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高速公路无人驾驶的分层抽样多动态窗口轨迹规划算法

张琳 薛建儒 马超 李庚欣 李勇强

张琳, 薛建儒, 马超, 李庚欣, 李勇强. 高速公路无人驾驶的分层抽样多动态窗口轨迹规划算法. 自动化学报, 2021, 45(x): 1−18 doi: 10.16383/j.aas.c210673
引用本文: 张琳, 薛建儒, 马超, 李庚欣, 李勇强. 高速公路无人驾驶的分层抽样多动态窗口轨迹规划算法. 自动化学报, 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

高速公路无人驾驶的分层抽样多动态窗口轨迹规划算法

doi: 10.16383/j.aas.c210673
基金项目: 国家自然科学基金(U1713217), 国家自然科学基金(61773311)
详细信息
    作者简介:

    张琳:西安交通大学人工智能学院硕士研究生. 2018年于西北工业大学自动化学院获得学士学位. 主要研究方向为无人车决策与运动规划. Email: zhanglin9668@stu.xjtu.edu.cn

    薛建儒:博士. 西安交通大学教授. 主要研究领域为计算机视觉、模式识别与机器学习、无人驾驶与混合增强智能等. 研究成果发表CVPR、ICCV、ECCV、ICRA、IROS等会议和T-PAMI、TIP期刊上发表论文多篇. 曾获ACCV2012最佳应用论文奖和IEEE智能交通学会杰出研究团队奖. Email: jrxue@mail.xjtu.edu.cn

    马超:2018年获得西安交通大学人工智能与机器人研究所博士学位. 主要研究方向为无人驾驶运动规划与控制的统计学习方法. Email: machao0919@stu.xjtu.edu.cn

    李庚欣:西安交通大学人工智能学院博士研究生. 现于西安交通大学电信学部人工智能与机器人研究所视觉认知计算与智能车实验室攻读博士学位. 研究领域为强化学习. 无人车运动规划与智能决策. Email: ligengxin@stu.xjtu.edu.cn

    李勇强:2014年于西安交通大学电气工程学院获控制理论与控制工程学科工学硕士学位, 现于西安交通大学电信学部人工智能与机器人研究所视觉认知计算与智能车实验室攻读博士学位. 研究领域为强化学习, 无人车运动规划与智能决策, 微观交通动力学仿真. 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: 高速公路无人驾驶轨迹规划面临着实时性强、安全性高的挑战. 本文提出了一种分层抽样多动态窗口的轨迹规划算法(Stratied sampling based multi-dynamic window trajectory planner, SMWTP). 首先, 用多动态窗口表征可行轨迹的搜索空间, 并基于贝叶斯网络构建了车辆轨迹分布模型. 其次, 采用先速度后路径的分层抽样策略生成符合动态场景约束的候选轨迹集合. 最后, 利用引入障碍车辆速度估计不确定性的责任敏感安全模型(Responsibility sensitive safety, RSS)从中选择最优轨迹. 大量仿真实验和实际交通场景测试验证了算法的有效性, 对比实验结果表明所提算法性能显著优于人工势场最优轨迹规划算法和多动态窗口模拟退火轨迹规划算法.
  • 图  1  SMWTP算法框图

    Fig.  1  Pipeline of SMWTP

    图  2  双车道多动态窗口模型

    Fig.  2  Multi-dynamic window model for two lanes

    图  3  轨迹的生成式模型

    Fig.  3  Trajectory generation Model

    图  4  动态窗口内的累积概率

    Fig.  4  Cumulative probability in dynamic window

    图  5  旁道窗口内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

    图  6  当前道窗口内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

    图  7  无人车相对于车辆$ c_i $的纵向安全概率

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

    图  8  期望轨迹候选集示意图

    Fig.  8  Sketch for desired trajectory set

    图  9  示例场景

    Fig.  9  Example scenario

    图  10  不考虑障碍车辆速度估计不确定性时轨迹代价与生成概率之间的关系

    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

    图  11  考虑障碍车辆速度估计不确定性时轨迹代价与生成概率之间的关系

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

    图  12  障碍车辆速度估计不确定性对轨迹规划的影响. 红色方框所示轨迹为规划轨迹

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

    图  13  2017年IVFC无人车行驶中一段航拍视频. 图中红色圆圈中心的机动车为无人车, (1)(2)(10)(11)为跟车行驶,(3)−(9) 为向右换道, (12)−(18)为向左换道

