Page 470 - 《软件学报》2025年第4期

1876 软件学报 2025 年第 36 卷第 4 期

[23] Bilik I, Longman O, Villeval S, Tabrikian J. The rise of radar for autonomous vehicles: Signal processing solutions and future research
directions. IEEE Signal Processing Magazine, 2019, 36(5): 20–31. [doi: 10.1109/MSP.2019.2926573]
[24] Xu WY, Yan C, Jia WB, Ji XY, Liu JH. Analyzing and enhancing the security of ultrasonic sensors for autonomous vehicles. IEEE
Internet of Things Journal, 2018, 5(6): 5015–5029. [doi: 10.1109/JIOT.2018.2867917]
[25] Petit J, Stottelaar B, Feiri M, Kargl F. Remote attacks on automated vehicles sensors: Experiments on camera and LiDAR. 2015. https://
api.semanticscholar.org/CorpusID:39608826
[26] Yan C, Xu WY, Liu JH. Can you trust autonomous vehicles: Contactless attacks against sensors of self-driving vehicles. 2016. https://
cyansec.com/files/articles/16DEFCON-Sensor.pdf
[27] Son Y, Shin H, Kim D, Park Y, Noh J, Choi K, Choi J, Kim Y. Rocking drones with intentional sound noise on gyroscopic sensors. In:
Proc. of the 24th USENIX Security Symp. Washington: USENIX Association, 2015.
[28] Trippel T, Weisse O, Xu WY, Honeyman P, Fu K. WALNUT: Waging doubt on the integrity of MEMS accelerometers with acoustic
injection attacks. In: Proc. of the 2017 IEEE European Symp. on Security and Privacy (EuroS&P). Paris: IEEE, 2017. 3–18. [doi: 10.
1109/EuroSP.2017.42]
[29] Warner JS, Johnston RG. A simple demonstration that the global positioning system (GPS) is vulnerable to spoofing. The Journal of
Security Administration, 2002, 25: 19–28.

[30] Volpe JA. Vulnerability assessment of the transportation infrastructure relying on the global positioning system. 2001. https://rosap.ntl.
bts.gov/view/dot/8435
[31] Humphreys TE, Ledvina BM, Psiaki ML, O’Hanlon BW, Kintner PM Jr. Assessing the spoofing threat: Development of a portable GPS
civilian spoofer. In: Proc. of the 21st Int’l Technical Meeting of the Satellite Division of the Institute of Navigation. Savanna, 2008.
[32] Nighswander T, Ledvina B, Diamond J, Brumley R, Brumley D. GPS software attacks. In: Proc. of the 2012 ACM Conf. on Computer
and Communications Security. Raleigh: ACM, 2012. 450–461. [doi: 10.1145/2382196.2382245]
[33] Shin H, Kim D, Kwon Y, Kim Y. Illusion and dazzle: Adversarial optical channel exploits against lidars for automotive applications. In:
Proc. of the 19th Int’l Conf. on Cryptographic Hardware and Embedded Systems. Taipei: Springer, 2017. 445–467.
[34] Mehmood S, Malik AN, Qureshi IM, Khan MZU, Zaman F. A novel deceptive jamming approach for hiding actual target and
generating false targets. Wireless Communications and Mobile Computing, 2021, 2021: 8844630. [doi: 10.1155/2021/8844630]
[35] Buller W, Wilson B, Garbarino J, Kelly J, Subotic N, Thelen B, Belzowski B. Radar congestion study. Technical Report, DOT HS 812
632, Washington: U.S. Department of Transportation, National Highway Traffic Safety Administration, 2018.
[36] Alland S, Stark W, Ali M, Hegde M. Interference in automotive radar systems: Characteristics, mitigation techniques, and current and
future research. IEEE Signal Processing Magazine, 2019, 36(5): 45–59. [doi: 10.1109/MSP.2019.2908214]
[37] Kim G, Mun J, Lee J. A peer-to-peer interference analysis for automotive chirp sequence radars. IEEE Trans. on Vehicular Technology,
2018, 67(9): 8110–8117. [doi: 10.1109/tvt.2018.2848898]
[38] Zeng KX, Liu SN, Shu YC, Wang D, Li HY, Dou YZ, Wang G, Yang YL. All your GPS are belong to us: Towards stealthy
manipulation of road navigation systems. In: Proc. of the 27th USENIX Conf. on Security Symp. Baltimore: USENIX Association,
2018. 1527–1544.
[39] Zhu Y, Miao CL, Hajiaghajani F, Huai MD, Su L, Qiao CM. Adversarial attacks against LiDAR semantic segmentation in autonomous
driving. In: Proc. of the 19th ACM Conf. on Embedded Networked Sensor Systems. Coimbra: Association for Computing Machinery,
2021. 329–342. [doi: 10.1145/3485730.3485935]
[40] Cao YL, Bhupathiraju SH, Naghavi P, Sugawara T, Mao ZM, Rampazzi S. You can’t see me: Physical removal attacks on LiDAR-based
autonomous vehicles driving frameworks. In: Proc. of the 32nd USENIX Security Symp. Anaheim: USENIX Association, 2023.
2993–3010.
[41] Ma C, Wang NF, Chen QA, Shen C. SlowTrack: Increasing the latency of camera-based perception in autonomous driving using
adversarial examples. In: Proc. of the 38th AAAI Conf. on Artificial Intelligence. Vancouver: AAAI, 2024. 4062–4070. [doi: 10.1609/
aaai.v38i5.28200]
[42] Zhu Y, Miao CL, Xue HF, Li ZX, Yu YN, Xu WY, Su L, Qiao CM. TileMask: A passive-reflection-based attack against mmWave radar
object detection in autonomous driving. In: Proc. of the 2023 ACM SIGSAC Conf. on Computer and Communications Security.
Copenhagen: ACM, 2023. 1317–1331. [doi: 10.1145/3576915.3616661]
[43] Cao YL, Wang NF, Xiao CW, Yang DW, Fang J, Yang RG, Chen QA, Liu MY, Li B. Invisible for both Camera and LiDAR: Security
of multi-sensor fusion based perception in autonomous driving under physical-world attacks. In: Proc. of the 2021 IEEE Symp. on
Security and Privacy (SP). San Francisco: IEEE, 2021. 176–194. [doi: 10.1109/SP40001.2021.00076]
[44] Shen JJ, Won JY, Chen ZY, Chen QA. Drift with devil: Security of multi-sensor fusion based localization in high-level autonomous