吉林大学学报(工学版) ›› 2022, Vol. 52 ›› Issue (11): 2558-2567.doi: 10.13229/j.cnki.jdxbgxb20210267

• 交通运输工程·土木工程 • 上一篇    

基于典型事故场景的雪天高速换道自动驾驶策略

彭涛1,2(),方锐2,刘兴亮2,王海玮3(),庞彦伟1,许洪国4,刘福聚2,王涛5   

  1. 1.天津大学 电气自动化与信息工程学院，天津 300072
    2.中国汽车技术研究中心有限公司，天津 300300
    3.广东交通职业技术学院 运输与经济管理学院，广州 510650
    4.吉林大学 交通学院，长春 130022
    5.天津职业技术师范大学 汽车与交通学院，天津 300222
  • 收稿日期:2021-03-30 出版日期:2022-11-01 发布日期:2022-11-16
  • 通讯作者: 王海玮 E-mail:pengtao@tute.edu.cn;whw2046@126.com
  • 作者简介:彭涛（1983-），男，副教授，博士.研究方向：交通环境与安全. E-mail： pengtao@tute.edu.cn
  • 基金资助:
    国家重点研发计划项目(2017YFB0102500);国家自然科学基金项目(51808151);天津市多元投入基金项目重点项目(21JCZDJC00700);天津市智能制造专项项目(TJZNZZ1811);汽车安全与节能国家重点实验室开放基金项目(KF2013)

Automatic driving strategy of high⁃speed lane changing in snowy weather based on typical accident scenarios

Tao PENG1,2(),Rui FANG2,Xing-liang LIU2,Hai-wei WANG3(),Yan-wei PANG1,Hong-guo XU4,Fu-ju LIU2,Tao WANG5   

  1. 1.School of Electrical and Information Engineering，Tianjin University，Tianjin 300072，China
    2.China Automotive Technology and Research Center Co. ，Ltd. ，Tianjin 300300，China
    3.School of Transportation and Economic Management，Guangdong Communication Polytechnic，Guangzhou 510650，China
    4.College of Transportation，Jilin University，Changchun 130022，China
    5.College of Automobile and Transportation，Tianjin University of Technology and Education，Tianjin 300222，China
  • Received:2021-03-30 Online:2022-11-01 Published:2022-11-16
  • Contact: Hai-wei WANG E-mail:pengtao@tute.edu.cn;whw2046@126.com

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摘要：

针对雪天低附道路下的车辆高速换道安全问题，提出了一种智能汽车高速安全换道自动驾驶策略。基于典型交通事故案例，研究了车辆雪天高速换道安全特性。构建了Gaussian分布换道轨迹模型，利用决策树和分层加权评分，提出了适应不同驾驶模式的车路协同安全换道决策和路径规划方法。基于PreScan再现交通事故场景，验证低附路面下车辆高速换道智能驾驶策略。仿真结果表明：提出的自动驾驶决策规划机制可有效避免交通事故的发生，可为智能驾驶决策系统开发提供参考。

关键词: 交通运输安全工程, 自动驾驶策略, 雪天, 高速换道, 决策规划, 车路协同

Abstract:

In order to improve the safety of vehicle lane changing on highway in snowy weather， an environment adaptive automatic driving strategy for high-speed safe lane changing of intelligent vehicle was proposed. Based on the typical case of high-speed lane changing traffic accidents， the dynamic mechanism of vehicle instability caused by environment was analyzed， and the lane changing trajectory model was constructed by using Gaussian distribution. Then taking the space-time situation and stability of safe lane changing as constraints， by using decision tree and hierarchical weighted score， a method of cooperative safe lane changing decision and path planning for different driving modes was proposed. Based on the hardware in the loop simulation test platform of intelligent vehicle built by Prescan/Simulink， the traffic accident scene of high-speed lane change of snowy vehicles was used to verify the automatic driving strategy of vehicles on the low attachment road. The simulation results show that the proposed safe lane change decision and path planning mechanism， with vehicle road cooperation， can better ensure the safety and the environmental adaptability of intelligent vehicles， which can provide a reference for the development of intelligent driving decision system.

