自动驾驶卡车路测安全员接管干预行为解析

doi:10.13229/j.cnki.jdxbgxb.20220553

吉林大学学报(工学版) ›› 2024, Vol. 54 ›› Issue (3): 727-740.doi: 10.13229/j.cnki.jdxbgxb.20220553

• 交通运输工程·土木工程 • 上一篇

自动驾驶卡车路测安全员接管干预行为解析

涂辉招¹(),王万锦¹,乔鹏²,郭静秋¹(),鹿畅¹,吴海飞³

^1.同济大学道路与交通工程教育部重点实验室，上海 201804
^2.上海临港科技创新城经济发展有限公司，上海 201306
^3.上研智联智能出行科技（上海）有限公司，上海 201306

收稿日期:2022-05-12 出版日期:2024-03-01 发布日期:2024-04-18
通讯作者: 郭静秋 E-mail:huizhaotu@tongji.edu.cn;guojingqiu@hotmail.com
作者简介:涂辉招（1977-），男，教授，博士.研究方向：智能网联汽车与智慧交通，交通风险管理，交通行为分析.E-mail：huizhaotu@tongji.edu.cn
基金资助:
国家重点研发计划项目(2019YFE0108300);上海市社科规划基金项目(2020JG008-BCK782);CAAC Safety Grant(OMSA2103)

Analysis of drivers′ intervention behavior in autonomous truck road testing

Hui-zhao TU¹(),Wan-jin WANG¹,Peng QIAO²,Jing-qiu GUO¹(),Chang LU¹,Hai-fei WU³

^1.Key Laboratory of Road and Traffic Engineering，Ministry of Education，Tongji University，Shanghai 201804，China
^2.Shanghai Lingang Science and Technology Innovation City Economic Development Co. ，Ltd. ，Shanghai 201306，China
^3.SICVIC （Shanghai） Co. ，Ltd. ，Shanghai 201306，China

Received:2022-05-12 Online:2024-03-01 Published:2024-04-18
Contact: Jing-qiu GUO E-mail:huizhaotu@tongji.edu.cn;guojingqiu@hotmail.com

摘要/Abstract

摘要：

为解析和量化评估自动驾驶卡车路测安全员接管干预行为的有效性和安全性，以卡车速度、加速度及角速度等运行指标构造特征参数，联合自动编码机（AE）与K-means++算法构建复合模型，辨识出急减速、急加速、横向右转、横向左转和稳定接管等5种干预运行模式；综合人-车-路-环境多重因素，将接管干预原因分为避险接管、信任接管及优化接管3个类型；针对不同运行模式，结合时变特征分析其接管控制时间并辨析接管原因。基于自动驾驶卡车路测数据，提取了778个接管干预样本，评估验证方法的合理性和有效性。结果表明，受场景风险程度等要素影响，路测阶段急减速和稳定接管干预运行模式的接管控制时间明显少于其他接管干预运行模式；避险接管主要为急减/加速接管干预运行模式，信任接管多表现为横向右转/左转及稳定接管干预运行模式，优化接管仅有急加速接管干预运行模式；避险及信任接管类型占比大于80%，表明安全员在自动驾驶卡车路测中仍以确保路测安全为首要目标。

关键词: 交通运输系统工程, 接管干预行为, 自动编码机, 接管干预运行模式, 自动驾驶卡车, 自动驾驶路测

Abstract:

In order to analyze and quantitatively evaluate the effectiveness and safety of drivers' intervention behavior in autonomous truck road testing， the characteristic parameters were constructed with the truck speed， acceleration and angular velocity as the operational indicators. Autoencoder （AE） and K-means++ algorithm were combined to build a composite mode to identify five intervention operational patterns， including sharp deceleration， sharp acceleration， transverse right turn， transverse left turn and transverse and smooth intervention. Combining multiple factors of human-vehicle-road-environment when drivers took over and intervened， the intervention reasons were divided into three categories： risk-avoiding intervention， trust intervention and optimized intervention. According to different operational patterns and characteristics variation with time， intervention and control time was analyzed， and the reasons for takeover were identified. Based on autonomous truck road testing data， the 778 intervention samples were extracted to evaluate and verify the rationality and effectiveness of the method. The results show that， affected by factors such as the degree of scene risk， intervention and control time of sharp deceleration and smooth intervention operation pattern is significantly less than other intervention operating patterns in the autonomous truck road testing process； risk-avoiding intervention is mainly the sharp acceleration/deceleration intervention operation pattern， trust intervention is mostly manifested as transverse right/left turning and smooth intervention operation pattern， and optimized intervention only has sharp acceleration intervention operation mode； the types of risk-avoiding intervention and trust intervention account for more than 80%， indicating that drivers still take ensuring the safety of road testing as the primary goal in autonomous truck road testing.

Key words: engineering of traffic and transportation system, intervention behavior, autoencoder, intervention operational pattern, autonomous truck, autonomous vehicle road testing

中图分类号:

U491

涂辉招,王万锦,乔鹏,郭静秋,鹿畅,吴海飞. 自动驾驶卡车路测安全员接管干预行为解析[J]. 吉林大学学报(工学版), 2024, 54(3): 727-740.

