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Abstract for presentation (Poster or Podium) with a Paper in the Conference Proceedings

Safety, Security, and Standards

Analyzing Traffic Pattern Vulnerabilities in Autonomous Vehicle Cybersecurity

Sunday, June 16, 2024

3:00 PM - 4:15 PM ET

Room: Galleria

Primary Author(s)

Saurav Silwal (he/him/his)

Research Assistant
University of Houston
HOUSTON, TX, United States

Submitter(s)

Lu Gao, n/a

Associate Professor
University of Houston
Plano, Texas, United States

Co-Author(s)

Yunpeng Zhang, n/a

Associate Professor
University of Houston
Houston, Texas, United States
AS

Ahmed Senouci, Ph.D.

Associate Professor of Construction Management, Coordinator of the Construction Management Undergrad
University of Houston
Houston, Texas, United States
Yi-lung Mo, PhD

Professor
University of Houston
Houston, Texas, United States

Co-Author(s)

Lu Gao, n/a

Associate Professor
University of Houston
Plano, Texas, United States

Abstract:

Analyzing Traffic Pattern Vulnerabilities in Autonomous Vehicle Cybersecurity

Saurav Silwal^*, Lu Gao, Yunpeng Zhang, Ahmed Senouci, Yi-Lung Mo

Department of Construction Management, University of Houston, 4800 Calhoun Rd, Houston, TX, USA 77004

^*Email: ssilwal@cougarnet.uh.edu

Abstract
Given the promising future of autonomous vehicles, it is foreseeable that self-driving cars will soon merge as the predominant mode of transportation. While autonomous vehicles offer enhanced efficiency, they remain vulnerable to external attacks. In this research, we sought to investigate the potential impact of cyberattacks on traffic patterns. To achieve this, we conducted simulations where cyberattacks were simulated on connected vehicles by disseminating false information to either a single vehicle or vehicle platoons. The primary objective of this research is to assess the cybersecurity challenges confronting connected and automated vehicles and propose practical solutions to minimize the adverse effects of malicious external information. In the simulation, we have implemented a car-following model for the simulation of self-driving vehicles. This model continually monitors data received from preceding vehicles and optimizes various actions, such as acceleration, deceleration, or lane changes, with the aim of maximizing overall traffic efficiency and safety.

Keywords: Autonomous Vehicle; Connected Vehicle; Car Following Model; Cyberattacks; Traffic flow; Simulation.

Learning Objectives:

Attendees can expect to learn the following from this session:

Upon completion, participants will be able to understand the threats present to autonomous vehicles.
Upon completion, participants will be able to understand how an autonomous vehicle dictates its movement with respect to its surrounding.
Upon completion, participants will be able to understand how connected vehicles increases the efficiency of traffic flow and provides safety.