2026· MATEC Web of Conferences· Vol 424, pp. 02009· 0 citations· 2 references
TL;DR
A conceptual framework is proposed that interprets sensor fusion as a reconstructive process, transforming diverse sensory inputs into a coherent environmental model, and connects fusion strategies to key autonomous driving tasks, including object detection, tracking, localisation, and planning.
Abstract
Sensor fusion plays a critical role in enabling reliable perception for autonomous driving systems by integrating heterogeneous data from multiple sensors such as cameras, LiDAR, radar, and inertial units. However, inconsistencies in spatial alignment, temporal synchronisation, and data representation present significant challenges to achieving a unified understanding of the driving environment. This paper proposes a conceptual framework that interprets sensor fusion as a reconstructive process, transforming diverse sensory inputs into a coherent environmental model. The study systematically analyses three levels of fusion: data-level, feature-level, and decision-level, and examines how artificial intelligence enhances each stage through learned alignment, cross-modal feature representation, and uncertainty-aware decision making. Furthermore, the paper connects fusion strategies to key autonomous driving tasks, including object detection, tracking, localisation, and planning, highlighting the relationship between task requirements and fusion architecture design. Finally, major challenges such as domain shift, long-tail scenarios, sensor failure, and interpretability are discussed. This work provides a structured perspective on AI-driven sensor fusion and its role in building robust and adaptive intelligent driving systems.
This work addresses major challenges in sensor fusion, including data synchronization, coordinate transformation, real-time computation and conflict resolution of heterogeneous sensor data.
Yu-He Lu· Applied and Computational En...· 0 citations
Autonomous engineering systems require accurate perception, intelligent decision-making, and adaptive control to operate safely in dynamic environments. This paper proposes an AI-driven Intelligent Multi-Sensor Data Fusion Framework that integrates heterogeneous sensors with deep learning, probabilistic fusion, and reinforcement learning for enhanced environmental perception, autonomous reasoning, and real-time control. The framework consists of four layers: multi-sensor acquisition, intelligent preprocessing, AI-based fusion and reasoning, and autonomous execution. By employing adaptive confidence weighting and context-aware learning, it improves object detection, localization, fault diagnosis, decision reliability, and system robustness while addressing challenges such as sensor uncertainty, synchronization, failures, and cybersecurity. The scalable architecture is applicable to autonomous vehicles, robotics, smart manufacturing, infrastructure monitoring, and Industry 5.0 cyber-physical systems, with future research focusing on explainable AI, federated learning, digital twins, edge intelligence, and trustworthy autonomous decision-making.
Steven Young· International Journal of Mod...· 0 citations
Autonomous vehicle navigation is a key component of modern intelligent transportation systems, relying on the integration of multiple sensors such as LiDAR, radar, cameras, GPS, and IMUs. Sensor fusion techniques combine data from these sources to improve perception, localization, and reliability. This paper reviews classical pre-2018 sensor fusion methods, including Kalman Filters, Extended Kalman Filters (EKF), Unscented Kalman Filters (UKF), and particle filters. Different sensors have individual limitations—cameras are affected by lighting, LiDAR is costly, and radar has lower resolution—but fusion enhances overall system performance by leveraging their complementary strengths. The study examines low-, mid-, and high-level fusion approaches and proposes a hybrid framework using GPS/IMU for localization and LiDAR-camera fusion for obstacle detection. The system is based on probabilistic and Bayesian models, designed for real-time performance and robustness against noise and sensor failures. Key challenges such as synchronization, calibration, and computational complexity are discussed. Results show that sensor fusion significantly improves navigation accuracy, highlighting the importance of selecting appropriate algorithms based on application needs.Overall, the paper emphasizes that multi-sensor fusion is essential for safe and reliable autonomous driving and provides a foundation for future advancements in the field.
