高速公路無(wú)人駕駛的分層抽樣多動(dòng)態(tài)窗口軌跡規劃算法

張琳; 薛建儒; 馬超; 李庚欣; 李勇強

doi:10.16383/j.aas.c210673

高速公路無(wú)人駕駛的分層抽樣多動(dòng)態(tài)窗口軌跡規劃算法

doi: 10.16383/j.aas.c210673

張琳^1,,
薛建儒^1,,
馬超^1,,
李庚欣^1,,
李勇強^1,

1.
西安交通大學(xué)人工智能與機器人研究所西安 710049

基金項目: 國家自然科學(xué)基金(62036008, 61773311)資助

詳細信息

作者簡(jiǎn)介:
張琳：2021年獲得西安交通大學(xué)人工智能與機器人研究所碩士學(xué)位. 主要研究方向為無(wú)人駕駛智能決策與運動(dòng)規劃. E-mail: zhanglin9668@stu.xjtu.edu.cn

薛建儒：博士, 西安交通大學(xué)人工智能與機器人研究所教授. 主要研究方向為計算機視覺(jué), 模式識別與機器學(xué)習, 無(wú)人駕駛與混合增強智能. 本文通信作者. E-mail: jrxue@mail.xjtu.edu.cn

馬超：2018年獲得西安交通大學(xué)人工智能與機器人研究所博士學(xué)位. 主要研究方向為無(wú)人駕駛運動(dòng)規劃與控制的統計學(xué)習方法. E-mail: machao0919@stu.xjtu.edu.cn

李庚欣：西安交通大學(xué)人工智能與機器人研究所博士研究生. 主要研究方向為強化學(xué)習, 無(wú)人駕駛智能決策與運動(dòng)規劃. E-mail: ligengxin@stu.xjtu.edu.cn

李勇強：西安交通大學(xué)人工智能與機器人研究所博士研究生. 主要研究方向為強化學(xué)習, 無(wú)人駕駛智能決策與運動(dòng)規劃, 微觀(guān)交通動(dòng)力學(xué)仿真. E-mail: keaijile321@163.com

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出版歷程
- 收稿日期: 2021-02-14
- 錄用日期: 2021-08-04
- 網(wǎng)絡(luò )出版日期: 2022-01-03
- 刊出日期: 2024-07-23

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

1.
Institute of Artificial Intelligence and Robotics, Xi＇an Jiaotong University, Xi＇an 710049

Funds: Supported by National Natural Science Foundation of China (62036008, 61773311)

More Information

Author Bio:
ZHANG Lin　Received her master degree from the Institute of Artificial Intelligence and Robotics, Xi＇an Jiaotong University in 2021. Her research interest covers decision making and motion planning for autonomous driving

XUE Jian-Ru　Ph.D., professor at the Institute of Artificial Intelligence and Robotics, Xi＇an Jiaotong University. His research interest covers computer vision, pattern recognition and machine learning, autonomous driving, and hybrid-augmented intelligence. Corresponding author of this paper

MA Chao　Received his Ph.D. degree from the Institute of Artificial Intelligence and Robotics, Xi＇an Jiaotong University in 2018. His research interest covers statistical learning on the motion planning and the control for autonomous driving

LI Geng-Xin　Ph.D. candidate at the Institute of Artificial Intelligence and Robotics, Xi＇an Jiaotong University. His research interest covers reinforcement learning, decision making and motion planning for autonomous driving

LI Yong-Qiang　Ph.D. candidate at the Institute of Artificial Intelligence and Robotics, Xi＇an Jiaotong University. His research interest covers reinforcement learning, decision making and motion planning for autonomous driving, and microscope traffic dynamics simulation

摘要

摘要: 高速公路無(wú)人駕駛軌跡規劃面臨著(zhù)實(shí)時(shí)性強、安全性高的挑戰. 為此, 提出一種分層抽樣多動(dòng)態(tài)窗口的軌跡規劃算法(Stratified sampling based multi-dynamic window trajectory planner, SMWTP). 首先, 用多動(dòng)態(tài)窗口表征可行軌跡的搜索空間, 并基于貝葉斯網(wǎng)絡(luò )構建軌跡概率分布模型. 其次, 采用先速度后路徑的分層抽樣策略生成符合動(dòng)態(tài)場(chǎng)景約束的候選軌跡集合. 最后, 利用引入障礙車(chē)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(yōu)軌跡. 大量仿真實(shí)驗和實(shí)際交通場(chǎng)景測試驗證了算法的有效性, 對比實(shí)驗結果表明, 所提算法性能顯著(zhù)優(yōu)于人工勢場(chǎng)最優(yōu)軌跡規劃算法和多動(dòng)態(tài)窗口模擬退火軌跡規劃算法.
- 無(wú)人駕駛 /
- 軌跡規劃 /
- 運動(dòng)規劃 /
- 貝葉斯網(wǎng)絡(luò )
Abstract: Autonomous driving trajectory planning on highways faces challenges of strong real-time performance and safety. This paper proposes a stratified sampling based multi-dynamic window trajectory planner (SMWTP) for unmanned vehicles on highway. Firstly, the search space of feasible trajectories is constructed with multi-dynamic windows. Then, the Bayesian network is used to derive the probability distribution model of trajectories. Secondly, the stratified sampling strategy where speed is sampled before path makes generated candidate trajectories meet the constraints in dynamic scenes. Finally, the uncertainty of traffic participant vehicles＇ speed estimation is embedded into responsibility sensitive safety (RSS) model to select the optimal trajectory. A large number of simulation experiments and real traffic scenario tests have verified the effectiveness of the algorithm. The comparative experimental results show that the performance of the proposed algorithm is significantly better than the optimal trajectory planning algorithm based on artificial potential fields and multi-dynamic window simulated annealing-optimized trajectory planning algorithm.
- Autonomous driving /
- trajectory planning /
- motion planning /
- Bayesian network

