Journal of Jilin University(Engineering and Technology Edition) ›› 2024, Vol. 54 ›› Issue (3): 589-599.doi: 10.13229/j.cnki.jdxbgxb.20230685

   

Data⁃filtering method for driving behavior based on vehicle shared autonomy

Zhen-hai GAO1,2(),Rong-gui CAI1,2,Tian-jun SUN1,2(),Tong YU1,2,Hao-yuan ZHAO1,Hao BAN3   

  1. 1.College of Automotive Engineering，Jilin University，Changchun 130022，China
    2.National Key Laboratory of Automotive Chassis Integration and Bionics，Jilin University，Changchun 130022，China
    3.Changsha Automobile Innovation Research Institute，Jilin University，Changsha 410016，China
  • Received:2023-07-01 Online:2024-03-01 Published:2024-04-18
  • Contact: Tian-jun SUN E-mail:gaozh@jlu.edu.cn;sun_tj@jlu.edu.cn

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Abstract:

Aiming at the problems of over-redundancy of low-quality data， difficult mining of feature data and time-consuming manual recording in the traditional data acquisition and analysis process， a driving behavior data filtering method based on dynamic time regularization algorithm under man-machine co-driving was proposed. Firstly， a dynamic time warping algorithm model was built in Python environment to realize real-time deviation calculation of two sequences by means of rolling time window. Then， considering the statistical characteristics of different distance calculation methods， the deviation threshold of triggering records was designed， and the model was optimized with the global constraint of structured path. Finally， simulation analysis and real vehicle test were conducted to compare the data filtering methods under different constraints. It was found that the proposed filtering method based on Sakoe-Chiba constraints can automatically filter out an average of 53.15% of invalid data in the data preparation stage， saving 1.87 TB of data storage space per hour， the effectiveness and feasibility of the proposed method is verified.

Key words: vehicle engineering, autonomous vehicle, intelligent vehicle shared autonomy, data-driven, dynamic time warping

CLC Number: 

  • TP29

Fig.1

System upgrade with driving behavior data-driven"

Fig.2

Digital quantification diagram of driving behavior"

Fig.3

Main technical process of research"

Fig.4

Distance measurement between ED and DTW"

Fig.5

Path global constraint diagram for DTW"

Fig.6

Technical flow of “human-vehicle”control instruction difference analysis model"

Fig.7

Actual vehicle data collection platform"

Fig.8

Differential data filtering effect of four models in the first set of offline tests"

Fig.9

Differential data filtering effect of four models in the second set of offline tests"

Table 1

Summary table of total hours of discrepancy data recording"

组别测试时间/s模型1记录时间/s模型2记录时间/s模型3记录时间/s模型4记录时间/s
均值104.6058.8053.9858.7154.37
184.0031.3427.3430.4027.94
2117.0072.5061.8875.0062.30
3105.0076.9469.6473.6870.16
496.0034.7433.7435.2033.74
5121.0078.4877.3079.2677.72

Table 2

Summary of data filtering rates"

组别数据滤除率/%
模型1模型2模型3模型4
均值45.2849.8445.4749.46
162.6967.4563.8166.74
238.0347.1135.9046.75
326.7233.6829.8333.18
463.8164.8563.3364.85
535.1436.1234.5035.77

Fig.10

Results of Sakoe-Chiba global constraint DTW"

