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Deep Learning-Based Autonomous Driving Systems: A Survey of Attacks and Defenses [article]

Yao Deng, Tiehua Zhang, Guannan Lou, Xi Zheng, Jiong Jin, Qing-Long Han
2021 arXiv   pre-print
The analysis is unrolled by taking an in-depth overview of each step in the ADS workflow, covering adversarial attacks for various deep learning models and attacks in both physical and cyber context.  ...  However, ADSs are still plagued by increasing threats from different attacks, which could be categorized into physical attacks, cyberattacks and learning-based adversarial attacks.  ...  For example, in Baidu Apollo [6] , which is the ADS applied in Baidu Go Robotaxi service [7] , several deep learning models are used in perception and decision modules.  ... 
arXiv:2104.01789v2 fatcat:zekeddt7zzcnrphu3f4yw6vzii

Towards Robust LiDAR-based Perception in Autonomous Driving: General Black-box Adversarial Sensor Attack and Countermeasures [article]

Jiachen Sun, Yulong Cao, Qi Alfred Chen, Z. Morley Mao
2020 arXiv   pre-print
Meanwhile, we take the first step towards exploring a general architecture for robust LiDAR-based perception, and propose SVF that embeds the neglected physical features into end-to-end learning.  ...  Recent studies have demonstrated that LiDAR-based perception is vulnerable to spoofing attacks, in which adversaries spoof a fake vehicle in front of a victim self-driving car by strategically transmitting  ...  Adv-LiDAR assumes that attackers have access to the deep learning model parameters and its pre-and post-processing modules.  ... 
arXiv:2006.16974v1 fatcat:wcer2du5gvegdivzusi5j22rbm

Sensor Data Validation and Driving Safety in Autonomous Driving Systems [article]

Jindi Zhang
2022 arXiv   pre-print
For example, LiDARs and cameras can be compromised by optical attacks, and deep learning models can be attacked by adversarial examples.  ...  In this thesis, we study the detection methods against the attacks on onboard sensors and the linkage between attacked deep learning models and driving safety for autonomous vehicles.  ...  we found that the spatial pyramid structure is more robust under adversarial attacks [70] in Chapter 5, we plan to design countermeasures for deep learning models against adversarial attacks by leveraging  ... 
arXiv:2203.16130v1 fatcat:iasvtakkdrdrjas76a7q5wxawm

Adversarial Sensor Attack on LiDAR-based Perception in Autonomous Driving [article]

Yulong Cao, Chaowei Xiao, Benjamin Cyr, Yimeng Zhou, Won Park, Sara Rampazzi, Qi Alfred Chen, Kevin Fu, Z. Morley Mao
2019 arXiv   pre-print
In contrast to prior work that concentrates on camera-based perception, in this work we perform the first security study of LiDAR-based perception in AV settings, which is highly important but unexplored  ...  In Autonomous Vehicles (AVs), one fundamental pillar is perception, which leverages sensors like cameras and LiDARs (Light Detection and Ranging) to understand the driving environment.  ...  This research was supported in part by an award from Mcity at University of Michigan, by the National Science Foundation under grants CNS-1850533, CNS-1330142, CNS-1526455 and CCF-1628991, by ONR under  ... 
arXiv:1907.06826v1 fatcat:mnqjpnuudvfqpdjjctrkc624he

A Survey on Automated Driving System Testing: Landscapes and Trends [article]

Shuncheng Tang, Zhenya Zhang, Yi Zhang, Jixiang Zhou, Yan Guo, Shuang Liu, Shengjian Guo, Yan-Fu Li, Lei Ma, Yinxing Xue, Yang Liu
2022 arXiv   pre-print
A typical ADS is composed of multiple modules, including sensing, perception, planning and control, which brings together the latest advances in multiple domains.  ...  view on the system, the problems due to the collaborations between modules, and the gaps between ADS testing in simulators and real world; (4) we identify the challenges and opportunities in ADS testing  ...  [25] Perception module in Apollo, PointRCNN [77] and PointPillars [78] Digital dataset Generating adversarial images against multi-sensor fusion based perception [21] Perception module in Apollo  ... 
arXiv:2206.05961v1 fatcat:fxntqw5asvhhzljee62vkmpkme