Suresh Babu Reddy· International Journal of Mod...· 0 citations
Since the technology of autonomous driving is gaining momentum in its implementation in real-life conditions with multifaceted and complicated road conditions, the weaknesses of single sensors in the context of sensing accuracy, stability, and adaptability to the environment become more evident. To improve the robustness and security of autonomous driving systems in compound environments, in this paper, the research on the multi-sensor fusion technology is put into the limelight and the value of such technology applied in autonomous driving perception systems are evaluated. Thereafter, a comparison of the perception properties, benefits, and deficits of cameras and lidar is made systematically and at the data level, the fundamental patterns of multi-sensor integration are divided into three levels, namely the feature-level, sensor-level and decision-level. This study shows that when multi-sensor fusion strategies are rationally designed, the constraints of a single sensor used to sample the environment can be addressed, and this strategy plays an important role in increasing the resilience of the system as well as its ability to understand its surrounding. The discussion made in this paper offers a useful source of information when it comes to the design and implementation of multi-sensors fusion systems within the engineering field.
Zhixiang Xu· MATEC Web of Conferences· 0 citations
Autonomous driving systems must operate safely and reliably under diverse traffic densities, varying weather
conditions, and dynamic road environments while maintaining real-time performance and low computational cost. However,
many existing autonomous driving frameworks rely on expensive LiDAR sensors, high-performance computing hardware,
and cloud-based processing, limiting their practical deployment in cost-sensitive applications. This paper proposes a Hybrid
AI Framework for Autonomous Driving Across Diverse Traffic and Weather Conditions that integrates Segment Anything
Model 2 (SAM2) for semantic scene segmentation, transformer-based multimodal sensor fusion for comprehensive
environmental perception, reinforcement learning (RL) for adaptive decision-making, and a neural vehicle controller for
continuous steering, throttle, and brake control. To reduce system cost and computational complexity, the framework
employs a low-cost sensor suite comprising an RGB camera, automotive radar, ultrasonic sensors, GPS, accelerometer,
gyroscope, odometer, magnetometer, temperature sensor, humidity sensor, and vibration sensor, eliminating the need for
expensive LiDAR systems. The entire architecture is optimized for deployment on the low-cost NVIDIA Jetson embedded
edge-computing platform, enabling real-time processing with reduced latency, lower power consumption, and improved
operational efficiency. The proposed framework is designed to handle challenging edge-case scenarios, including sudden
obstacles, adverse weather, dense traffic, and low-visibility conditions, by dynamically adapting sensor fusion and driving
policies. Experimental evaluation demonstrates that the proposed approach achieves over 90% perception and decisionmaking accuracy, while maintaining stable vehicle control, smooth steering, adaptive throttle regulation, and timely braking
across varying traffic and weather conditions. The results further indicate significant improvements in driving safety, vehicle
stability, collision avoidance, and computational efficiency compared with conventional autonomous driving approaches.
The proposed hybrid architecture provides a scalable, cost-effective, and practical solution for next-generation intelligent
autonomous vehicles operating in real-world environments.
N. Sehgal· International Journal of Inn...· 0 citations
In recent years, the fusion of millimeter-wave radar and vision has emerged as a prominent research hotspot and a mainstream solution for autonomous driving perception. This integration spans multiple hierarchical levels, and the evolution of each level is not an isolated technological advancement, but rather a synergistic outcome driven by technological maturity, computational constraints, and mass-production requirements. Despite the inherent information loss associated with decision-level fusion, it remains the predominant engineering approach in the industry due to its superior functional safety and cost-effectiveness. Conversely, feature-level fusion has developed rapidly, propelled by a positive feedback loop of deep learning, bird’s-eye view (BEV) representations, and cross-modal attention mechanisms, moving beyond exclusive reliance on the Transformer architecture. Meanwhile, data-level fusion directly integrates raw radar point clouds and image pixels, a strategy that theoretically minimizes information loss. However, its large-scale deployment in practical engineering applications is hindered by critical bottlenecks, including poor interpretability, vulnerability to cross-sensor fault propagation, and severe challenges in safety isolation. From an engineering perspective, this paper systematically analyzes the evolutionary trajectory of millimeter-wave radar and vision fusion technologies, clarifying the parallel coexistence and adaptive deployment of these three fusion levels in practical autonomous driving scenarios.
Yi Han, Si-Yu Wang, De-Yuan Feng et al.· Electronics· 0 citations
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