Jul 2026· FUDMA Journal of Sciences· Vol 10, pp. 227-241· 0 citations· 28 references
TL;DR
This work maps out the market landscape and identifies hybrid architectures as the current Pareto-optimal solution, balancing operational capability, safety assurance, and immediate regulatory feasibility for L3/L4 automation.
Abstract
Autonomous vehicles (AVs) represent a foundational cornerstone of future smart city transportation systems, offering the potential to eliminate human driving errors, reduce traffic fatalities by at least 40%, and optimize energy consumption. While AV technology is advancing rapidly toward highly automated driving (HAV) and driver-out Level 4 (L4) deployment, commercial realization remains hindered by significant technological uncertainties, soaring development costs, and strict safety-critical validation requirements. This provides a comprehensive, holistic survey of autonomous vehicle technology, bridging gaps in the existing literature by tracing its historical evolution to modern platforms equipped with LiDAR, radar, cameras, and (V2X) communication. Beyond exploring essential architectural components, it evaluates the modern state of research by benchmarking the three dominant autonomous vehicle software architectures: modular pipelines, pure End-to-End (E2E), and hybrid systems across seven quantitative dimensions: planning quality, safety certification, latency, data efficiency, debuggability, Operational Design Domain (ODD) adaptation, and robustness. Comparative analysis of industry-standard benchmarks, including nuScenes and CARLA, reveals that while E2E and hybrid approaches achieve superior planning scores (88–91%) and lower collision rates (1.1–2.0%) than modular pipelines (84–86% planning; 3.95% collisions), pure E2E models lack a viable regulatory path for 2026 L4 deployment due to prohibitive validation mandates. Conversely, hybrid modular-E2E algorithms recover approximately 98% of E2E planning performance, drastically reduce debugging times to 2–6 hours, and retain compliance with ISO 26262:2018 ASIL-D safety standards. This work maps out the market landscape and identifies hybrid architectures as the current Pareto-optimal solution, balancing operational capability, safety assurance, and immediate regulatory feasibility for L3/L4 automation
European safety regulation now permits a large share of automated-driving homologation evidence to be produced virtually, provided a validated physical-virtual facility generates it. We present a deployed hybrid Vehicle-in-the-Loop (ViL) platform that couples a real instrumented vehicle with a CARLA-based digital twin (DT) through a V2X message pipeline, and we report its first integrated operation on a public-road-representative test track. A real vehicle streams ETSI-compliant CAM/CPM messages into the DT, where a GPU-accelerated Cooperative Perception (CP) module fuses them into a probabilistic occupancy grid during scenario runtime. We demonstrate the platform on a multi-vehicle double T-intersection scenario, characterise the CP workload across nominal, rain and night conditions and five localization-noise levels, and discuss the platform's current architectural limits and the engineering targets they define. The results show that CP substantially widens field-of-view (FoV) coverage and improves occupied-cell recall, and that beyond a moderate localization-noise threshold, positioning uncertainty, and not weather, becomes the dominant error source. We outline the platform's trajectory toward a Mediterranean operational design domain (ODD) testing service.
A. Bolovinou, Giorgos Hadjipavlis, M. Antonopoulos et al.· arXiv.org· 0 citations
The introduction of Connected and Autonomous Vehicles (CAVs) into the existing traffic system represents one of the greatest challenges of modern road traffic engineering. Beyond their role as active traffic participants, CAVs can also be regarded as mobile (floating) sensors, effectively turning the vehicle fleet itself into a distributed, city-wide and motorway-wide sensing infrastructure. The transition from fully human-driven vehicles to fully autonomous vehicles will take decades, giving rise to a prolonged mixed-traffic period in which vehicles with different levels of automation share the same road space. This paper analyses the parameters and measures used for evaluating the throughput, environmental impact, and safety of traffic networks at different CAV penetration rates. It further reviews studies that rely exclusively on data collected from CAVs acting as mobile sensors, examining data-aggregation and traffic-state-estimation methods used to reconstruct macroscopic traffic parameters such as flow, density, headway, and speed. Additionally, measures for evaluating specific use cases for CAVs including mobility-on-demand services and their cost comparison with human-driven taxi operations are also addressed. The energy and emissions implications of CAV deployment, including the added burden of sensing hardware and system-level rebound effects, are also examined. Based on the synthesis performed, a set of representative CAVs penetration rates is proposed as a standardised framework for future mixed-traffic flow evaluations.
