考虑传感噪声的智能网联车队安全风险分析

汪旭; 江欣国; 赵新宇

doi:10.3969/j.issn.0258-2724.20230418

考虑传感噪声的智能网联车队安全风险分析

doi: 10.3969/j.issn.0258-2724.20230418

汪旭^1,,
江欣国^{1, 2, 3, ,},
赵新宇¹

1.
西南交通大学交通运输与物流学院，四川成都 611756
2.
西南交通大学综合交通运输智能化国家地方联合工程实验室，四川成都 611756
3.
西南交通大学综合交通大数据应用技术国家工程实验室，四川成都 611756

基金项目: 国家自然科学基金项目（72271207）

详细信息

作者简介:
汪旭（1994—），男，博士研究生，研究方向为交通信息管理与智能控制，E-mail：wangxu@my.swjtu.edu.cn

通讯作者:
江欣国（1975—），男，教授，研究方向为交通安全规划与设计，E-mail： xjiang@swjtu.edu.cn

中图分类号: U491
计量
- 文章访问数: 54
- HTML全文浏览量: 32
- PDF下载量: 5
- 被引次数: 0
出版历程
- 收稿日期: 2023-08-21
- 修回日期: 2024-08-05
- 网络出版日期: 2025-08-28

Safety Risk Analysis of Connected and Automated Vehicle Platoons Considering Sensor Noise

WANG Xu^1
,,
JIANG Xinguo^{1, 2, 3
, ,},
ZHAO Xinyu¹

1.
School of Transportation and Logistics, Southwest Jiaotong University, Chengdu 611756, China
2.
National United Engineering Laboratory of Integrated and Intelligent Transportation, Southwest Jiaotong University, Chengdu 611756, China
3.
National Engineering Laboratory of Integrated Transportation Big Data Application Technology, Southwest Jiaotong University, Chengdu 611756, China

摘要

摘要:
针对智能网联车辆（connected and automated vehicles, CAV）之间车车（vehicle-to-vehicle, V2V）通信失败情况下采用车载传感设备感知前车运动状态的场景，分析传感噪声对智能网联车队安全风险的影响. 首先，基于智能驾驶员模型（intelligent driver model, IDM）构建CAV车辆动力学模型，提出CAV车辆感知前车运动状态的2种模式；随后，分析出现噪声的原因，并采用自适应卡尔曼滤波算法（adaptive Kalman filter, AKF）对噪声进行处理；最后，开展智能网联车队头车突然减速（极端场景）和基于NGSIM （next generation simulation）的实车数据集（常规场景）仿真实验，采用替代安全评价指标TIT （time integrated time-to-collision）与TET （time exposed time-to-collision）分析不同位置车辆退化和不同车间时距下的车队某一时间段内整体安全风险以及噪声影响. 实验结果表明：去噪声后的TIT和TET均出现显著下降，车队安全风险随车辆退化位置靠后和车间时距增大逐渐降低；当车辆2退化、车间时距为0.6 s时，车队安全风险最大，车辆4及之后的车辆退化时，车队严重和中度安全风险达到最低，此时传感噪声影响不明显，车队安全风险只与车间时距有关.
- 交通控制 /
- 车辆安全 /
- 自适应卡尔曼滤波 /
- 传感噪声 /
- 替代安全评价指标
Abstract:
The scenario in which vehicle-to-vehicle (V2V) communication between connected and automated vehicles (CAVs) fails was studied, and the onboard sensors were used to perceive the motion state of the preceding vehicle. The impact of sensor noise on the safety risk of the CAV platoon was analyzed. First, a CAV dynamics model was established based on the intelligent driver model (IDM), and two sensing modes for perceiving the predecessor’s motion state were proposed. The sources of sensor noise were analyzed, and an adaptive Kalman filter (AKF) was applied for noise processing. Finally, two simulation experiments were conducted under extreme (sudden deceleration of the lead vehicle) and normal (trajectory data based on the NGSIM dataset) scenarios. Surrogate safety metrics, including time integrated time-to-collision (TIT), were used to evaluate the overall platoon safety risk and the effect of noise under different vehicle degradation positions and time headways. The results indicate that after denoising, both TIT and TET significantly decrease. The safety risk of the platoon decreases as the degraded vehicle position moves rearward, and the time headway increases. The highest risk occurs when the second vehicle degrades with a time headway of 0.6 s. When degradation occurs from the fourth vehicle onward, the severe and moderate safety risks are minimized, and the influence of sensor noise becomes negligible. In this case, the safety risk is mainly determined by the time headway.
- traffic control /
- vehicle safety /
- adaptive Kalman filter /
- sensor noise /
- surrogate safety metric