HTML全文

圖 1 SMWTP算法框圖

Fig. 1 Pipeline of SMWTP

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圖 2 雙車(chē)道多動(dòng)態(tài)窗口模型

Fig. 2 Multi-dynamic window model for two lanes

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圖 3 軌跡的生成式模型

Fig. 3 Trajectory generation model

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圖 4 動(dòng)態(tài)窗口內的累積概率

Fig. 4 Cumulative probability in dynamic window

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圖 5 旁道動(dòng)態(tài)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

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圖 6 當前道動(dòng)態(tài)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

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圖 7 無(wú)人車(chē)相對于車(chē)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 $

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圖 8 期望軌跡候選集示意圖

Fig. 8 Sketch for desired trajectory candidate set

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圖 9 示例場(chǎng)景

Fig. 9 Example scenario

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圖 10 不考慮障礙車(chē)輛速度估計不確定性時(shí)軌跡代價(jià)與生成概率之間的關(guān)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

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圖 11 考慮障礙車(chē)輛速度估計不確定性時(shí)軌跡代價(jià)與生成概率之間的關(guān)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

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圖 12 障礙車(chē)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 (The trajectory shown in the red box is the planned trajectory)

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圖 13 2017年IVFC無(wú)人車(chē)行駛中一段航拍視頻(紅色圓圈中心的機動(dòng)車(chē)為無(wú)人車(chē), (a), (b), (j), (k)為跟車(chē)行駛,(c) ~ (i) 為向右換道, (l) ~ (r)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, (a), (b), (j), (k) show car-following, (c) ~ (i) show lane-right, (l) ~ (r) show lane-left)

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圖 14 2018年IVFC中SMWTP規劃結果示例(橙色矩形為無(wú)人車(chē), 藍色曲線(xiàn)為規劃軌跡)

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

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圖 15 規劃軌跡的安全概率變化

Fig. 15 Safety probability＇s variation of planning trajectories

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圖 16 2019年IVFC中SMWTP規劃結果示例(橙色矩形為無(wú)人車(chē), 藍色曲線(xiàn)為規劃軌跡)

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

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圖 17 規劃軌跡的安全概率變化

Fig. 17 Safety probability＇s variation of planning trajectories

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圖 18 SMWTP規劃重型牽引車(chē)換道軌跡示例(橙色矩形為無(wú)人車(chē), 藍色曲線(xiàn)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)

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圖 19 仿真測試場(chǎng)景

Fig. 19 Simulation scenes for test

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圖 20 虛線(xiàn)車(chē)道線(xiàn)下的縱向安全避讓

Fig. 20 Longitudinal safety avoidance with dashed lane

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圖 21 實(shí)線(xiàn)車(chē)道線(xiàn)下的縱向安全避讓

Fig. 21 Longitudinal safety avoidance with solid lane markings

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圖 22 橫向安全避讓

Fig. 22 Lateral safety avoidance

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圖 23 動(dòng)態(tài)交通流中TP-ATP規劃結果

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

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圖 24 動(dòng)態(tài)交通流中SMWTP規劃結果

Fig. 24 Performance of SMWTP planning results in dynamic traffic flow

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圖 25 動(dòng)態(tài)交通流測試場(chǎng)景

Fig. 25 Dynamic traffic flow for test

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表 1 SMWTP參數設置

Table 1 Parameters of SMWTP

參數名稱(chēng)	參數值
$ k $	1.5
$\sigma_{v}\;({\rm{m/s} })$	2
$\Delta {v}_{\mathrm{thr} }\;({\rm{m/s} })$	5
$\omega _{\mathrm{yawr}} $	20
$\omega _{\mathrm{safe}} $	5
$ \omega _{\mathrm{acc}} $	3
$ \omega _{s1} $	1
$ \omega _{s2} $	0.5

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表 2 TP-ATP參數設置

Table 2 Parameters of TP-ATP

參數名稱(chēng)	參數值
$\omega _{\mathrm{s}} $	5
$\omega _{\mathrmrf50c1hsl6} $	5
$ \omega _{\mathrm{c}} $	0.5
$ \omega _{\mathrm{p}} $	0.005
$ c _{\mathrm{j},\mathrm{s}} $	1
$ c _{\mathrm{v},\mathrm{s}} $	0.2
$ c _{T,\mathrm{s}} $	0.1
$ c _{\mathrm{j},\mathrmrf50c1hsl6} $	1.5
$ c _{T,\mathrmrf50c1hsl6} $	0.1
$ D_0 $	10
$\tau$	4

