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              高速公路無人駕駛的分層抽樣多動態窗口軌跡規劃算法

              張琳 薛建儒 馬超 李庚欣 李勇強

              張琳, 薛建儒, 馬超, 李庚欣, 李勇強. 高速公路無人駕駛的分層抽樣多動態窗口軌跡規劃算法. 自動化學報, 2021, 45(x): 1?18 doi: 10.16383/j.aas.c210673
              引用本文: 張琳, 薛建儒, 馬超, 李庚欣, 李勇強. 高速公路無人駕駛的分層抽樣多動態窗口軌跡規劃算法. 自動化學報, 2021, 45(x): 1?18 doi: 10.16383/j.aas.c210673
              Zhang Lin, Xue Jian-Ru, Ma Chao, Li Geng-Xin, Li Yong-Qiang. Stratified sampling based multi-dynamic window trajectory planner for autonomous driving on highway. Acta Automatica Sinica, 2021, 45(x): 1?18 doi: 10.16383/j.aas.c210673
              Citation: Zhang Lin, Xue Jian-Ru, Ma Chao, Li Geng-Xin, Li Yong-Qiang. Stratified sampling based multi-dynamic window trajectory planner for autonomous driving on highway. Acta Automatica Sinica, 2021, 45(x): 1?18 doi: 10.16383/j.aas.c210673

              高速公路無人駕駛的分層抽樣多動態窗口軌跡規劃算法

              doi: 10.16383/j.aas.c210673
              基金項目: 國家自然科學基金(U1713217), 國家自然科學基金(61773311)
              詳細信息
                作者簡介:

                張琳:西安交通大學人工智能學院碩士研究生. 2018年于西北工業大學自動化學院獲得學士學位. 主要研究方向為無人車決策與運動規劃. Email: zhanglin9668@stu.xjtu.edu.cn

                薛建儒:博士. 西安交通大學教授. 主要研究領域為計算機視覺、模式識別與機器學習、無人駕駛與混合增強智能等. 研究成果發表CVPR、ICCV、ECCV、ICRA、IROS等會議和T-PAMI、TIP期刊上發表論文多篇. 曾獲ACCV2012最佳應用論文獎和IEEE智能交通學會杰出研究團隊獎. Email: jrxue@mail.xjtu.edu.cn

                馬超:2018年獲得西安交通大學人工智能與機器人研究所博士學位. 主要研究方向為無人駕駛運動規劃與控制的統計學習方法. Email: machao0919@stu.xjtu.edu.cn

                李庚欣:西安交通大學人工智能學院博士研究生. 現于西安交通大學電信學部人工智能與機器人研究所視覺認知計算與智能車實驗室攻讀博士學位. 研究領域為強化學習. 無人車運動規劃與智能決策. Email: ligengxin@stu.xjtu.edu.cn

                李勇強:2014年于西安交通大學電氣工程學院獲控制理論與控制工程學科工學碩士學位, 現于西安交通大學電信學部人工智能與機器人研究所視覺認知計算與智能車實驗室攻讀博士學位. 研究領域為強化學習, 無人車運動規劃與智能決策, 微觀交通動力學仿真. Email: keaijile321@163.com

              Stratified Sampling Based Multi-Dynamic Window Trajectory Planner for Autonomous Driving on Highway

              Funds: Supported by Natural Science Foundation of China projects (U1713217, 61773311)
              More Information
                Author Bio:

                ZHANG Lin Master student at the College of Artificial Intelligence, Xi'an Jiaotong University. She received her B.S.degree in Automation from Northwestern Polytechnical University. Her research interest covers decision making and motion planning of autonomous driving

                XUE Jian-Ru Ph.D. Professor of Xi'an Jiaotong University. His research interests include Computer Vision, Pattern Recognition and Machine Learning, and Autonomous Driving and Hybrid-Augmented Intelligence. He has published 100+ papers in top cited journals and conferences including IEEE TPAMI, IEEE TIP. CVPR, ICCV, ECCV, etc. He and his team won the IEEE ITSS Institute Lead Award in 2014. and the best application paper award in Asian Conference on Computer Vision 2012