HTML全文

图 1 CAV车辆之间V2V通信

Figure 1. V2V communication between CAVs

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图 2 车载雷达传感器获取前车运动状态

Figure 2. Perception of predecessor’s motion state by onboard radar sensors

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图 3 头车减速场景下的车队各车加速度变化情况

Figure 3. Acceleration changes of each vehicle in platoon under lead vehicle deceleration scenario

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图 4 头车减速场景下CAV不同退化位置对TIT的影响

Figure 4. Impact of different degradation positions on TIT under lead vehicle deceleration scenario

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图 5 头车减速场景下CAV不同退化位置对TET的影响

Figure 5. Impact of different degradation positions on TET under lead vehicle deceleration scenario

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图 6 头车减速场景下不同车间时距对TIT的影响

Figure 6. Impact of different time headways on TIT under lead vehicle deceleration scenario

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图 7 头车减速场景下不同车间时距对TET的影响

Figure 7. Impact of different time headways on TET under lead vehicle deceleration scenario

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图 8 NGSIM数据集场景下车队各车的加速度变化情况

Figure 8. Acceleration changes of each vehicle in platoon under NGSIM dataset scenario

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图 9 NGSIM数据集场景下CAV不同退化位置对TIT的影响

Figure 9. Impact of different degradation positions of CAV on TIT under NGSIM dataset scenario

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图 10 NGSIM数据集场景下CAV不同退化位置对TET影响

Figure 10. Impact of different degradation positions of CAV on TET under NGSIM dataset scenario

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图 11 NGSIM数据集场景下不同车间时距对TIT的影响（第2辆车退化）

Figure 11. Impact of different time headways on TIT under NGSIM dataset scenario （Vehicle 2 degraded）

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图 12 NGSIM数据集场景下不同的车间时距对TET的影响（第2辆车退化）

Figure 12. Impact of different time headways on TET under NGSIM dataset scenario (vehicle 2 degraded)

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表 1 仿真实验参数

Table 1. Simulation experiment parameters

参数	取值	参数描述
$ T $/s	45	仿真时长
$ \Delta t $/s	0.1	仿真步长
$ {a_{\max }} $/（m·s⁻²）	4	车辆最大加速度
$ {d_{\max }} $/（m·s⁻²）	−5	车辆最大减速度
$ d $/（m·s⁻²）	1.5	最大舒适加速度
$ {v_0} $/（m·s⁻¹）	33.3	自由流速度
$ {s_0} $/m	2	最小安全车距
$ l $/m	5	车身长度
$ {q_1},{\text{ }}{q_2} $	0.01，0.01	随机过程噪声
$ {\sigma _1} $/m	0.2	传感噪声车距
${\sigma _2} $/（m·s⁻¹）	0.2	传感噪声车速
$ N $	3	移动时间窗口大小

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表 2 头车减速场景下不同车间时距以及不同退化位置的TIT和TET值

Table 2. TIT and TET values under different time headways and degradation positions under lead vehicle deceleration scenario

车间时距/s	噪声处理	TTC阈值/s	退化位置
			2		3		4		5		6
			TIT	TET	TIT	TET	TIT	TET	TIT	TET	TIT	TET
0.6	去噪前	2.0	0.16	1.8	0.03	0.5	0.03	0.5	0.03	0.5	0.03	0.5
		2.5	0.47	4.9	0.24	3.6	0.15	2.7	0.15	2.7	0.15	2.7
		3.0	1.07	12.0	0.74	11.3	0.58	11.0	0.52	10.6	0.51	10.1
	去噪后	2.0	0.12	1.5	0.03	0.5	0.03	0.5	0.03	0.5	0.03	0.5
		2.5	0.41	4.2	0.19	3.5	0.15	2.7	0.15	2.7	0.15	2.7
		3.0	0.97	11.6	0.65	11.2	0.55	10.8	0.51	10.4	0.51	10.1
0.7	去噪前	2.0	0.10	1.1	0.02	0.5	0.02	0.5	0.02	0.5	0.02	0.5
		2.5	0.30	3.7	0.14	1.9	0.12	1.9	0.12	1.9	0.12	1.9
		3.0	0.70	10.9	0.47	9.7	0.40	9.0	0.40	8.5	0.40	8.5
	去噪后	2.0	0.06	0.9	0.02	0.5	0.02	0.5	0.02	0.5	0.02	0.5
		2.5	0.26	3.2	0.13	1.9	0.12	1.8	0.12	1.8	0.12	1.8
		3.0	0.64	10.1	0.42	9.2	0.40	8.3	0.40	7.9	0.40	7.7
0.9	去噪前	2.0	0.02	0.5	0	0.2	0	0.2	0	0.2	0	0.2
		2.5	0.13	1.7	0.09	1.5	0.09	1.5	0.09	1.5	0.09	1.5
		3.0	0.32	6.4	0.25	5.9	0.25	5.0	0.25	5.0	0.25	5.0
	去噪后	2.0	0.01	0.4	0	0.2	0	0.2	0	0.2	0	0.2
		2.5	0.11	1.4	0.09	1.4	0.09	1.4	0.09	1.4	0.09	1.4
		3.0	0.30	4.6	0.24	4.6	0.25	4.6	0.25	4.6	0.25	4.6
1.1	去噪前	2.0	0	0	0	0	0	0	0	0	0	0
		2.5	0.05	1.3	0.04	1.2	0.04	1.2	0.04	1.2	0.04	1.2
		3.0	0.17	3.6	0.16	2.5	0.16	2.5	0.16	2.5	0.16	2.5
	去噪后	2.0	0	0	0	0	0	0	0	0	0	0
		2.5	0.04	1.1	0.04	1.1	0.04	1.1	0.04	1.1	0.04	1.1
		3.0	0.16	2.2	0.16	2.2	0.16	2.2	0.16	2.2	0.16	2.2

