多目標自適應巡航控制算法，根據李雅普諾夫（Lyapunov）穩定性理論對該問題進行了量化分析，并給出了同質與異質車隊穩定性的設計要求，基于模型預測控制（Model predictive control，MPC）理論，綜合協調駕駛員期望響應、跟馳安全性、車隊穩定性、車隊整體品質等控制目標，采用加權二次型性能泛函以及線性矩陣不等式約束的形式，將協同式多目標自適應巡航（Adaptive cruise control, ACC）設計問題最終轉化成帶約束的在線凸二次規劃問題。仿真結果表明，相比單車ACC而言，協同ACC的約束空間更為嚴苛，車隊互聯系統穩定性易受車間時距、車隊規模、多目標權重、瞬態工況、車輛異質性等因素的影響，建議在跟馳安全性、車隊穩定性良好的前提下尋求一定的駕乘舒適性與燃油經濟性，以確保車隊整體品質。Abstract: With the rapid progress of the automated highway system, the issue of platoon stability, which might significantly affect highway traffic characteristics, such as traffic efficiency, traffic capacity, and traffic safety, has attracted considerable attention. A string of vehicles equipped with adaptive cruise control (ACC) and moving longitudinally in an automated manner is regarded as an autonomous vehicle platooning system. During car following, the quality of the ride could be poor and rear-end collisions could occur, particularly if the spacing and velocity errors are amplified to some extent as they propagate upstream. Research on platoon stability has been the focus of significant interest. However, a method to coordinate multiple sub-objectives dynamically during autonomous vehicle platooning against multiple traffic scenarios has not yet been developed. In this study, a multi-objective ACC algorithm for cooperative adaptive cruise control (CACC) platooning based on vehicle-to-vehicle (V2V) real-time communication technology, which enabled the interconnection of vehicles within a limited range to share vehicle position and motion state information, was thus proposed. The quantization of homogeneous and heterogeneous platoon stability was analyzed on the basis of the Lyapunov stability theory. Furthermore, on the basis of the model predictive control framework, the coordination among various conflicting sub-objectives, such as driver-desired car-following response, rear-end safety, platoon stability, and platoon overall quality, was comprehensively considered. Then, by utilizing a quadratic cost function with linear multi-constraints, the design of the multi-objective CACC was transformed into the convex quadratic programming problem with multiple constraints. The comparative simulations show that the I/O constraints and slack relaxation of platoon control are strict, indicating that platoon stability is easily affected by certain factors, such as time gap, platoon size, sub-objective weight coefficient, transient traffic scenarios, and heterogeneous features. Thus, rear-end safety and platoon stability should be prioritized to guarantee the overall quality of the platoon.
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圖 1 搭載V2V模塊的CACC車隊示意圖
Figure 1. Sketch of CACC platoon equipped with V2V real-time communication technology
圖 4 時距
${\tau _i}$ 對同質車隊響應的影響. （a）τi=2.0 s；（b）τi=1.5 s；（c）τi=1.0 s；（d）τi=0.5 sFigure 4. Homogeneous platoon response with time gap
${\tau _i}$ : (a) τi=2.0 s; (b) τi=1.5 s; (c) τi=1.0 s; (d) τi=0.5 s
圖 5 目標權重
${w_{\Delta {d_i}}}$ 對同質車隊響應的影響. （a）${w_{\Delta {d_i}}} = 0.01$ ；（b）${w_{\Delta {d_i}}} = 0.1$ ；（c）${w_{\Delta {d_i}}} = 1.0$ Figure 5. Homogeneous platoon response with
${w_{\Delta {d_i}}}$ : (a)${w_{\Delta {d_i}}} = 0.01$ ; (b)${w_{\Delta {d_i}}} = 0.1$ ; (c)${w_{\Delta {d_i}}} = 1.0$ 
