Localization estimation algorithm under cyber delay attack for autonomous vehicle based on LGSVL/Apollo

doi:10.3969/j.issn.1674-8484.2021.01.006

Abstract

Abstract:

An unbiased finite impulse response filter (UFIR) under delay model was proposed to improve the accuracy of autonomous vehicle location estimation algorithm under delayed attack. A vehicle kinematics model under delayed attack was established and extended to a finite length time window. A batch and iterative forms of UFIR algorithms were derived. The embedding position of the algorithm was selected by analyzing the data flow of Apollo functional modules. A co-simulation test platform was built based on LG Silicon Valley Lab (LGSVL) Simulator and Apollo system, and conducted experiments. The results show that compared with the Kalman filter (KF), the algorithm has higher estimation accuracy, faster response speed, smaller fluctuation amplitude, and stronger robustness when the delay data changes greatly. The estimation effect is great when the data delay time is less than or equal to 1 s. Therefore, the result verifies the feasibility of the autonomous driving simulation experiment based on LGSVL and Apollo system.

Key words: autonomous vehicle, location estimation algorithm, Apollo system, LGSVL (LG Silicon Valley Lab) simulator, unbiased finite impulse response (UFIR), cyber-attack, delay attack

CLC Number:

U46
TN92

FENG Minjian, ZHANG Hui, JU Zhiyang, XU Qing. Localization estimation algorithm under cyber delay attack for autonomous vehicle based on LGSVL/Apollo[J]. Journal of Automotive Safety and Energy, 2021, 12(1): 62-69.

Figures/Tables 12

References 22

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采样时间,τ	10 ms
时间窗口长度,N	65 ms
按批式窗口长度,K	4 ms
行驶速度,v	7 m/s
速度波动值,?v	2 m/s
真实定位测量精度,μ	10 mm

采样时间,τ	10 ms
时间窗口长度,N	65 ms
按批式窗口长度,K	4 ms
行驶速度,v	7 m/s
速度波动值,?v	2 m/s
真实定位测量精度,μ	10 mm

数据延迟时间/ s	平均估计误差/ m
数据延迟时间/ s	KF	UFIR
0.1	0.198	0.155
0.2	0.216	0.180
0.5	0.418	0.328
1.0	1.383	0.747
1.2	1.957	1.027
1.5	14.905	6.952

数据延迟时间/ s	平均估计误差/ m
数据延迟时间/ s	KF	UFIR
0.1	0.198	0.155
0.2	0.216	0.180
0.5	0.418	0.328
1.0	1.383	0.747
1.2	1.957	1.027
1.5	14.905	6.952