Aug 2026· INTERNATIONAL JOURNAL OF MATHEMATICS AND COMPUTER RESEARCH· 0 citations
TL;DR
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.
Abstract
Autonomous car systems were developed to transform modern transportation by enabling vehicles to operate with minimal or no human intervention. The system was designed to improve road safety, reduce traffic accidents caused by human errors, and enhance driving efficiency through the integration of artificial intelligence, sensor technologies, and real-time decision-making algorithms. The study focused on the design and evaluation of an autonomous driving framework that combined computer vision, deep learning models, and sensor fusion techniques to enable accurate perception of the driving environment. The system utilized multiple sensors such as cameras, LiDAR, radar, and ultrasonic devices to capture real-time road data. These inputs were processed using machine learning algorithms to detect objects, recognize lanes, identify traffic signs, and estimate distances. A decision-making module based on artificial intelligence was used to determine optimal driving actions, while a control unit translated these decisions into physical vehicle movements such as steering, braking, and acceleration. The performance of the system was evaluated using a structured questionnaire-based assessment involving usability, safety, trust, and system performance. The results indicated that the system achieved strong user acceptance and high perceived effectiveness. The usability dimension recorded an average mean score of 4.19, safety and reliability achieved 4.31, trust level recorded 4.04, and overall system performance achieved 4.29 on a 5-point Likert scale. These results demonstrated that respondents generally agreed that the autonomous car system was effective, reliable, and safe for intelligent transportation applications. However, limitations were observed in complex weather conditions and highly unpredictable traffic scenarios, where performance slightly decreased due to sensor and environmental constraints. 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.
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
The proposed framework comprises sensor fusion, image processing, feature extraction, deep neural network inference and explainability mechanisms such as Gradient-weighted Class Activation Mapping, Local Interpretable Model-Agnostic Explanations and SHapley Additive exPlanations.
Johan Håstad's mentor Arne Andersson, Börje Langefors· International Journal of Eme...· 0 citations
In an autonomous driving vehicle, the onboard perception system is essential for understanding a dynamic, complex road scene in real time, which is fundamental to autonomous driving. Perception tasks, such as object detection, classification of traffic signs, lane detection, etc., have seen significant progress in autonomous vehicles. One of the major practical challenges is designing an integrated, computationally efficient system capable of supporting multiple perception tasks without compromising detection performance or increasing the hardware cost of embedded automotive platforms. The proposed work introduces an integrated module-based Unified Advanced Driver Assistance System (ADAS) architecture. The first is a Traffic Sign Recognition (TSR) subsystem using the lightweight YOLOv8n backbone for detecting and classifying regulatory, warning, and informational traffic signs under a variety of illumination and environmental conditions. The second module focuses on the Forward Collision Warning (FCW) subsystem, which is trained to detect vehicles, pedestrians, bicyclists, and traffic control signals in dense urban and highway situations. The third module is a novel bimodal distillation fusion module, which bundles the knowledge from both perception streams into a single student model efficient inference pipeline using the principles of knowledge distillation, compressing the knowledge of two specialist detectors into an efficient student model. The architecture has been trained and tested with domain-specific datasets that include realistic noise on the weather, illumination, and complexity. For all the major object and sign categories, significant quantitative evaluation, such as the Precision-Recall curve, the F1-confidence analysis, the confusion matrix, and the Mean Average Precision (mAP) measurement, reveals satisfactory detection performance. Achieved with the TSR module, the mAP@0.5 score is approximately 0.994, and for primary vehicle classes, the mAP@0.5 score is above 0.81 with the FCW module. In addition to the static regulatory information used in fusion, the scenario utilizes dynamic information from the environment, significantly increasing the completeness of the result, which creates a flatter confidence distribution for subsequent ADAS decision logic. The proposed lightweight ADAS architecture supports Sustainable Development Goal (SDG) 9 (Industry, Innovation and Infrastructure) by enabling computationally efficient AI-based intelligent transportation technologies, while also contributing to SDG 11 (Sustainable Cities and Communities) through improved road safety and intelligent mobility.