1 Shin S, Kang H, Kwon S. A Study on Data analysis for improving driving safety in field operational test (FOT) of autonomous vehicles[J]. Machines (Basel), 2022, 10(9): No. 784.
2 Hiller J, Koskinen S, Berta R, et al. The L3Pilot data management toolchain for a level 3 vehicle automation pilot[J]. Electronics (Basel), 2020, 9(5): No. 809.
3 邓伟文, 李江坤, 任秉韬, 等. 面向自动驾驶的仿真场景自动生成方法综述[J]. 中国公路学报,2022,35(1): 316-333.
Deng Wei-wen, Li Jiang-kun, Ren Bing-tao, et al. A survey on automatic simulation scenario generation methods for autonomous driving[J]. China Journal of Highway and Transport, 2022, 35(1): 316-333.
4 Coifman B, Wu M, Redmill K, et al. Collecting ambient vehicle trajectories from an instrumented probe vehicle: High quality data for microscopic traffic flow studies[J]. Transportation Research, Part C, Emerging Technologies, 2016, 72: 254-271.
5 Jordan G. Tesla's new Autopilot will run in "shadow mode" to prove that it's safer than human driving[EB/OL]. [2022-06-28].
6 Anonymity. Tesla shadow mode in autopilot (FSD)[EB/OL]. [2022-06-28].
7 U.S.Department of Transportation. Department of transportation, ensuring american leadership in automated vehicle technologies: automated vehicles 4.0[EB/OL]. [2022-06-28].
8 中华人民共和国工业和信息化部. 工业和信息化部关于加强智能网联汽车生产企业及产品准入管理的意见[EB/OL]. [2023-05-28].
9 Bathaee N, Mohseni A, Park S, et al. A cluster analysis approach for differentiating transportation modes using Bluetooth sensor data[J]. Journal of Intelligent Transportation Systems, 2018, 22(4): 353-364.
10 Folkers A, Rick M, Buskens C, et al. Controlling an autonomous vehicle with deep reinforcement learning[C]∥2019 30th IEEE Intelligent Vehicles Symposium, Paris, France, 2019: 2025-2031.
11 Akagi Y, Kato R, Kitajima S, et al. A risk-index based sampling method to generate scenarios for the evaluation of automated driving vehicle safety[C]∥2019 IEEE Intelligent Transportation Systems Conference, Auckland, New Zealand, 2019: No. 2153.
12 Zhang Z S, Yang D G, Zhang T, et al. A study on the method for cleaning and repairing the probe vehicle data[J]. IEEE Transactions on Intelligent Transportation Systems, 2013, 14(1): 419-427.
13 Gao Z H, Zhu N X, Gao F, et al. A self-learning lane change motion planning system considering the driver's personality[J]. Proceedings of the Institution of Mechanical Engineers, Part D, Journal of Automobile Engineering, 2013, 235(14): 3322-3338.
14 Zhang H, Zhou H J, Sun J, et al. Risk assessment of highly automated vehicles with naturalistic driving data: a surrogate-based optimization method[C]∥2022 IEEE Intelligent Vehicles Symposium, Aachen, Germany, 2022: 580-585.
15 Sun T J, Gao Z H, Gao F, et al. A brain-inspired decision-making linear neural network and its application in automatic drive[J]. Sensors, 2021, 21(3): 794-814.
16 Sun T J, Gao Z H, Chang Z Y, et al. Brain-like intelligent decision-making based on basal ganglia and its application in automatic car-following[J]. Journal of Bionic Engineering, 2021, 18(6): 1439-1451.
17 王殿海, 金盛. 车辆跟驰行为建模的回顾与展望[J]. 中国公路学报, 2012, 25(1): 115-127.
Wang Dian-hai, Jin Sheng. Review and outlook of modeling of car following behavior[J]. China Journal of Highway and Transport, 2012, 25(1): 115-127.
18 贺正冰, 徐瑞康, 谢东繁, 等. 数据驱动跟驰模型综述[J]. 交通运输系统工程与信息, 2021, 21(5):102-113.
He Zheng-bing, Xu Rui-kang, Xie Dong-fan, et al. A review of data-driven car-following models[J]. Journal of Transportation Systems Engineering and Information Technology, 2021, 21(5): 102-113.
19 吴光强, 陶义超, 曾翔. 数据驱动的变速器传感器故障诊断方法[J]. 同济大学学报: 自然科学版, 2021, 49(2): 272-279.
Wu Guang-qiang, Tao Yi-chao, Zeng Xiang. Data-driven fault diagnosis method for transmission sensors[J]. Journal of Tongji University (Natural Science), 2021, 49(2): 272-279.
20 赵健, 宋东鉴, 朱冰, 等. 基于自学习和监督学习混合驱动的智能汽车跟驰控制策略[J].中国公路学报,2022,35(3): 55-65.