Evaluating Adversarial Attacks on Driving Safety in Vision-Based Autonomous Vehicles

Jindi Zhang, Yang Lou, Jianping Wang, Kui Wu, Kejie Lu, Xiaohua Jia
2021 IEEE Internet of Things Journal  
The findings of this paper provide a new perspective to evaluate adversarial attacks and guide the selection of deep learning models in autonomous driving.  ...  Specifically, recent studies have demonstrated that adversarial attacks can cause a significant decline in detection precision of deep learning-based 3D object detection models.  ...  attacks, such as attacks against LiDARs.  ... 
doi:10.1109/jiot.2021.3099164 fatcat:fxynfecp7vdhpdtejdzz52nreu

A Probabilistic Approach to Estimating Allowed SNR Values for Automotive LiDARs in "Smart Cities" under Various External Influences

Roman Meshcheryakov, Andrey Iskhakov, Mark Mamchenko, Maria Romanova, Saygid Uvaysov, Yedilkhan Amirgaliyev, Konrad Gromaszek
2022 Sensors  
In addition, the sources analyzed in this paper do not offer methodological support for the design of the LiDAR in the very early stages of their creation, taking into account a priori assessment of the  ...  It has been established that the current works on the analysis of external influences on the LiDARs and methods for their mitigation focus mainly on physical (hardware) approaches (proposing most often  ...  Data Availability Statement: No new data were created or analyzed in this study. Data sharing is not applicable to this article.  ... 
doi:10.3390/s22020609 pmid:35062575 pmcid:PMC8781900 fatcat:mwmnzbn6kvc7diboxj2y5z2v4e

Autonomous Driving with Deep Learning: A Survey of State-of-Art Technologies [article]

Yu Huang, Yue Chen
2020 arXiv   pre-print
Almost at the same time, deep learning has made breakthrough by several pioneers, three of them (also called fathers of deep learning), Hinton, Bengio and LeCun, won ACM Turin Award in 2019.  ...  This is a survey of autonomous driving technologies with deep learning methods.  ...  The goal of attack techniques is to give adversarial examples for the lack of robustness of a DNN.  ... 
arXiv:2006.06091v3 fatcat:nhdgivmtrzcarp463xzqvnxlwq

SoK: On the Semantic AI Security in Autonomous Driving [article]

Junjie Shen, Ningfei Wang, Ziwen Wan, Yunpeng Luo, Takami Sato, Zhisheng Hu, Xinyang Zhang, Shengjian Guo, Zhenyu Zhong, Kang Li, Ziming Zhao, Chunming Qiao (+1 others)
2022 arXiv   pre-print
Unfortunately, today's AI algorithms are known to be generally vulnerable to adversarial attacks.  ...  In this paper, we define such research space as semantic AI security as opposed to generic AI security.  ...  Unfortunately, today's AI algorithms, especially deep learning, are known to be generally vulnerable to adversarial attacks [12, 13] .  ... 
arXiv:2203.05314v1 fatcat:paaqqli33bhx3ez7wbmankyqfq

Seeing isn't Believing: Practical Adversarial Attack Against Object Detectors [article]

Yue Zhao, Hong Zhu, Ruigang Liang, Qintao Shen, Shengzhi Zhang, Kai Chen
2019 arXiv   pre-print
In this paper, we presented systematic solutions to build robust and practical AEs against real world object detectors.  ...  (AA), we proposed the nested-AE, which combines two AEs together to attack object detectors in both long and short distance.  ...  In the early works, AEs are studied only in the digital space, but now the physical adversarial attack against deep learning models attracts more attention.  ... 
arXiv:1812.10217v3 fatcat:d3g4aui2zjgqrjmdhsjtnna3dq