    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

    图  14  2018年IVFC中SMWTP规划结果示例. 图中, 橙色矩形为无人车, 蓝色曲线为规划轨迹

    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

    图  15  规划轨迹的安全概率变化

    Fig.  15  Safety probability's variation of trajectories

    图  16  2019年IVFC中SMWTP规划结果示例. 图中, 橙色矩形为无人车, 蓝色曲线为规划轨迹

    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

    图  17  规划轨迹的安全概率变化

    Fig.  17  Safety probability's variation of trajectories

    图  18  SMWTP规划重型牵引车换道轨迹示例. 图中, 橙色矩形为无人车, 蓝色曲线为规划轨迹

    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

    图  19  仿真测试场景

    Fig.  19  Simulation scenes for test

    图  20  虚线车道线下的纵向安全避让

    Fig.  20  Longitudinal risk avoidance with dashed lane marking

    图  21  实线车道线下的纵向安全避让

    Fig.  21  Longitudinal risk avoidance with solid lane marking

    图  22  横向安全避让

    Fig.  22  Lateral risk avoidance

    图  23  动态交通流中TP-ATP规划结果

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

    图  24  动态交通流中SMWTP规划结果

    Fig.  24  Performance of SMWTP in dynamic traffic flow

    图  25  动态交通流测试场景

    Fig.  25  Dynamic traffic flow for test

    表  1  SMWTP参数设置

    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
    下载: 导出CSV

    表  2  TP-ATP参数设置

    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
    下载: 导出CSV

    表  3  不同障碍车辆速度估计误差下的规划结果

    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} $ ${\rm{m/s}}$ 决策安全概率%
    $\sigma_m = 0.5 \; {\rm{m/s}}$22.5167.35.67.525LC91.1
    $\sigma_m = 1 \;{\rm{ m/s}}$20.51051.855.121LK95.9
    下载: 导出CSV

    表  4  2018-2019年IVFC比赛中SMWTP规划情况概览

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

    行驶时长$ {\rm{min}} $平均速度${\rm{ m/s}} $平均安全
    概率%
    最低安全
    概率%
    平均耗时$ {\rm{ms}} $
    20182013.891.38035.1
    20193013.293.68033.5
    下载: 导出CSV

    表  5  虚线车道线下的纵向安全避让规划结果对比

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

    场景一 $v_{\mathrm{g}} $ ${\rm{m/s}}$ $s_{\mathrm{g}} $ ${\rm{m}}$ $d_{\mathrm{g}} $ ${\rm{m}}$ $T $ ${\rm{s}}$ $v_{lim} $ ${\rm{m/s}}$ 决策
    TP-ATP20111.41.855.420LK
    SMWTP19.51605.6825LC
    下载: 导出CSV

    表  6  实线车道线下的纵向安全避让规划结果对比

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

    场景二 $v_{\mathrm{g}} $ ${\rm{m/s}}$ $s_{\mathrm{g}} $ ${\rm{m}}$ $d_{\mathrm{g}} $ ${\rm{m}}$ $T $ ${\rm{s}}$ $v_{\mathrm{lim}} $ ${\rm{ m/s}}$ 决策
    TP-ATP201071.855.220LK
    SMWTP181131.855.620LK
    下载: 导出CSV

    表  7  横向安全避让规划结果对比

    Table  7  Comparison of planning results for lateral risk avoidance

    场景三$v_{\mathrm{g}} $ ${\rm{m/s}}$$s_{\mathrm{g}} $ ${\rm{m }}$$d_{\mathrm{g}} $ ${\rm{m}}$$T $ ${\rm{s}}$$v_{\mathrm{lim}} $ ${\rm{m/s}}$决策
    TP-ATP20801.854.120LK
    SMWTP19.51031.35.320LK
    下载: 导出CSV

    表  8  动态交通流中TP-ATP多帧规划结果

    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}} $ ${\rm{m/s}}$ 决策
    $ t=0 \; {\rm{s}} $21901.854.021LK
    $ t=12.5 \; {\rm{s}} $251595.66.925LC
    下载: 导出CSV

    表  9  动态交通流中SMWTP规划结果

    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}} $ ${\rm{m/s}}$ 决策
    $ 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
    下载: 导出CSV

    表  10  SMWTP与SA-TP规划结果对比

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

    测试场景 $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/%决策
    SA-TP23.42.471365.65.2100LK
    SMWTP250.191505.65.7100LK
    下载: 导出CSV

