Assessing Cybersecurity Risks and Traffic Impact in Connected Autonomous Vehicles

TL;DR

This paper investigates the impact of cyberattacks on connected autonomous vehicles by simulating false information dissemination and proposes solutions to mitigate traffic safety and efficiency risks.

Contribution

It introduces a novel car-following simulation model and assesses cybersecurity challenges and mitigation strategies for connected autonomous vehicles.

Findings

Cyberattacks can significantly disrupt traffic flow and safety.

Simulated false information affects vehicle coordination and traffic efficiency.

Proposed solutions can reduce the adverse effects of malicious attacks.

Abstract

Given the promising future of autonomous vehicles, it is foreseeable that self-driving cars will soon emerge 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 an innovative car-following model for the simulation of connected self-driving vehicles. This model continually monitors data received from preceding vehicles and optimizes various actions, such as acceleration, and deceleration, with the aim of maximizing overall traffic efficiency and safety.