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Deep Learning-Based Autonomous Driving Systems: A Survey of Attacks and Defenses
[article]
2021
arXiv
pre-print
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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]
2020
arXiv
pre-print
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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]
2022
arXiv
pre-print
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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]
2019
arXiv
pre-print
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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]
2022
arXiv
pre-print
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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
2021
IEEE Internet of Things Journal
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2021 pre-print arXiv:2108.02940v1 fatcat:qzcwerr3yrhodkc3jrinww7sb4
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
2022
Sensors
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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]
2020
arXiv
pre-print
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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]
2022
arXiv
pre-print
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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]
2019
arXiv
pre-print
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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]
2022
arXiv
pre-print
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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]
2022
arXiv
pre-print
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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]
2022
arXiv
pre-print
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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]
2022
arXiv
pre-print
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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
2022
IEEE Access
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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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