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  4. Reliable Smart Road Signs
 
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Reliable Smart Road Signs

Journal
IEEE Transactions on Intelligent Transportation Systems
Journal Volume
21
Journal Issue
12
Pages
4995-5009
Date Issued
2020
Author(s)
Sayin M.O
CHUNG-WEI LIN  
Kang E
Shiraishi S
Başar T.
DOI
10.1109/TITS.2019.2946356
URI
https://www.scopus.com/inward/record.uri?eid=2-s2.0-85097246383&doi=10.1109%2fTITS.2019.2946356&partnerID=40&md5=49d99ce7f347c009a0fd4448f73fb147
https://scholars.lib.ntu.edu.tw/handle/123456789/632561
Abstract
In this paper, we propose a game theoretical adversarial intervention detection mechanism for reliable smart road signs. A future trend in intelligent transportation systems is 'smart road signs' that incorporate smart codes (e.g., visible at infrared) on their surface to provide more detailed information to smart vehicles. Such smart codes make road sign classification problem aligned with communication settings more than conventional classification. This enables us to integrate well-established results in communication theory, e.g., error-correction methods, into road sign classification problem. Recently, vision-based road sign classification algorithms have been shown to be vulnerable against (even) small scale adversarial interventions that are imperceptible for humans. On the other hand, smart codes constructed via error-correction methods can lead to robustness against small scale intelligent or random perturbations on them. In the recognition of smart road signs, however, humans are out of the loop since they cannot see or interpret them. Therefore, there is no equivalent concept of imperceptible perturbations in order to achieve a comparable performance with humans. Robustness against small scale perturbations would not be sufficient since the attacker can attack more aggressively without such a constraint. Under a game theoretical solution concept, we seek to ensure certain measure of guarantees against even the worst case (intelligent) attackers that can perturb the signal even at large scale. We provide a randomized detection strategy based on the distance between the decoder output and the received input, i.e., error rate. Finally, we examine the performance of the proposed scheme over various scenarios. © 2000-2011 IEEE.
Subjects
adversarial classification; autonomous driving; certifiable machine learning; Game theory; traffic sign recognition
SDGs

[SDGs]SDG3

[SDGs]SDG11

Other Subjects
Codes (symbols); Error correction; Game theory; Intelligent systems; Correction method; Decoder output; Detection mechanism; Intelligent transportation systems; Random perturbations; Road sign classifications; Smart vehicles; Theoretical solutions; Roads and streets
Type
journal article

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