Jul 2026· Applied and Computational Engineering· 0 citations
TL;DR
This work addresses major challenges in sensor fusion, including data synchronization, coordinate transformation, real-time computation and conflict resolution of heterogeneous sensor data.
Abstract
Autonomous driving has become a transformative technology poised to reshape modern transportation systems. This paper explores multimodal fusion techniques that integrate various sensors with machine vision for autonomous driving. We examine the integration of various sensor modalities, including cameras, LiDAR, and millimeter-wave radar, alongside advanced machine vision algorithms such as YOLO, Faster R-CNN, Point Pillars, and MVX-Net for environment perception. This work addresses major challenges in sensor fusion, including data synchronization, coordinate transformation, real-time computation and conflict resolution of heterogeneous sensor data. We systematically analyze three typical fusion architectures: data-level, feature-level and decision-level fusion, and compare their performance in information retention, computational efficiency and system robustness. Through representative application cases in object detection and classification, high-precision localization and mapping, and decision-making and path planning, we demonstrate how multimodal fusion significantly enhances the robustness, accuracy, and reliability of autonomous vehicle perception systems. The paper further discusses current limitations including computational overhead, adverse-weather robustness, and lack of standardized evaluation, and outlines future directions such as end-to-end learning, 4D radar integration, and V2X-enabled cooperative perception. The results prove that reliable multimodal fusion is a core prerequisite for realizing safe and stable autonomous driving.
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.
With the widespread use of automatic driving technology, multimodal fusion technology has gradually formed three mainstream fusion levels: early stage, middle stage, and late stage. On the basis of extensive literature research, this paper not only systematically analyzes the research progress and optimization direction of multimodal fusion technology from the perspective of feature fusion, but also analyzes the strategies of different feature fusion based on different sensors. What's more, the general data set of multimodal fusion is integrated, and the applicable scenarios of different methods are compared and analyzed. For Camera-LiDAR feature fusion, it is more suitable for urban target detection. Camera-Radar feature fusion completes the task of bad weather perception. BEV multimodal feature fusion uses a unified bird 's-eye view as a shared representation space to realize panoramic perception. At last, the paper summarizes the core problems faced by multimodal fusion technology. In the future, it is still necessary to make continuous breakthroughs in fusion architecture innovation, robustness optimization, and engineering implementation, so as to promote the large-scale application of high-order autonomous driving technology.
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
The study concluded that autonomous car systems have strong potential to revolutionize transportation systems by improving safety and efficiency, although further improvements are required to enhance robustness, real-time adaptability, and user trust in fully autonomous driving environments.
Harold Orji, Ohaeri Ignatius, Udoudom Emem Etim et al.· INTERNATIONAL JOURNAL OF MAT...· 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
With the rapid development of intelligent connected vehicles and autonomous driving technology in recent years, environmental perception has gradually been added to ensure the safety and reliability of autonomous driving; now, automotive LiDAR is one of the required sensors in the perception system because it has high-precision 3D space detection and strong anti-interference ability. The point cloud data obtained by LiDAR is often affected by internal hardware faults and various complicated external environmental factors; therefore, there is a lot of noise that can damage the operation of autonomous driving perception. Research on the Development of Automotive LiDAR Systems and Denoising Technology for Multi-sensor Fusion. It systematically introduces the working principle and range mechanism of ToF and FMCW, the technical development path of automotive LiDAR, typical point cloud noise sources, relevant denoising methods, etc. At the same time, it will also study how the hardware characteristics of LiDAR are related to multi-sensor fusion denoising and explore the advantages of vehicle-road cooperative perception. This paper will introduce the current situation of research at home and abroad on technical progress and provide theoretical support for the engineering optimisation and application of automotive LiDAR perception technology.
Yuxin Hu· MATEC Web of Conferences· 0 citations
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