Hui-zhao TU,Wan-jin WANG,Peng QIAO,Jing-qiu GUO,Chang LU,Hai-fei WU. Analysis of drivers′ intervention behavior in autonomous truck road testing[J]. Journal of Jilin University(Engineering and Technology Edition), 2024, 54(3): 727-740.

图/表 16

表1

表2

表3

表4

特征参数提取矩阵"

特征参数	$y ? 1$	$y ? 2$
$V a l d$	0.91	-4.81^*
$V a t d$	-4.31^*	-1.44
$V a a d$	-3.35^*	-1.74

表4

图1

图2

表5

安全员接管干预运行模式阈值及辨别结果"

接管干预运行模式	$V a l d$ /（m·s^-1）	$V a t d$ /（m·s^-1）	$V a a d$ /（rad·s^-1）	频次	比例/%
Ⅰ类-急减速	<-3.43	-0.18~0.23	-0.03（-1.72）~0.05（2.86）	102	13.1
Ⅱ类-急加速	>0.87	-0.18~0.23	-0.03（-1.72）~0.05（2.86）	239	30.7
Ⅲ类-横向右转	-3.43~0.87	<-0.18	<-0.03（-1.72）	36	4.7
Ⅳ类-横向左转	-3.43~0.87	>0.23	>0.05（2.86）	64	8.2
Ⅴ类-稳定接管	-3.43~0.87	-0.18~0.23	-0.03（-1.72）~0.05（2.86）	337	43.3

表5

图3

图4

图5

表6

图6

图7

图8

表7

表8

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要素类别		具体要素内容
碰撞风险	车辆是否存在碰撞风险	①是	②否
安全员状态	安全员是否掌控方向盘	①是	②否
	安全员精神状态	①谨慎（背部挺直，眼睛时刻观测道路状态，双手紧挨方向盘）
		②正常（背部后靠，定期观测道路，双手放在腿上）
		③放松（玩手机、与副驾驶聊天，极少观测道路）
	安全员动作	①加速	④双手放置方向盘后未进行其他操作
		②制动	⑤其余动作
		③转动方向盘
测试车状态	车辆速度	①不变	②加速	③减速
	车辆状态	①正常	②软件失效	③硬件失效
	车辆驾驶模式	①自动驾驶	②人工驾驶
交通及环境要素	测试区域	①快速路	②城市道路	③封闭场道路
	路段	①直线段	④匝道	⑦收费口
		②合流主线	⑤曲线段
		③分流主线	⑥交叉口
	道路设施情况	①良好	②中	③差
	交通流情况/（pcu·h^-1）	①<20	③150~300	⑤>600
	交通流情况/（pcu·h^-1）	②20~150	④300~600
	周边车辆状况	①无车辆	③周边车辆变道	⑤其余状况
	周边车辆状况	②周边车辆超车	④前方车辆制动
	天气状况	①晴天	④下雨	⑦其他天气状况
		②多云	⑤下雪
		③阴天	⑥雾或霾
	日夜情况	①白天	②夜晚

主要分类依据		避险接管	信任接管	优化接管
碰撞风险	是否存在碰撞风险	是，周边车辆存在紧急变化，如超车、制动、变道等	否	否
安全员状态	接管前安全员状态	谨慎	谨慎	正常
安全员状态	接管时是否改变车辆状态（速度或方向）	是	-	是
测试车状态	接管前测试车辆是否有软硬件失效	-	否	否
测试车状态	接管后行驶速度	-	降低或不变	提升
交通及环境状态	交通流量/（pcu·h^-1）	≥300	<300	<300
	接管时所处路段	-	直线段、交叉口、合/分流主线	直线段、交叉口
	接管时周边车辆数（安全车距范围内）	≥2	<2	0

字段名称	数据类型	数据示例
车辆编号	string	××号车
时间	datetime	2021-01-29
时间	datetime	10∶11∶12.00
GPS车速/（km·h^-1）	float	61.3
GPS方向/（°）	float	181.1
经度/（°）	float	121.974 755 3
纬度/（°）	float	30.724 545 5
驾驶模式	int	2

接管干预运行模式	最优拟合函数	AIC值	接管控制时间均值/s	接管控制时间标准差/s
Ⅰ类-急减速	对数正态分布	554.4	8.2	4.0
Ⅱ类-急加速	稳定分布	1205.4	13.1	3.8
Ⅲ类-横向右转	稳定分布	225.9	15.0	3.8
Ⅳ类-横向左转	稳定分布	199.4	13.9	4.7
Ⅴ类-稳定接管	对数正态分布	1872.2	8.2	4.6

接管干预运行模式	样本编号	纵向速度	纵向加速度	横向速度	角速度
Ⅰ类-急减速	10	0.98	0.73	-	-
Ⅰ类-急减速	11	0.99	0.67	-	-
Ⅱ类-急加速	93	0.96	0.68	-	-
Ⅱ类-急加速	292	0.99	0.59	-	-
Ⅲ类-横向右转	290	-	-	0.84	0.81
Ⅳ类-横向左转	132	-	-	0.89	0.92

自动驾驶卡车路测安全员接管干预行为解析

Analysis of drivers′ intervention behavior in autonomous truck road testing

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