Key words: engineering communication and transportation safety, automatic driving strategy, snow weather, high-speed lane changing, decision planning, vehicle road cooperation

中图分类号: 

  • U469.2

图1

事故发生地点道路路面和现场情况"

图2

后车与前车碰撞时刻状态"

表1

交通事故涉及车辆相关参数"

车辆参 数数值
客车宽度B0/m2.54
质心至前轴距离b/m2.10
后悬b'/m3.28
货车宽度B1/m2.50

表2

不同工况下车辆最大加速度及最小安全距离"

工况初始车速 /（m·s-1）弯道半径/m制动减速度 /（m·s-2）概率系数转向频率/Hz

a0max

/（m·s-2）

φc（t）max

/rad

SYc（tp）m

/m

tp/s

VX0（tp）

/（m·s-1）

SX0m/mSX1m/mSX2m/m
恒速27.782510—2.250.100.54280.02742.66707.001227.78204.493319.731718.3818
0.150.79980.03962.73235.063227.78150.655717.037816.2307
0.201.12340.05102.79284.096027.78123.786915.693415.1571
0.251.49880.06162.84903.516927.78107.699514.888514.5143
0.301.91410.07152.90133.097627.7896.051314.305714.0488
制动27.7825100.52.250.100.73800.03042.68287.074925.08195.897119.024618.4636
0.150.94320.04252.74755.129625.91147.257117.894616.3044
0.201.22970.05392.80764.158826.33122.275616.545215.2268
0.251.58000.06452.86373.577826.58107.099315.737614.5819
0.301.97830.07442.91593.191526.7596.915515.200714.1531
12.250.101.13780.03412.70277.105922.33185.582910.000018.6632
0.151.28050.04592.76555.149524.00142.244213.506716.4427
0.201.50400.05712.82484.174024.83119.204614.854615.3355
0.251.80180.06762.88043.590425.33104.966015.206014.6731

图3

换道决策树"

表3

自动驾驶汽车雪天高速换道避障路径规划评分表"

层次性能f/a最大加速度/（m·s-2）最小安全距离/m

评价

参数

分值
1总体————s1=s2×（i×s3+j×s4）
2安全—

ai ≤amax

（i=0，1，2）

SXi ≥SXim

（i=0，1，2）

ai

SXi

s2=1（SXi-SXim>0）
s2=0（SXi-SXim≤0）
3舒适0.10/00.5428204.4933a0maxs3=10
0.10/0.50.7380195.8971s3=9
0.15/00.7998150.6557s3=8
0.15/0.50.9432147.2571s3=7
0.20/01.1234123.7869s3=6
0.10/11.1378185.5829s3=5
0.20/0.51.2297122.2756s3=4
0.15/11.2805142.2442s3=3
0.25/01.4988107.6995s3=2
0.20/11.5040119.2046s3=1
0.25/0.51.5800107.0993s3=0.5
4效率0.25/0.51.5800107.0993SX0ms4=10
0.25/01.4988107.6995s4=9
0.20/11.5040119.2046s4=8
0.20/0.51.2297122.2756s4=7
0.20/01.1234123.7869s4=6
0.15/11.2805142.2442s4=5
0.15/0.50.9432147.2571s4=4
0.15/00.7998150.6557s4=3
0.10/11.1378185.5829s4=2
0.10/0.50.7380195.8971s4=1
0.10/00.5428204.4933s4=0.5

表4

自动驾驶汽车换道避障路径规划模式"

模式应用条件转向频率/Hz/ 制动减速度/（m·s-2）分数

优化驾驶策略

转向频率/（Hz）/

制动减速度/（m·s-2）

1SX0≥2000.10/0.56.90.10/0.5
0.15/06.8
0.15/0.56.1
0.20/06.0
0.20/0.54.9
0.10/14.1
0.25/04.1
0.15/13.6
0.20/13.1
0.25/0.53.35
0.10/00
2170≤SX0<2000.15/06.80.15/0
0.15/0.56.1
0.20/06.0
0.20/0.54.9
0.25/04.1
0.15/13.6
0.20/13.1
0.25/0.53.35
0.10/00
0.10/0.50
0.10/10
3140≤SX0<1700.20/06.00.20/0
0.20/0.54.9
0.25/04.1
0.20/13.1
0.25/0.53.35
0.10/00
0.10/0.50
0.10/10
0.15/00
0.15/0.50
0.15/10
4108≤SX0<1400.25/04.10.25/0
0.25/0.53.35
0.10/00
0.10/0.50
0.10/10
0.15/00
0.15/0.50
0.15/10
0.20/00
0.20/0.50
0.20/10
5SX0<108危险

表5

自动驾驶高速换道场景定义参数"

车辆决策反应时间/s（制动/加速 延迟时间）/s侧向运动 延迟时间/s初始速度 /（m·s-1）初始加速度 /（m·s-2）距离/m
自动驾驶汽车0.50.050.427.780

SX0=120

SX1=20

SX2=20

原车道前车—00
目标车道前车0.50.05—250
目标车道后车0.50.05—300

图4

车辆换道避撞转向盘转角变化图"

图5

车辆侧向位置情况"

图6

车辆横向位置情况"

图7

车辆纵向速度变化情况"

图8

自动驾驶汽车换道避撞过程图"

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