Zhao Jian, Song Dong-jian, Zhu Bing, et al. Intelligent vehicle-following control strategy based on self-learning and supervised-learning hybrid-driven framework[J]. China Journal of Highway and Transport, 2022, 35(3): 55-65.
21 Wegener J, van Putten S, Neubeck J,等. 数据挖掘推动客户数据驱动车辆的开发进程[J]. 同济大学学报: 自然科学版, 2021, 49(): 1-10.
Wegener J, van Putten S, Neubeck J,et al. Data mining as an enabler for customer data driven vehicle development[J]. Journal of Tongji University (Natural Science), 2021, 49(Sup.1): 1-10.
22 高振海, 于桐, 孙天骏, 等. 面向无人驾驶的数据采集与分析系统研究综述[J]. 汽车技术, 2021(6): 1-11.
Gao Zhen-hai, Yu Tong, Sun Tian-jun, et al. Review on data acquisition and analysis system for autonomous vehicles[J]. Automobile Technology, 2021(6): 1-11.
23 Gao Z H, Yu T, Sun T J, et al. Data filtering method for intelligent vehicle shared autonomy based on a dynamic time warping algorithm[J]. Sensors, 2022, 22(23): 9436-9453.
24 Koppanyi Z, Toth C K. Experiences with acquiring highly redundant spatial data to support driverless vehicle technologies[J/OL]. [2022-06-28]. Ⅳ-2-161-2018
25 蔡英凤, 陆子恒, 李祎承, 等. 基于多传感器融合的紧耦合SLAM系统[J]. 汽车工程, 2022, 44(3): 350-361.
Cai Ying-feng, Lu Zi-heng, Li Yi-cheng, et al. Tightly coupled SLAM system based on multi-sensor fusion[J]. Automotive Engineering, 2022, 44(3): 350-361.
26 罗玉涛, 秦瀚. 基于稀疏彩色点云的自动驾驶汽车3D目标检测方法[J]. 汽车工程, 2021,43(4):492-500.
Luo Yu-tao, Qin Han. 3D Object detection method for autonomous vehicle based on sparse color point cloud[J]. Automotive Engineering, 2021, 43(4): 492-500.
27 Yi D W, Su J Y, Liu C J, et al. A machine learning based personalized system for driving state recognition[J]. Transportation Research Part C, 2019, 105: 241-261.
28 Gahr B, Liu S, Koch K, et al. Driver identification via the steering wheel[J/OL]. [2022-06-28].
29 Ung N C, Wallace B, Chan A D C, et al. Driver identification using vehicle acceleration and deceleration events from naturalistic driving of older drivers[C]∥2017 IEEE International Symposium on Medical Measurements and Applications, Rochester, USA, 2017:33-38.
30 熊璐, 李志强, 姚杰. 面向低速清扫车的信息融合车辆跟踪方法[J]. 中国公路学报, 2019, 32(6): 61-70.
Xiong Lu, Li Zhi-qiang, Yao Jie. Vehicle tracking method based on information fusion for low-speed sweeper vehicles[J]. China Journal of Highway and Transport, 2019, 32(6): 61-70.
31 高振海, 朱乃宣, 高菲, 等. 考虑驾驶员特性的自学习换道轨迹规划系统[J]. 汽车工程, 2020, 42(12): 1710-1717.
Gao Zhen-hai, Zhu Nai-xuan, Gao Fei, et al. Self-learning lane-change trajectory planning system with driver characteristics[J]. Automotive Engineering, 2020, 42(12): 1710-1717.
32 Itakura F. Minimum prediction residual principle applied to speech recognition[J]. IEEE Transactions on Acoustics, Speech, and Signal Processing, 1975, 23(1): 67-72.
33 Sakoe H, Chiba S. Dynamic programming algorithm optimization for spoken word recognition[J]. IEEE Transactions on Acoustics, Speech, and Signal Processing, 1978, 26(1): 43-49.
34 Rakthanmanon T, Campana B, Mueen A, et al. Searching and mining trillions of time series subsequences under dynamic time warping[C]∥Proceedings of the ACM SIGKDD International Conference on Knowledge, Discovery and Data Mining, Beijing, China, 2012: 262-270.
35 谢枫, 娄静涛, 赵凯, 等. 基于行为识别和曲率约束的车辆轨迹预测方法研究[J]. 汽车工程, 2019, 41(9):1036-1042.
Xie Feng, Lou Jing-tao, Zhao Kai, et al. A research on vehicle trajectory prediction method based on behavior recognition and curvature constraints[J]. Automotive Engineering, 2019, 41(9): 1036-1042.
36 Liu X L, Wang Y F, Jiang K, et al. Interactive trajectory prediction using a driving risk map-integrated deep learning method for surrounding vehicles on highways[J]. IEEE Transactions on Intelligent Transportation Systems, 2022, 23(10): 1-12.
37 Du R L, Liu J Q, Gao L, et al. Long term trajectory prediction based on advanced guidance law recognition[C]∥2017 IEEE International Workshop on Metrology for AeroSpace, Padua, Italy, 2017: 456-461.
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