Modern autonomous driving and intelligent transportation systems face critical challenges in managing real-time data processing, network latency, and security threats across distributed vehicular environments. Conventional cloud-centric architectures typically struggle to meet the low-latency and high-reliability requirements of vehicle-to-everything (V2X) applications, particularly in dynamic and resource-constrained edge environments. To address these challenges, this study introduces the V2X-Car Edge Cloud system, which is a cloud-native architecture driven by DevSecOps principles to ensure secure deployment, dynamic resource orchestration, and real-time monitoring across distributed edge nodes. The proposed system integrates multicluster orchestration with Kubernetes, hybrid communication protocols (C-V2X, 5G, and WAVE), and data-fusion pipelines to enhance transparency in artificial intelligence (AI)-driven decision making. A software-in-the-loop simulation environment was implemented to validate AI models, and the SmartX MultiSec framework was integrated into the proposed system to dynamically monitor network traffic flow and security. Experimental evaluations in a virtual driving environment demonstrate the ability of the proposed system to perform automated security updates, continuous performance monitoring, and dynamic resource allocation without manual intervention.

Autonomous vehicles (AVs) rely heavily on multi-modal sensors to perceive their surroundings and make real-time decisions. However, the increasing complexity of these sensors, combined with the computational demands of AI models and the challenges of synchronizing data across multiple inputs, presents significant obstacles for AV systems. These challenges of the AV domain often lead to performance latency, resulting in delayed decision-making, causing major traffic accidents. The data concentrator unit (DCU) concept addresses these issues by optimizing data pipelines and implementing intelligent control mechanisms to process sensor data efficiently. Identifying and addressing bottlenecks that contribute to latency can enhance system performance, reducing the need for costly hardware upgrades or advanced AI models. This paper introduces a delay measurement tool for multi-node analysis, enabling synchronized monitoring of data pipelines across connected hardware platforms, such as clock-synchronized DCUs. The proposed tool traces the execution flow of software applications and assesses time delays at various stages of the data pipeline in clock-synchronized hardware. The various stages are represented with intuitive graphical visualization, simplifying the identification of performance bottlenecks.