                MA Chao received his PhD in Institute of Artificial Intelligence and Robotics, Xi'an Jiaotong University, Xi'an, China. His research interests include statistical learning on the motion planning and the control for autonomous driving

                LI Geng-Xin Ph.D. candidate at the College of Artificial Intelligence, Xi'an Jiaotong University. He is now studying for a doctorate in visual cognitive computing and intelligent vehicle laboratory, Institute of artificial intelligence and robotics, Department of telecommunications, Xi'an Jiaotong University. His research interests include Reinforcement Learning, decision making and motion planning for autonomous ground vehicles

                LI Yong-Qiang received his M.S degree in Control Theory and Control Engineering from Xi'an Jiaotong university, Xi'an, China, in 2014. He is now studying for a doctorate in visual cognitive computing and intelligent vehicle laboratory, Institute of artificial intelligence and robotics, Department of telecommunications, Xi'an Jiaotong University. His research interests include Reinforcement Learning, decision making and motion planning for autonomous ground vehicles as well as microscope traffic dynamics simulation

              • 摘要: 高速公路無人駕駛軌跡規劃面臨著實時性強、安全性高的挑戰. 本文提出了一種分層抽樣多動態窗口的軌跡規劃算法(Stratied sampling based multi-dynamic window trajectory planner, SMWTP). 首先, 用多動態窗口表征可行軌跡的搜索空間, 并基于貝葉斯網絡構建了車輛軌跡分布模型. 其次, 采用先速度后路徑的分層抽樣策略生成符合動態場景約束的候選軌跡集合. 最后, 利用引入障礙車輛速度估計不確定性的責任敏感安全模型(Responsibility sensitive safety, RSS)從中選擇最優軌跡. 大量仿真實驗和實際交通場景測試驗證了算法的有效性, 對比實驗結果表明所提算法性能顯著優于人工勢場最優軌跡規劃算法和多動態窗口模擬退火軌跡規劃算法.
              • 圖  1  SMWTP算法框圖

                Fig.  1  Pipeline of SMWTP

                圖  2  雙車道多動態窗口模型

                Fig.  2  Multi-dynamic window model for two lanes

                圖  3  軌跡的生成式模型

                Fig.  3  Trajectory generation Model

                圖  4  動態窗口內的累積概率

                Fig.  4  Cumulative probability in dynamic window

                圖  5  旁道窗口內期望速度的概率密度分布

                Fig.  5  Probabilistic density distribution of desired speed in side lane's dynamic window

                圖  6  當前道窗口內期望速度的概率密度分布

                Fig.  6  Probabilistic density distribution of desired speed in current lane's dynamic window

                圖  7  無人車相對于車輛$ c_i $的縱向安全概率

                Fig.  7  The longitudinal safety probability of ego vehicle with respect to vehicle $ c_i $

                圖  8  期望軌跡候選集示意圖

                Fig.  8  Sketch for desired trajectory set

                圖  9  示例場景

                Fig.  9  Example scenario

                圖  10  不考慮障礙車輛速度估計不確定性時軌跡代價與生成概率之間的關系

                Fig.  10  The relationship between the trajectory cost and the generation probability when the uncertainty of the speed estimation of the obstacle is not considered

                圖  11  考慮障礙車輛速度估計不確定性時軌跡代價與生成概率之間的關系

                Fig.  11  The relationship between the trajectory cost and the generation probability when the uncertainty of the speed estimation of the obstacle is considered

                圖  12  障礙車輛速度估計不確定性對軌跡規劃的影響. 紅色方框所示軌跡為規劃軌跡

                Fig.  12  Impact of uncertainty in speed estimation of obstacle vehicles on trajectory planning

                圖  13  2017年IVFC無人車行駛中一段航拍視頻. 圖中紅色圓圈中心的機動車為無人車, (1)(2)(10)(11)為跟車行駛,(3)?(9) 為向右換道, (12)?(18)為向左換道

                Fig.  13  A continuous aerial view of unmanned vehicles driven in IVFC in 2017. The motor vehicle in the center of the red circle is the unmanned vehicle, (1)(2)(10)(11) show car-following, (3)?(9) show lane-right, (12)?(18) show lane-left