圖 6 目標權重
${w_{{j_i}}}$ 對同質車隊響應的影響. （a）${w_{{j_i}}} = 0.0006$ ；（b）${w_{{j_i}}} = 0.001$ ；（c）${w_{{j_i}}} = 0.0012$ Figure 6. Homogeneous platoon response with
${w_{{j_i}}}$ : (a)${w_{{j_i}}} = 0.0006$ ; (b)${w_{{j_i}}} = 0.001$ ; (c)${w_{{j_i}}} = 0.0012$ 
圖 7 時距
${\tau _i}$ 對同質車隊響應的影響. （a）τi=2.0 s；（b）τi=1.5 s；（c）τi=1.0 s；（d）τi=0.5 sFigure 7. Homogeneous platoon response with time gap
${\tau _i}$ : (a) τi=2.0 s; (b) τi=1.5 s; (c) τi=1.0 s; (d) τi=0.5 s
圖 8 時距τi=0.5 s時同質車隊速度傳播情況
Figure 8. Propagation velocities of the homogeneous platoon when
${\tau _i}$ =0.5 s
圖 9 時距
${\tau _i}$ 對同質車隊響應的影響. （a）τi=2.0 s；（b）τi=1.5 s；（c）τi=1.0 s；（d）τi=0.5 sFigure 9. Homogeneous platoon response with time gap
${\tau _i}$ : (a) τi=2.0 s; (b) τi=1.5 s; (c) τi=1.0 s; (d) τi=0.5 s
圖 10 異質車隊車距誤差傳播情況. （a）組Ⅰ；（b）組Ⅱ
Figure 10. Propagation spacing errors of the heterogeneous platoon: (a) group I; (b) group II
圖 11 在組Ⅰ時距
${\tau _i}$ 下異質車隊響應. （a）車距誤差；（b）車速Figure 11. Heterogeneous platoon response for group I with the preset time gap: (a) spacing error; (b) velocity
圖 12 在組Ⅱ時距
${\tau _i}$ 下異質車隊響應. （a）車距誤差；（b）車速Figure 12. Heterogeneous platoon response for group II with the preset time gap: (a) spacing error; (b) velocity
圖 13 在組Ⅰ時距
${\tau _i}$ 下異質車隊響應. （a）車距誤差；（b）實際車距Figure 13. Heterogeneous platoon response for group I with the preset time gap: (a) spacing error; (b) actual spacing
圖 14 在組Ⅱ時距
${\tau _i}$ 下異質車隊響應. （a）車距誤差；（b）實際車距Figure 14. Heterogeneous platoon response for group II with the preset time gap: (a) spacing error; (b) actual spacing
表 1 控制算法仿真參數
Table 1. Parameters of the CACC platoon
${K_{i,{\rm{L}}}}$ ${T_{\rm{s}}}$ ${k_{\rm{d}}}$ ${k_{\rm{v}}}$ ${t_{{\rm{TTC}}}}$ ${d_0}$ ${d_{{\rm{cr}}}}$ p 1.0 0.1 0.02 0.25 ?3 5.0 5.0 5 Ncr ?0 ρi ${w_{\Delta {v_i}}}$ ${w_{{a_{i,{\rm{des}}}}}}$ $w_{{c_i}}$ yi,max yi,min 11 0.75 diag(3,3,3) 3.0 0.1 0.01 [5.0,1.0,0.6]T [?5.0,?1.0,?0.6]T ui,max ui,min Δui,max Δui,min ${\upsilon }_{\max }^{{y_i}}$ ${\upsilon }_{\min }^{{y_i}}$ $\upsilon _{\max }^{{u_i}}$ $\upsilon _{\min }^{{u_i}}$ 0.6 ?0.6 0.1 ?0.1 [3.0,1.0,0.1]T [?3.0,?1.0,?0.1]T 0.1 ?0.1 $\upsilon _{\max }^{\Delta {u_i}}$ $\upsilon _{\min }^{\Delta {u_i}}$ 0.01 ?0.01 
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表 2 同質車隊仿真參數
Table 2. Parameters of the homogeneous platoon
Group No. ${T_{i,{\rm{L}}}}$ ${T_{i,{\rm{D}}}}$ ${\tau _i}$ Ⅰ 0.40 0 2.0 Ⅱ 0.40 0 1.5 Ⅲ 0.40 0 1.0 Ⅳ 0.40 0 0.5
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中文字幕在线观看表 3 異質車隊仿真參數
Table 3. Parameters of the heterogeneous platoon
Vehicle No. ${T_{i,{\rm{L}}}}$ ${T_{i,{\rm{D}}}}$ ${\tau _i}$ Group Ⅰ Group Ⅱ 1 0.40 0 1.5 1.5 2 0.40 0 1.5 1.5 3 0.36 0 1.2 1.2 4 0.36 0 1.2 1.2 5 0.60 0 2.0 1.0 6 0.60 0 2.0 1.0 7 0.55 0 1.8 1.1 8 0.55 0 1.8 1.1 9 0.40 0 1.0 0.5 10 0.40 0 1.0 0.5
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參考文獻