M. Maddan, N. Sankkarshana, M. Gayathri et al.· Frontiers in Future Transpor...· 0 citations
Autonomous vehicles depend on fast and reliable perception systems to detect surrounding vehicles, pedestrians, cyclists, traffic signs, and other road objects in real time. This paper presents a comprehensive survey and analysis of one-stage object detectors for autonomous driving rather than an implementation of a new detection system. The survey reviews the evolution of major one-stage detectors, including YOLOv1, SSD, RetinaNet, EfficientDet, anchor-free detectors such as FCOS and CenterNet, and recent real-time models such as YOLOv10. It compares these architectures through their design choices, feature-fusion strategies, loss functions, deployment trade-offs, and reported benchmark performance. The paper also summarizes commonly used autonomous-driving datasets, evaluation metrics, open challenges, and future research directions. Overall, this survey highlights how one-stage detectors balance speed, accuracy, efficiency, and robustness, while also emphasizing the remaining gap between benchmark results and dependable real-world autonomous-driving performance.
Jonel Roman, Ryan Sirjue, Peter Nguyen et al.· 0 citations
To address the limitations of conventional intelligent driving decision systems, including insufficient real-time performance, low decision accuracy, and weak adaptability under complex road conditions, this study proposes a deep learning-based intelligent driving decision framework with enhanced multi-source electromagnetic sensing and environmental perception capabilities. Considering the increasing importance of millimeter-wave radar and heterogeneous sensor fusion in intelligent transportation systems, a multi-source data fusion module integrating cameras, LiDAR, millimeter-wave radar, and vehicle status information is first established, where adaptive Kalman filtering is employed to improve data reliability and suppress noise interference. Subsequently, a hybrid decision architecture combining an improved Deep Q-Network (DQN) and a Transformer encoder is developed. Dual Q-learning and prioritized experience replay optimize sequential driving decisions, while the Transformer captures global contextual features of complex road environments, enabling effective integration of local behavioral optimization and holistic situational awareness. Finally, a comprehensive validation framework covering extreme weather, special traffic scenarios, and sudden obstacles is constructed to evaluate system robustness. Experimental results based on the nuScenes dataset and real-vehicle tests demonstrate that the proposed system achieves a decision response time below 80 ms and an average decision accuracy of 92.7% under complex road conditions, outperforming conventional rule-based and single deep learning approaches by 10.6%–28.4%. The proposed framework provides reliable decision support for intelligent vehicles and offers valuable references for electromagnetic sensing, multi-source information fusion, and wireless perception systems in advanced autonomous driving applications.
H. Hao, Y. Ling, Y. Wang et al.· Advanced Electromagnetics· 0 citations
Ensuring road safety requires timely and accurate detection of lanes and roadside traffic signs, which are essential for assisting drivers and reducing accident risks. However, robust simultaneous detection remains challenging due to variations in illumination, occlusions, complex backgrounds, and diverse road conditions. This research introduces a novel vision-based system for lane detection and roadside traffic sign recognition using advanced artificial neural network architectures. In the pre-processing stage, Contrast Limited Adaptive Histogram Equalization (CLAHE) is applied to enhance image visibility under varying lighting conditions. For classification, a Self-Artificial Attentive Gated Neuro-Recurrent Unit (SAAG-NRU) is proposed, a novel architecture integrating deep artificial neural network (ANN) layers, Gated Recurrent Units (GRU), and a self-attention mechanism, enabling sequential refinement of features and prioritization of the most safety-relevant cues. In addition, YOLOv11 was utilized to train and detect traffic signs exclusively. For lane detection, a lane mask-based approach is implemented to accurately segment and highlight lane boundaries. The trained model was integrated into a Tkinter-based Graphical User Interface (GUI) to visualize real-time detection outputs effectively. Implemented using Python-based tools, the framework is validated against benchmark lane and traffic signs, demonstrating better performance compared to existing systems in terms of lane detection, achieving 91.11% precision, and traffic sign detection, attaining 96.88% precision. Therefore, the proposed system delivers fast, accurate, and robust simultaneous lane and traffic sign detection, significantly improving real-time road safety and driver assistance.
Viraj Sonawane, B. Agarkar, Sachin Chaudhari· International Journal of Adv...· 0 citations
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