Adversarial Robustness Analysis of Cloud-Assisted Autonomous Driving Systems

arXiv cs.RO / 4/7/2026

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Key Points

The paper analyzes how cloud-assisted autonomous driving can fail under cross-layer attacks that combine adversarial manipulation of perception models with vehicle-cloud network impairments.
It introduces a hardware-in-the-loop IoV testbed that jointly emulates real-time perception, control, and communications to evaluate these vulnerabilities end-to-end.
Using a YOLOv8 cloud object detector, whitebox FGSM and PGD attacks substantially reduce detection performance, with PGD at epsilon=0.04 dropping precision/recall from 0.73/0.68 to 0.22/0.15.
The study shows that network delays of 150–250 ms (about 3–4 lost frames) and packet loss of 0.5–5% destabilize closed-loop control, causing delayed actuation and rule violations.
Overall, the findings argue for designing cross-layer resilience rather than protecting perception or networking in isolation for cloud-assisted autonomous driving.

Abstract

Autonomous vehicles increasingly rely on deep learning-based perception and control, which impose substantial computational demands. Cloud-assisted architectures offload these functions to remote servers, enabling enhanced perception and coordinated decision-making through the Internet of Vehicles (IoV). However, this paradigm introduces cross-layer vulnerabilities, where adversarial manipulation of perception models and network impairments in the vehicle-cloud link can jointly undermine safety-critical autonomy. This paper presents a hardware-in-the-loop IoV testbed that integrates real-time perception, control, and communication to evaluate such vulnerabilities in cloud-assisted autonomous driving. A YOLOv8-based object detector deployed on the cloud is subjected to whitebox adversarial attacks using the Fast Gradient Sign Method (FGSM) and Projected Gradient Descent (PGD), while network adversaries induce delay and packet loss in the vehicle-cloud loop. Results show that adversarial perturbations significantly degrade perception performance, with PGD reducing detection precision and recall from 0.73 and 0.68 in the clean baseline to 0.22 and 0.15 at epsilon= 0.04. Network delays of 150-250 ms, corresponding to transient losses of approximately 3-4 frames, and packet loss rates of 0.5-5 % further destabilize closed-loop control, leading to delayed actuation and rule violations. These findings highlight the need for cross-layer resilience in cloud-assisted autonomous driving systems.