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表 3 NGSIM数据集场景下不同车间时距以及不同退化位置的TIT和TET值

Table 3. TIT and TET values under different time headways and degradation positions under NGSIM dataset scenario

车间时距/s	噪声处理	TTC阈值/s	退化位置
			2		3		4		5		6
			TIT	TET	TIT	TET	TIT	TET	TIT	TET	TIT	TET
0.6	去噪前	2.0	0.10	1.0	0.05	0.5	0.02	0.3	0.02	0.3	0.02	0.3
		2.5	0.29	2.6	0.15	1.9	0.09	1.4	0.09	1.4	0.09	1.4
		3.0	0.67	6.4	0.46	6.0	0.36	5.8	0.33	5.4	0.32	5.4
	去噪后	2.0	0.07	0.8	0.03	0.4	0.02	0.3	0.02	0.3	0.02	0.3
		2.5	0.25	2.2	0.12	1.6	0.09	1.4	0.09	1.4	0.09	1.4
		3.0	0.61	6.2	0.41	5.9	0.34	5.6	0.32	5.4	0.32	5.4
0.7	去噪前	2.0	0.06	0.6	0.01	0.3	0.01	0.3	0.01	0.3	0.01	0.3
		2.5	0.19	2.0	0.09	1.0	0.07	1.0	0.07	1.0	0.07	1.0
		3.0	0.44	5.8	0.29	5.2	0.25	4.8	0.25	4.5	0.25	4.5
	去噪后	2.0	0.04	0.5	0.01	0.3	0.01	0.3	0.01	0.3	0.01	0.3
		2.5	0.16	1.7	0.08	1.0	0.07	1.0	0.07	1.0	0.07	1.0
		3.0	0.40	5.4	0.26	4.9	0.25	4.4	0.25	4.2	0.25	4.1
0.9	去噪前	2.0	0.02	0.3	0	0.1	0	0.1	0	0.1	0	0.1
		2.5	0.08	0.9	0.05	0.8	0.05	0.8	0.05	0.8	0.05	0.8
		3.0	0.20	3.4	0.15	3.1	0.15	2.7	0.15	2.7	0.15	2.7
	去噪后	2.0	0.01	0.2	0	0.1	0	0.1	0	0.1	0	0.1
		2.5	0.07	0.7	0.05	0.7	0.05	0.7	0.05	0.7	0.05	0.7
		3.0	0.19	2.5	0.15	2.5	0.15	2.5	0.15	2.5	0.15	2.5
1.1	去噪前	2.0	0	0	0	0	0	0	0	0	0	0
		2.5	0.04	0.7	0.03	0.6	0.03	0.6	0.03	0.6	0.03	0.6
		3.0	0.11	1.9	0.10	1.3	0.10	1.3	0.10	1.3	0.10	1.3
	去噪后	2.0	0	0	0	0	0	0	0	0	0	0
		2.5	0.03	0.6	0.03	0.6	0.03	0.6	0.03	0.6	0.03	0.6
		3.0	0.10	1.2	0.10	1.2	0.10	1.2	0.10	1.2	0.10	1.2

下载: 导出CSV

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