2024· International Journal of Emerging Trends in Multidisciplinary Research· Vol 7, pp. 01-17· 0 citations
TL;DR
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.
Abstract
For this reason, autonomous transportation systems have been established as a progressive technology that leverages Artificial Intelligence (AI), Internet of Things (IoT), computer vision, and advanced sensing technologies to improve road safety, operational efficiency, and smart mobility. Deep learning models have shown great strength in detecting objects, recognizing lanes and pedestrians, avoiding obstacles on the road, as well as analyzing real-time traffic situation. Although very accurate, these models are typically black-boxes which have limited transparency and confidence in safety-critical transportation applications. Interpretability can help with accident investigation, regulatory compliance, ethical decision-making, and public acceptance of autonomous vehicles where their lack presents major problems. We present an Explainable Deep Learning Framework for Autonomous Transportation Safety by combining convolutional neural networks with the Understandable Artificial Intelligence (XAI) techniques to ensure transparency and soulfulness in autonomous transportation. The proposed framework comprises sensor fusion, image processing, feature extraction, deep neural network inference and explainability mechanisms such as Gradient-weighted Class Activation Mapping (Grad-CAM), Local Interpretable Model-Agnostic Explanations (LIME) and SHapley Additive exPlanations (SHAP). It pre-evaluates the prediction interpretation, and creates visualisations and feature-level explanations that give stakeholders insight into how models can have high accuracy on detection outcomes. Additionally, the architecture promotes accountability, compliance with regulations, safer autonomous driving and public trust in intelligent transportation systems. Explainability is shown to be an important building block for designing robust autonomous transport platforms usable in the future.
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.
Harold Orji, Ohaeri Ignatius, Udoudom Emem Etim et al.· INTERNATIONAL JOURNAL OF MAT...· 0 citations
A systematic analysis of the machine learning and deep learning models underpinning vehicle autonomy, spanning classical convolutional neural networks for object detection and semantic segmentation to recurrent and Transformer-based architectures for trajectory prediction and motion planning is presented.
Esraa Khatab, Fares Fathy, Abdallah AlKholy et al.· Machine Learning and Knowled...· 0 citations
Road accidents caused by unexpected animal crossings are a major concern, especially during nighttime when visibility is poor. To address this issue, the proposed system introduces an advanced animal detection and alert framework designed to enhance road safety through continuous monitoring. The system employs a high-resolution night-vision camera to capture real-time footage of roadways. Deep learning models such as Convolutional Neural Networks (CNN) and YOLO are used to accurately identify animals even under low-light or foggy conditions. Once an animal is detected, the system immediately triggers alert signals to warn approaching vehicles, thereby reducing the chances of collision. This intelligent approach minimizes the need for human intervention and provides a scalable solution for highways and rural roads. The integration of AI-based vision technology with real-time detection ensures efficient performance and faster response. By combining automation, deep learning, and alert mechanisms, the proposed system aims to improve nighttime driving safety and prevent animal-related road accidents.
G. S, R. S, Sanjay A K et al.· 2026 4th International Confe...· 0 citations
Driver behavior detection is essential for improving road safety in Intelligent Transportation Systems (ITSs). This paper proposes an attention-enhanced CNN integrated with Explainable Artificial Intelligence (XAI) to achieve reliable and interpretable driver behavior classification. Furthermore, the model also uses a multi-head attention mechanism to ensure that it captures local spatial features as well as long-range dependencies in the activities of the driver. The State Farm Distracted Driver dataset is experimented with using stratified 5-fold cross-validation. The proposed MHA-CNN model has a mean accuracy of 99.48% on all folds and an overall high levels of precision, recall, and F1-score in all ten distraction classes. Grad-CAM is used to produce attribution maps to improve interpretability by showing semantically important parts of the image, including hands, face, and mobile devices, and can give a visual explanation of why models make decisions. In addition, an ablation investigation is performed with k-fold validation to assess the value of the multi-head attention mechanism. Its model can run at 122 frames per second with 42.92 million parameters, showing that the suggested method is a reasonable tradeoff between accuracy, efficiency, and transparency and can be applied in real-time driver monitoring applications.
Abdullah Al Mamun, Md Shahidul Islam Shabuz, Mohamed N. Rahaman et al.· Algorithms· 0 citations
High traffic volume, urbanization and car ownership have exacerbated traffic congestion, travel time and road accidents; these are some of the problems facing modern transportation systems. Artificial Intelligence (AI) has become a viable solution, allowing intelligent, adaptive and data-informed traffic management. This paper provides an overview of the most prominent types of AI models employed in smart traffic control and road safety, such as supervised and unsupervised machine learning, deep learning, computer vision, and reinforcement learning. It discusses their applications for traffic flow prediction, adaptive traffic signal control, vehicle and pedestrian detection, accident prediction, driver behaviour monitoring, and prioritisation of emergency vehicles. The research also covers the features of Vehicle-to-Everything (V2X) communication, connected vehicles, the Internet of Things (IoT), and Intelligent Transportation Systems (ITS), as well as their potential for enhancing transportation efficiency and road safety. In addition, the paper points out potential roadblocks for the implementation of AI such as data quality, computational complexity, cybersecurity, privacy, infrastructure cost, and model interpretability. Finally, future research directions are outlined, highlighting explainable AI, generative AI, digital twins and integration of intelligent transportation systems in smart cities for sustainable cities. Overall, the review shows that AI can revolutionize traditional transportation systems, turning them into intelligent networks that can help alleviate congestion, lower the risk of accidents, optimize traffic flow, and facilitate safer and more sustainable urban mobility.
Shaikh Amra Bano, Kamal, Pramod Kumar Soni et al.· International journal of com...· 0 citations
In order to promote public safety and quick emergency response, smart cities are depending more and
more on networked cameras, Internet-of-things sensors, unmanned aerial vehicles, and edge
computing. However, occlusion, illumination fluctuation, camera motion, crowd density, complex
relationships, and the temporal evolution of anomalous behaviour make it challenging to identify and
forecast important events from continuous urban footage. For real-time critical-event monitoring, this
study presents XAI-CityVision, an explainable artificial intelligence platform that combines computer
vision, object identification, temporal learning, risk assessment, and visual explanation. The
framework uses a synchronised acquisition layer to receive heterogeneous urban observations,
preprocesses videos, uses CNN or Vision Transformer backbones to extract spatial representations,
uses a YOLO-family detector to identify pertinent objects, and uses ConvLSTM or transformer-based
temporal learning to model event evolution. Grad-CAM, SHAP, and attention visualisation offer
complementary explanations of the choice, while a risk module integrates event likelihood, temporal
persistence, and contextual severity. A universal event ontology including normal activity,
accident/collision, fire/smoke, crowd anomaly, intrusion/suspicious behaviour, and traffic-related
occurrences is mapped to dataset-specific labels in the experimental design, which employs public
urban and surveillance benchmarks. Precision, recall, F1-score, IoU, mean average precision, ROCAUC, false-alarm rate, inference delay, and frames per second are used to assess performance. The
contributions of spatial learning, object detection, temporal modelling, and multimodal context are
measured by ablation studies. In smart-city settings, the suggested framework offers a repeatable
architecture for integrating deployment-aware evaluation, operator-oriented explanations, and
predictive performance.
K. N. V. R. Kumar, S. D. Bhopale, Konolla Siva Ramakrishna et al.· International Journal of Mod...· 0 citations