D4: Dynamic Data-Driven Discovery of Adversarial Vehicle Maneuvers
Publication Date
1-1-2026
Document Type
Conference Proceeding
Publication Title
Lecture Notes in Computer Science
Volume
15514 LNCS
DOI
10.1007/978-3-031-94895-4_19
First Page
182
Last Page
190
Abstract
Intelligent mechanisms implemented in autonomous vehicles, such as proactive driving assist and pre-collision alerts reduce traffic accidents. However, verifying their correct functionality is difficult due to complex interactions with the environment. This problem is exacerbated in adversarial conditions, where an attacker can control the environment surrounding autonomous vehicles to exploit any vulnerabilities. To preemptively identify vulnerabilities in these systems, in this paper, we implement a scenario-based framework with a formal method to identify the impact of malicious drivers interacting with autonomous vehicles.
Funding Number
FA9550-24-1-0015
Funding Sponsor
U.S. Department of Transportation
Keywords
Adversarial Driving, Applications Systems, DDDAS, Dynamic Data Driven, InfoSymbiotic Systems, Testing
Department
Computer Engineering
Recommended Citation
Carlos Hernandez, Diego Ortiz Barbosa, Zengxiang Lei, Luis Burbano, Younghee Park, Satish Ukkusuri, and Alvaro A. Cardenas. "D4: Dynamic Data-Driven Discovery of Adversarial Vehicle Maneuvers" Lecture Notes in Computer Science (2026): 182-190. https://doi.org/10.1007/978-3-031-94895-4_19