Lucija Bukvić, M. Gregurić, Filip Vrbanić et al.· Vehicles· 0 citations
The rapid advancements in intelligent mobility, linked cars, and autonomous transportation systems have elevated transportation safety to the forefront of research priorities. When it comes to monitoring, decision-making, and road safety, traditional transport is being utterly transformed by technologies like AI, ML, the Internet of Things (IoT), V2X communications, edge computing, and autonomous cars. The present review provides an extensive overview of the key concepts of transportation safety, such as VRUs, camera-based perception, AI detection and recognition, tracking and trajectory prediction techniques. In addition, the state-of-the-art in the areas of IoT enabled transportation systems, intelligent vehicles and V2X communication frameworks is discussed to explore how these innovations enable cooperative and connected mobility. Besides that, the main challenges faced by the autonomous vehicles, especially safety and reliability issues, legal and moral concerns, infrastructure issues and human-machine interaction are considered. Moreover, the recent literature in the area of emerging transportation technologies is reviewed in order to examine the most recent trends, advancements and open problems. This analysis demonstrates how critical it is to build a trustworthy transportation ecosystem by integrating intelligent communication networks, artificial intelligence (AI), and edge computing.
Manish Jain· International Journal of Nex...· 0 citations
This review proposes a novel function-oriented taxonomy by categorizing architectures into perception-integrated and planning-integrated paradigms, and addresses critical challenges, particularly long-tail data scarcity and the deficiency in human-like decision-making.
Yunxing Chen, Guo Yu, Pengfei Ran et al.· Actuators· 0 citations
The increasing use of Unmanned Aerial Vehicles (UAVs) in energy-sector operations- such as pipeline inspection, infrastructure monitoring, and remote asset surveillance- has highlighted critical limitations in predominantly manual and semi-automated drone systems. While current UAV deployments offer improved safety and efficiency over traditional inspection methods, their dependence on continuous human control and stable communication links restricts scalability, resilience, and operational autonomy in complex or hazardous environments. This paper presents a conceptual framework for (state) adaptive autonomous UAV systems designed to address these limitations. The proposed approach emphasizes the integration of intelligent sensing, perception, decision-making, control, and communication as coordinated layers capable of adjusting to changing operational conditions. Rather than focusing on specific implementations, the framework outlines how autonomy-driven design principles can enhance UAV reliability, reduce human intervention, and improve operational continuity in energy-sector applications. By positioning autonomy as a critical enabler rather than an optional feature, this work aligns with ongoing digital transformation and energy transition efforts. The paper discusses potential application scenarios within oil and gas, power infrastructure, and renewable energy systems, and highlights key challenges related to regulation, system validation, and future deployment. The proposed framework provides a foundation for further research and development toward resilient, intelligent UAV operations in the evolving global energy landscape.
G. I. Akanbi, O. Kolade, S. Akande et al.· SPE Nigeria Annual Internati...· 0 citations
The proliferation of autonomous vehicle necessitates robust perception systems capable of functioning within inherently dynamic traffic environments. This dissertation systematically evaluates state-of-the-art object detection methodologies for autonomous vehicles through comprehensive taxonomic analysis. The prime objectives encompass: establishing a comparative framework for evaluating heterogeneous detection approaches; identifying domain-specific optimization strategies; and elucidating research trajectories within this rapidly evolving domain. The investigation categorizes detection paradigms across multiple dimensions: spatial representation (2D/3D), computational architecture (single/multi-stage algorithms), and sensory modalities (camera/LiDAR/fusion approaches). Methodologically, this work employs systematic review protocols incorporating bibliometric analysis of high-impact publications from Scopus, Elsevier, Springer, and Google Scholar (2018-2025). Performance evaluation integrates multiple benchmarks utilizing standardized metrics (mAP, IoU, latency) to facilitate cross-architectural comparison. Implementation will adapt high-performing architectures through transfer learning with emphasis on edge-optimization techniques including quantization-aware training and neural architecture search. Validation will employ both simulation environments and instrumented vehicle testing. This investigation addresses critical gaps in perception system robustness while providing actionable insights for autonomous driving technology advancement under diverse operational scenarios, ultimately enhancing traffic safety through more reliable obstacle detection
Sharmistha Dey, Kuldeep Chouhan· International Journal For Mu...· 0 citations
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