    表  11  SMWTP与SA-TP实时性比较

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

    算法耗时平均耗时${\rm{ms}}$标准差${\rm{ms}}$最大耗时${\rm{ms}}$最小耗时${\rm{ms}}$
    SA-TP72109961
    SMWTP3424931
    下载: 导出CSV
  • [1] Claussmann L, Revilloud M, Gruyer D, Glaser S. A review of motion planning for highway autonomous driving. IEEE Transactions on Intelligent Transportation Systems, 2020, 21(5):1826-1848 doi: 10.1109/TITS.2019.2913998
    [2] Rasekhipour Y, Khajepour A, Chen S, Litkouhi B. A potential field-based model predictive path-planning controller for autonomous road vehicles. IEEE Transactions on Intelligent Transportation Systems, 2017, 18(5): 1255-1267 doi: 10.1109/TITS.2016.2604240
    [3] Kim D, Kim H, Huh K. Trajectory Planning for Autonomous Highway Driving Using the Adaptive Potential Field. Proceedings of IEEE International Conference on Intelligent Transportation Systems. Maui, HI, USA: IEEE, 2018. 1069−1074
    [4] Wolf M, Burdick J W. Artificial potential functions for highway driving with collision avoidance. Proceedings of IEEE International Conference on Robotics & Automation.California, USA: IEEE, 2008. 3731–3736
    [5] Claussmann L, Revilloud M and Glaser S. Simulated Annealing-optimized Trajectory Planning within Non-Collision Nominal Intervals for Highway Autonomous Driving. Proceedings of International Conference on Robotics and Automation. Montreal, QC, Canada: IEEE, 2019. 5922−5928
    [6] Paden B, Cap M, Yong S Z, Yershov D, Frazzoli E. A Survey of Motion Planning and Control Techniques for Self-Driving Urban Vehicles. IEEE Transactions on Intelligent Vehicles, 2016, 1(1): 33-55 doi: 10.1109/TIV.2016.2578706
    [7] Claussmann L, Revilloud M, Glaser S, Gruyer D. A study on AI-based approaches for high-level decision making in highway autonomous driving. Proceedings of IEEE International Conference on Systems, Man, and Cybernetics (SMC). Banff, Canada: IEEE, 2017. 3671−3676
    [8] Werling M, Ziegler J, Kammel S, Thrun S. Optimal trajectory generation for dynamic street scenarios in a Frenét Frame. Proceedings of IEEE International Conference on Robotics and Automation. Anchorage, AK: IEEE, 2010. 987−993
    [9] 苏锑, 杨明, 王春香, 唐卫, 王冰. 一种基于分类回归树的无人车汇流决策方法[J]. 自动化学报, 2018, 44(1): 35-43

    Su Ti, Yang Ming, Wang Chun-xiang, Tang Wei, Wang Bing. Classification and regression tree based traffic merging for method self-driving vehicles. Acta Automatica Sinica, 2018, 44(1): 35-43
    [10] Ziegler J, Stiller C. Spatiotemporal state lattices for fast trajectory planning in dynamic on-road driving scenarios. Proceedings of IEEE/RSJ International Conference on Intelligent Robots and Systems. St Louis MO, USA: IEEE, 2009. 1879−1884
    [11] Mcnaughton M, Urmson C, Dolan J M, Lee J. Motion planning for autonomous driving with a conformal spatiotemporal lattice. Proceedings of IEEE International Conference on Robotics and Automation. ShangHai, China: IEEE, 2011. 4889−4895
    [12] 袁静妮, 杨林, 唐晓峰, 陈傲文. 基于改进RRT*与行驶轨迹优化的智能汽车运动规划. 自动化学报, 2020, 46(x):1-10

