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A non-invasive cyberrisk in cooperative driving

 
: Bapp, F.; Becker, J.; Beyerer, Jürgen; Doll, J.; Filsinger, Max; Frese, Christian; Hubschneider, C.; Lauber, A.; Müller-Quade, J.; Pauli, M.; Roschani, Masoud; Salscheider, O.; Rosenhahn, B.; Ruf, Miriam; Stiller, C.; Willersinn, Dieter; Ziehn, J.

:
Volltext urn:nbn:de:0011-n-4871780 (1.8 MByte PDF)
MD5 Fingerprint: d0fd7f58292f94bc1925b2ec7573f7f7
Erstellt am: 13.3.2018


TU München, Lehrstuhl für Fahrzeugtechnik:
Einführung hochautomatisiertes Fahren. 8. Tagung Fahrerassistenz 2017 : 22.-23. November 2017, München; CD-ROM
München: TÜV SÜD, 2017
8 S.
Tagung Fahrerassistenz <8, 2017, München>
Conference Driver Assistance <8, 2017, Munich>
Englisch
Konferenzbeitrag, Elektronische Publikation
Fraunhofer IOSB ()

Abstract
This paper presents a hacking risk arising in fully automated cooperative driving. As opposed to common cyber risk scenarios, this scenario does not require internal access to an automated car at all, and is therefore largely independent of current on-board malware protection. A hacker uses a wireless mobile device, for example a hacked smartphone, to send vehicle to- vehicle (V2V) signals from a human-driven car, masquerading it as a fully-automated, cooperating vehicle. It deliberately engages only in high-risk cooperative maneuvers with other cars, in which the unwitting human driver is expected to perform a specific maneuver to avoid collisions with other vehicles. As the human driver is unaware of the planned maneuver, he fails to react as expected by the other vehicles; depending on the situation, a severe collision risk can ensue. We propose a vision-based countermeasure that only requires state-of-the-art equipment for fully-automated vehicles, and assures that such an attack without internal access to an automated car is impossible.

: http://publica.fraunhofer.de/dokumente/N-487178.html