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表 3 不同障礙車(chē)輛速度估計誤差下的規劃結果

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

$\sigma_{ {\rm{m} } }\; ({\rm{m/s} })$	$v_{\mathrm{g} }\;({\rm{m/s} })$	$s_{\mathrm{g} }\;({\rm{m} })$	$d_{\mathrm{g} }\;({\rm{m} })$	$T\;({\rm{s} })$	$v_{ {\rm{lim} } }\;({\rm{m/s} })$	決策	安全概率 (%)
$0.5$	22.5	167.3	5.60	7.5	25	LC	91.1
$1.0$	20.5	105.0	1.85	5.1	21	LK	95.9

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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

年份	行駛時(shí)長(cháng) $({\rm{min} })$	平均速度 $({\rm{ m/s} })$	平均安全概率(%)	最低安全概率(%)	平均耗時(shí) $({\rm{ms} })$
2018	20	13.8	91.3	80	35.1
2019	30	13.2	93.6	80	33.5

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表 5 虛線(xiàn)車(chē)道線(xiàn)下的縱向安全避讓規劃結果對比

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

場(chǎng)景1	$v_{\mathrm{g} }\;({\rm{m/s} })$	$s_{\mathrm{g} }\;({\rm{m} })$	$d_{\mathrm{g}} \;({\rm{m} })$	$T\;({\rm{s} })$	$v_{ {\rm{lim} } }\;({\rm{m/s} })$	決策
TP-ATP	20.0	111.4	1.85	5.4	20	LK
SMWTP	19.5	160.0	5.60	8.0	25	LC

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表 6 實(shí)線(xiàn)車(chē)道線(xiàn)下的縱向安全避讓規劃結果對比

Table 6 Comparison of planning results for longitudinal safety avoidance with solid lane markings

場(chǎng)景2	$v_{\mathrm{g} }\;({\rm{m/s} })$	$s_{\mathrm{g} }\;({\rm{m} })$	$d_{\mathrm{g} }\;({\rm{m} })$	$T\;({\rm{s} })$	$v_{\mathrm{lim} }\;({\rm{m/s} })$	決策
TP-ATP	20	107	1.85	5.2	20	LK
SMWTP	18	113	1.85	5.6	20	LK

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表 7 橫向安全避讓規劃結果對比

Table 7 Comparison of planning results for lateral safety avoidance

場(chǎng)景3	$v_{\mathrm{g} }\;({\rm{m/s} })$	$s_{\mathrm{g} }\;({\rm{m } })$	$d_{\mathrm{g} }\;({\rm{m } })$	$T\;({\rm{s} })$	$v_{\mathrm{lim} }\;({\rm{m/s} })$	決策
TP-ATP	20.0	80	1.85	4.1	20	LK
SMWTP	19.5	103	1.30	5.3	20	LK

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表 8 動(dòng)態(tài)交通流中TP-ATP多幀規劃結果

Table 8 Performance of TP-ATP multi-frame planning results in dynamic traffic flow

t (s)	$v_{\mathrm{g} }\;({\rm{m/s} })$	$s_{\mathrm{g} }\;({\rm{m} })$	$d_{\mathrm{g} }\;({\rm{m} })$	$T\;({\rm{s} })$	$v_{\mathrm{lim} }\;({\rm{m/s} })$	決策
$0$	21	90	1.85	4.0	21	LK
$12.5$	25	159	5.60	6.9	25	LC

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表 9 動(dòng)態(tài)交通流中SMWTP多幀規劃結果

Table 9 Performance of SMWTP multi-frame planning results in dynamic traffic flow

t (s)	$v_{\mathrm{g} }\;({\rm{m/s} })$	$s_{\mathrm{g} }\;({\rm{m} })$	$d_{\mathrm{g} }\;({\rm{m} })$	$T\;({\rm{s} })$	$v_{\mathrm{lim} }\;({\rm{m/s} })$	決策
$0$	22.8	156.0	5.60	6.7	25.0	LC
$4.5$	26.1	172.0	5.60	7.0	25.0	LK
$24.5$	25.1	169.4	1.85	6.8	33.3	LC

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表 10 SMWTP與SA-TP規劃結果對比

Table 10 Comparison of SMWTP and SA-TP planning results

測試場(chǎng)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-TP	23.4	2.47	136	5.6	5.2	100%	LK
SMWTP	25.0	0.19	150	5.6	5.7	100%	LK

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表 11 SMWTP與SA-TP實(shí)時(shí)性比較

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

測試場(chǎng)景	平均耗時(shí)$({\rm{ms} })$	標準差$({\rm{ms} })$	最大耗時(shí)$({\rm{ms} })$	最小耗時(shí)$({\rm{ms} })$
SA-TP	72	10	99	61
SMWTP	34	2	49	31

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