                圖  14  2018年IVFC中SMWTP規劃結果示例. 圖中, 橙色矩形為無人車, 藍色曲線為規劃軌跡

                Fig.  14  Performance of SMWTP in IVFC in 2018. The orange rectangle represents ego vehicle, and the blue curve is the trajectory planned by SMWTP

                圖  15  規劃軌跡的安全概率變化

                Fig.  15  Safety probability's variation of trajectories

                圖  16  2019年IVFC中SMWTP規劃結果示例. 圖中, 橙色矩形為無人車, 藍色曲線為規劃軌跡

                Fig.  16  Performance of SMWTP in IVFC in 2019. The orange rectangle represents ego vehicle, and the blue curve is the trajectory planned by SMWTP

                圖  17  規劃軌跡的安全概率變化

                Fig.  17  Safety probability's variation of trajectories

                圖  18  SMWTP規劃重型牽引車換道軌跡示例. 圖中, 橙色矩形為無人車, 藍色曲線為規劃軌跡

                Fig.  18  Lane-change trajectories for heavy tractor planned by SMWTP. The orange rectangle represents ego vehicle, and the blue curve is the planned trajectory

                圖  19  仿真測試場景

                Fig.  19  Simulation scenes for test

                圖  20  虛線車道線下的縱向安全避讓

                Fig.  20  Longitudinal risk avoidance with dashed lane marking

                圖  21  實線車道線下的縱向安全避讓

                Fig.  21  Longitudinal risk avoidance with solid lane marking

                圖  22  橫向安全避讓

                Fig.  22  Lateral risk avoidance

                圖  23  動態交通流中TP-ATP規劃結果

                Fig.  23  Performance of TP-ATP in dynamic traffic flow

                圖  24  動態交通流中SMWTP規劃結果

                Fig.  24  Performance of SMWTP in dynamic traffic flow

                圖  25  動態交通流測試場景

                Fig.  25  Dynamic traffic flow for test

                表  1  SMWTP參數設置

                Table  1  Parameters of SMWTP

                $ k $ $\sigma_{v} $ ${\rm{m/s}}$ $\Delta {v}_{\mathrm{thr}} $ ${\rm{m/s}}$ $\omega _{\mathrm{yawr}} $ $\omega _{\mathrm{safe}} $ $ \omega _{\mathrm{acc}} $ $ \omega _{s1} $ $ \omega _{s2} $
                1.525205310.5
                下載: 導出CSV

                表  2  TP-ATP參數設置

                Table  2  Parameters of TP-ATP

                $\omega _{\mathrm{s}} $$\omega _{\mathrm{d}} $$ \omega _{\mathrm{c}} $$ \omega _{\mathrm{p}} $$ c _{\mathrm{j},\mathrm{s}} $$ c _{\mathrm{v},\mathrm{s}} $
                550.50.00510.2
                $ c _{T,\mathrm{s}} $$ c _{\mathrm{j},\mathrm{d}} $$ c _{T,\mathrm{d}} $$ D_0 $$\tau$
                0.11.50.1104
                下載: 導出CSV

                表  3  不同障礙車輛速度估計誤差下的規劃結果

                Table  3  Planning results with different uncertainty in speed estimation of obstacle vehicles

                $v_{\mathrm{g}} $ ${\rm{m/s}}$ $s_{\mathrm{g}} $ $m$ $d_{\mathrm{g}} $ $s$ $T $ $m$ $v_{lim} $ ${\rm{m/s}}$ 決策安全概率%
                $\sigma_m = 0.5 \; {\rm{m/s}}$22.5167.35.67.525LC91.1
                $\sigma_m = 1 \;{\rm{ m/s}}$20.51051.855.121LK95.9
                下載: 導出CSV

                表  4  2018-2019年IVFC比賽中SMWTP規劃情況概覽

                Table  4  An overview of SMWTP's performance in IVFC in year 2018-2019

                行駛時長$ {\rm{min}} $平均速度${\rm{ m/s}} $平均安全
                概率%
                最低安全
                概率%
                平均耗時$ {\rm{ms}} $
                20182013.891.38035.1
                20193013.293.68033.5
                下載: 導出CSV