    Yuan Jing-Ni, Yang Lin, Tang Xiao-Fen, Chen Ao-Wen, Autonomous vehicle motion planning based on improved RRT* algorithm and trajectory optimization. Acta Automatica Sinica, 2020, 46(x):1-10
    [13] Yue M, Hou X, Zhao X and Wu X. Robust Tube-Based Model Predictive Control for Lane Change Maneuver of Tractor-Trailer Vehicles Based on a Polynomial Trajectory. IEEE Transactions on Systems, 2020, 50(12): 5180-5188
    [14] Zhou Y, Cholette M E, Bhaskar A and Chung E. Optimal Vehicle Trajectory Planning With Control Constraints and Recursive Implementation for Automated On-Ramp Merging. IEEE Transactions on Intelligent Transportation Systems, 2019, 20(9): 3409-3420 doi: 10.1109/TITS.2018.2874234
    [15] Liu C, Lee S, Varnhagen S, Tseng H E. Path planning for autonomous vehicles using model predictive control. Proceedings of IEEE Intelligent Vehicles Symposium. Los Angeles, USA, 2017. 174–179
    [16] Plessen M, Lima P, Martensson J, Bemporad A, Wahlberg B. Trajectory planning under vehicle dimension constraints using sequential linear programming. Proceedings of IEEE International Conference on Intelligent Transportation Systems. Yokohama, Japan: IEEE, 2017. 1–6
    [17] Werling M, Kammel S, Ziegler J, Gröll L. Optimal Trajectories for Time-Critical Street Scenarios Using Discretized Terminal Manifolds. The International Journal of Robotics Research, 2012, 31(3): 346–359 doi: 10.1177/0278364911423042
    [18] Zhan W, Chen J, Chan C, Liu C and Tomizuka M. Spatially-partitioned environmental representation and planning architecture for on-road autonomous driving. Proceedings of IEEE Intelligent Vehicles Symposium, Los Angeles, USA: IEEE, 2017. 632−639
    [19] Kant K, Zucker S W. Toward Efficient Trajectory Planning: The Path-Velocity Decomposition. The International Journal of Robotics Research, 1986, 5(3): 72-89 doi: 10.1177/027836498600500304
    [20] Gu T Y, Atwood J, Dong C Y, Dolan J M, Lee J W. Tunable and stable real-time trajectory planning for urban autonomous driving. In: Proceedings of IEEE/RSJ International Conference on Intelligent Robots and Systems. Hamburg, Germany: IEEE, 2015. 250−256
    [21] González D, Milanés V, Pérez J and Nashashibi F. Speed profile generation based on quintic Bézier curves for enhanced passenger comfort. Proceedings of IEEE International Conference on Intelligent Transportation Systems. Rio de Janeiro, Brazil : IEEE, 2016: 814−819
    [22] Lima P F, Trincavelli M, Martensson J, Wahlberg B. Clothoid-Based Speed Profiler and Control for Autonomous Driving. Proceedings of IEEE International Conference on Intelligent Transportation Systems. Las Palmas, Spain: IEEE, 2015. 2194−2199
    [23] Liu C, Zhan W and Tomizuka M. Speed profile planning in dynamic environments via temporal optimization. Proceedings of IEEE Intelligent Vehicles Symposium. Los Angeles, USA: IEEE, 2017: 154−159
    [24] Wang Y, Chardonnet J and Merienne F. Speed Profile Optimization for Enhanced Passenger Comfort: An Optimal Control Approach. Proceedings of IEEE International Conference on Intelligent Transportation Systems (ITSC). Maui, USA: IEEE, 2018: 723−728
    [25] Xu W D, Wei J Q, Dolan J M, Zhao H J, Zha H B. A real-time motion planner with trajectory optimization for autonomous vehicles. Proceedings of IEEE International Conference on Robotics and Automation. MN, USA: IEEE, 2012. 2061−2067
    [26] Fan H Y, Zhu F, Liu C, Zhang L, Zhuang L, et al. Baidu Apollo EM Motion Planner.arXiv: Robotics, 2018
    [27] Zheng Z. Recent developments and research needs in modeling lane changing. Transportation Research Part B: Methodological, 2014, 60, 16-32 doi: 10.1016/j.trb.2013.11.009
    [28] 聂建强, 高速公路车辆自主性换道行为建模研究. 东南大学, 中国, 2017.

    Xie J. Research on modelling discretionary lane-changing behaviore of vehicles in freeway[Ph. D. dissertation]. Southeast University, China, 2017
    [29] Shalev-Shwartz S, Shammah S, Shashua A. On a Formal Model of Safe and Scalable Self-driving Cars. arXiv: Robotics, 2017.
    [30] 符锌砂, 胡嘉诚, 何石坚. 基于交通状况及行驶速度的高速公路换道时间研究[J]. 公路交通科技, 2020, 37(04): 133-139

    Fu Xin-Sha, Hu Jia-Cheng, He Shi-Jian. Study on Expressway Lane-changing Time Based on Traffic Condition and Driving Speed. Journal of Highway and Transportation Research and Development. 2020, 37(04): 133-139
    [31] Yang D, Zhu L, Ran B, Pu Y, Hui P. Modeling and Analysis of the Lane-Changing Execution in Longitudinal Direction. IEEE Transactions on Intelligent Transportation Systems, 2016, 17(10): 2984-2992 doi: 10.1109/TITS.2016.2542109
    [32] Toledo T, Zohar D. Modeling Duration of Lane Changes.Transportation Research Record, 2007, 10.3141/1999−08
    [33] K. Kawabata, L. Ma, J. Xue and N. Zheng. A path generation method for automated vehicles based on Bezier curve. Proceedings of IEEE/ASME International Conference on Advanced Intelligent Mechatronics. NSW, Australia: IEEE, 2013. 991−996
    [34] Ziegler J, Bender P, Dang T, Stiller C. Trajectory planning for Bertha — A local, continuous method. Proceedings of IEEE Intelligent Vehicles Symposium. Michigan, USA: IEEE, 2014. 450−457
    [35] Li, Li and Wang, Xiao and Wang et al. Parallel testing of vehicle intelligence via virtual-real interaction. Science Robotics. 2019, 10.1126/scirobotics.aaw4106
    [36] F. -Y. Wang et al. China's 12-Year Quest of Autonomous Vehicular Intelligence: The Intelligent Vehicles Future Challenge Program. IEEE Intelligent Transportation Systems Magazine 2021. 6−19
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出版历程
  • 收稿日期:  2021-02-14
  • 录用日期:  2021-08-04
  • 网络出版日期:  2022-01-03

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