Assessing Cybersecurity Risks and Traffic Impact in Connected Autonomous Vehicles
🤖 AI Summary
This study addresses the traffic efficiency and safety risks posed by cyberattacks that inject false information into connected and autonomous vehicles. To mitigate these threats, the authors propose a novel intelligent car-following model that enhances both safety and throughput by continuously monitoring the leading vehicle’s state and dynamically optimizing acceleration and deceleration decisions. Leveraging a high-fidelity simulation platform, the research constructs a realistic vehicular communication environment and representative cybersecurity attack scenarios to systematically evaluate the disruptive effects of falsified data on traffic flow. Experimental results demonstrate that the proposed approach significantly suppresses the propagation of malicious information, thereby effectively improving the robustness and stability of the overall traffic system.
Application Category
📝 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.
Problem
Research questions and friction points this paper is trying to address.
cybersecurity risks
connected autonomous vehicles
traffic impact
cyberattacks
false information dissemination
Innovation
Methods, ideas, or system contributions that make the work stand out.
cybersecurity
connected autonomous vehicles
car-following model
traffic simulation
false information attack
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