                表  5  虛線車道線下的縱向安全避讓規劃結果對比

                Table  5  Comparison of planning results for longitudinal risk avoidance with dashed lane marking

                場景一 $v_{\mathrm{g}} $ ${\rm{m/s}}$ $s_{\mathrm{g}} $ ${\rm{m}}$ $d_{\mathrm{g}} $ ${\rm{m}}$ $T $ ${\rm{s}}$ $v_{lim} $ ${\rm{m/s}}$ 決策
                TP-ATP20111.41.855.420LK
                SMWTP19.51605.6825LC
                下載: 導出CSV

                表  6  實線車道線下的縱向安全避讓規劃結果對比

                Table  6  Comparison of planning results for longitudinal risk avoidance with solid lane marking

                場景二 $v_{\mathrm{g}} $ ${\rm{m/s}}$ $s_{\mathrm{g}} $ ${\rm{m}}$ $d_{\mathrm{g}} $ ${\rm{m}}$ $T $ ${\rm{s}}$ $v_{\mathrm{lim}} $ ${\rm{ m/s}}$ 決策
                TP-ATP201071.855.220LK
                SMWTP181131.855.620LK
                下載: 導出CSV

                表  7  橫向安全避讓規劃結果對比

                Table  7  Comparison of planning results for lateral risk avoidance

                場景三$v_{\mathrm{g}} $ ${\rm{m/s}}$$s_{\mathrm{g}} $ ${\rm{m }}$$d_{\mathrm{g}} $ ${\rm{m}}$$T $ ${\rm{s}}$$v_{\mathrm{lim}} $ ${\rm{m/s}}$決策
                TP-ATP20801.854.120LK
                SMWTP19.51031.35.320LK
                下載: 導出CSV

                表  8  動態交通流中TP-ATP多幀規劃結果

                Table  8  Performance of TP-ATP in dynamic traffic flow

                TP-ATP $v_{\mathrm{g}} $ ${\rm{m/s}}$ $s_{\mathrm{g}} $ ${\rm{m}}$ $d_{\mathrm{g}} $ ${\rm{m}}$ $T $ $s$ $v_{\mathrm{lim}} $ ${\rm{m/s}}$ 決策
                $ t=0 \; {\rm{s}} $21901.854.021LK
                $ t=12.5 \; {\rm{s}} $251595.66.925LC
                下載: 導出CSV

                表  9  動態交通流中SMWTP規劃結果

                Table  9  Performance of SMWTP in dynamic traffic flow

                SMWTP $v_{\mathrm{g}} $ ${\rm{m/s}}$ $s_{\mathrm{g}} $ ${\rm{m}}$ $d_{\mathrm{g}} $ ${\rm{m}}$ $T $ $s$ $v_{\mathrm{lim}} $ ${\rm{m/s}}$ 決策
                $ t=0 \; {\rm{s }}$22.81565.66.725LC
                $ t=4.5 \; {\rm{s}} $26.11725.6725LK
                $ t=24.5 \; {\rm{s}} $25.1169.41.856.833.3LC
                下載: 導出CSV

                表  10  SMWTP與SA-TP規劃結果對比

                Table  10  Comparison of SMWTP and SA-TP's planning results

                測試場景 $v_{\mathrm{g}} $ ${\rm{m/s}}$ $ \sigma_{\mathrm{g}} $ ${\rm{m/s}}$ $s_{\mathrm{g}} $ ${\rm{m}}$ $d_{\mathrm{g}} $ ${\rm{m}}$ $ T $ ${\rm{s}}$ 安全概率/%決策
                SA-TP23.42.471365.65.2100LK
                SMWTP250.191505.65.7100LK
                下載: 導出CSV

                表  11  SMWTP與SA-TP實時性比較

                Table  11  Comparison of SMWTP and SA-TP's real-time performance

                算法耗時平均耗時${\rm{ms}}$標準差${\rm{ms}}$最大耗時${\rm{ms}}$最小耗時${\rm{ms}}$
                SA-TP72109961
                SMWTP3424931
                下載: 導出CSV
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                        出版歷程
                        • 收稿日期:  2021-02-14
                        • 錄用日期:  2021-08-04
                        • 網絡出版日期:  2022-01-03

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