Federated Deep Learning Meets Autonomous Vehicle Perception: Design and Verification [article]

Shuai Wang, Chengyang Li, Qi Hao, Chengzhong Xu, Derrick Wing Kwan Ng, Yonina C. Eldar, H. Vincent Poor
2022 arXiv   pre-print
Realizing human-like perception is a challenge in open driving scenarios due to corner cases and visual occlusions.  ...  To gather knowledge of rare and occluded instances, federated learning empowered connected autonomous vehicle (FLCAV) has been proposed, which leverages vehicular networks to establish federated deep neural  ...  Deep generative adversarial networks can be adopted to close the gap between the digital and physical systems. Autonomous driving under perception uncertainties.  ... 
arXiv:2206.01748v1 fatcat:gnbkko25znam3cks66yh26ajle

Physical Backdoor Attacks to Lane Detection Systems in Autonomous Driving [article]

Xingshuo Han, Guowen Xu, Yuan Zhou, Xuehuan Yang, Jiwei Li, Tianwei Zhang
2022 arXiv   pre-print
Extensive evaluations on public datasets and physical autonomous vehicles demonstrate that our backdoor attacks are effective, stealthy and robust against various defense solutions.  ...  explored, especially in the physical world.  ...  INTRODUCTION The rapid development of deep learning technology has increased the perception capability of autonomous vehicles to interpret the environment and make intelligent actions.  ... 
arXiv:2203.00858v2 fatcat:uwdchbtrvjguvgseowkvabkhz4

Roadmap for Cybersecurity in Autonomous Vehicles [article]

Vipin Kumar Kukkala, Sooryaa Vignesh Thiruloga, Sudeep Pasricha
2022 arXiv   pre-print
Attacks on automotive systems are already on the rise in today's vehicles and are expected to become more commonplace in future autonomous vehicles.  ...  In this article, we discuss major automotive cyber-attacks over the past decade and present state-of-the-art solutions that leverage artificial intelligence (AI).  ...  In [8] , researchers generated various robust visual adversarial perturbations to a stop sign that resulted in it being misidentified as a 45 mph speed limit sign.  ... 
arXiv:2201.10349v1 fatcat:prtoyngmfng3bktjwn2qfjzqae

Deep Learning Serves Traffic Safety Analysis: A Forward-looking Review [article]

Abolfazl Razi, Xiwen Chen, Huayu Li, Hao Wang, Brendan Russo, Yan Chen, Hongbin Yu
2022 arXiv   pre-print
This paper explores Deep Learning (DL) methods that are used or have the potential to be used for traffic video analysis, emphasizing driving safety for both Autonomous Vehicles (AVs) and human-operated  ...  Besides, we investigate connections to the closely related research areas of drivers' cognition evaluation, Crowd-sourcing-based monitoring systems, Edge Computing in roadside infrastructures, Automated  ...  The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.  ... 
arXiv:2203.10939v2 fatcat:oml733wvjfh3blne4h7kg5y3du

On the Integration of Enabling Wireless Technologies and Sensor Fusion for Next-Generation Connected and Autonomous Vehicles

Faran Awais Butt, Jawwad Nasar Chattha, Jameel Ahmad, Muhammad Umer Zia, Muhammad Rizwan, Ijaz Haider Naqvi
2022 IEEE Access  
Communication infrastructure can be vital in transmitting necessary information to peers and receiving critical information for timely decisions.  ...  The article reviews data acquisition using various sensing devices such as RADAR (Radio Detection and Ranging), LiDAR (Light Detection and Ranging), cameras, and multi-modal sensor fusion of the acquired  ...  The amalgamation of deep learning algorithms in traditional LiDAR feature extraction is being studied greatly [179] .  ... 
doi:10.1109/access.2022.3145972 fatcat:lafduphuona2neb3vkbn2y67gu
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