[1] Xiao L Y, Gao F. Practical string stability of platoon of adaptive cruise control vehicles. IEEE Trans Intell Transp Syst, 2011, 12(4): 1184 doi: 10.1109/TITS.2011.2143407 [2] Kayacan E. Multiobjective H∞ control for string stability of cooperative adaptive cruise control systems. IEEE Trans Intell Transp Syst, 2017, 2(1): 52 [3] Filho C M, Terra M H, Wolf D F. Safe optimization of highway traffic with robust model predictive control-based cooperative adaptive cruise control. IEEE Trans Intell Transp Syst, 2017, 18(11): 3193 doi: 10.1109/TITS.2017.2679098 [4] Fernandes P, Nunes U. Multiplatooning leaders positioning and cooperative behavior algorithms of communicant automated vehicles for high traffic capacity. IEEE Trans Intell Transp Syst, 2015, 16(3): 1172 doi: 10.1109/TITS.2014.2352858 [5] Swaroop D, Rajagopal K R. A review of constant time headway policy for automatic vehicle following // 2001 IEEE Intelligent Transportation Systems Conference Proceedings. Oakland, 2001: 65 [6] Wu G Q, Zhang L X, Liu Z Y, et al. Research status and development trend of vehicle adaptive cruise control systems. J Tongji Univ Nat Sci, 2017, 45(4): 544吳光強, 張亮修, 劉兆勇, 等. 汽車自適應巡航控制系統研究現狀與發展趨勢. 同濟大學學報: 自然科學版, 2017, 45(4):544 [7] Zhang J H, Li Q, Chen D P. Drivers imitated multi-objective adaptive cruise control algorithm. Control Theory Appl, 2018, 35(6): 769 doi: 10.7641/CTA.2017.70585章軍輝, 李慶, 陳大鵬. 仿駕駛員多目標決策自適應巡航魯棒控制. 控制理論與應用, 2018, 35(6):769 doi: 10.7641/CTA.2017.70585 [8] Zhao J, Masahiro O, Wang T, et al. Impacts of the ACC system on highway traffic safety and capacity. China Mech Eng, 2007, 18(12): 1496 doi: 10.3321/j.issn:1004-132X.2007.12.026趙津, 大屋勝敬, 王婷, 等. 自適應巡航系統對高速公路交通安全及流量的影響. 中國機械工程, 2007, 18(12):1496 doi: 10.3321/j.issn:1004-132X.2007.12.026 [9] Bifulco G N, Pariota L, Simonelli F, et al. Development and testing of a fully adaptive cruise control system. Transp Res Part C Emerg Technol, 2013, 29: 156 doi: 10.1016/j.trc.2011.07.001 [10] Zhang J H, Li Q, Chen D P. Integrated adaptive cruise control with weight coefficient self-tuning strategy. Appl Sci, 2018, 8(6): 978 doi: 10.3390/app8060978 [11] Milanes V, Shladover S E, Spring J, et al. Cooperative adaptive cruise control in real traffic situations. IEEE Trans Intell Transp Syst, 2014, 15(1): 296 doi: 10.1109/TITS.2013.2278494 [12] Zhang J H, Li Q, Chen D P. Multi-objective adaptive cruise control with multi-mode strategy. J Univ Electron Sci Technol China, 2018, 47(3): 368 doi: 10.3969/j.issn.1001-0548.2018.03.008章軍輝, 李慶, 陳大鵬. 車輛多模式多目標自適應巡航控制. 電子科技大學學報, 2018, 47(3):368 doi: 10.3969/j.issn.1001-0548.2018.03.008 [13] Li S B, Li K Q, Rajamani R, et al. Model predictive multi-objective vehicular adaptive cruise control. IEEE Trans Control Syst Technol, 2011, 19(3): 556 doi: 10.1109/TCST.2010.2049203 [14] Bageshwar V L, Garrard W L, Rajamani R. Model predictive control of transitional maneuvers for adaptive cruise control vehicles. IEEE Trans Veh Technol, 2004, 53(5): 1573 doi: 10.1109/TVT.2004.833625 [15] Zhang J, Ioannou P A. Longitudinal control of heavy trucks in mixed traffic: Environmental and fuel economy considerations. IEEE Trans Intell Transp Syst, 2006, 7(1): 92 doi: 10.1109/TITS.2006.869597 [16] Boer E, Nicholas W, Michael M, et al. Driver-model based assessment of behavioral adaptation. Trans Soc Autom Eng Japan, 2006, 37(4): 21 [17] Zhang J H, Li Q, Chen D P. Vehicle-to-vehicle based multi-objective coordinated adaptive cruise control considering platoon stability. Adv Mech Eng, 2018, 10(10): 1 -
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