Current road safety systems face challenges in detecting curved roads and vehicles under varying lighting and weather conditions, leading to lane departure and collision risks. To address this, we propose CurvLaneNet-YOLO, a deep learning framework based on YOLOv8 that simultaneously detects road curvature and vehicles in lanes. The system integrates a parallel polynomial lane detection head into the YOLOv8 architecture, enabling real-time curvature estimation alongside vehicle detection. This research develops an AI-enabled real-time monitoring system to substantially improve road safety. The system makes use of the enhanced capabilities and features of the YOLOv8 model, which involves data preparation, model training, extensive testing, and data augmentation to guarantee model precision. Main goals are estimation of road curvature in real-time, high-performance processing and better assist for the driver. To assess the research, the Cars sample from KITTI dataset on Kaggle, which includes 7,481 images of 640 × 640 resolution, were used. The research achieves an inference latency of 15.4 ± 0.3 ms/image on the test hardware, with mAP@0.5 = 0.9373 and mAP@0.5:0.95 = 0.9217, indicating significant improvements over existing work. This research demonstrates the feasibility of using deep learning techniques for vehicle detection and road curvature estimation in real-time. The results presented in this study show promising potential to integrate into a driver-assistance system, although the data used here is limited to a proof-of-concept validation on the KITTI dataset. The suggested solution is competitive in terms of accuracy and inference time on embedded hardware, providing a potential roadmap for its application in real-world scenarios within the context of intelligent transportation systems.
Amit Pimpalkar, Pranali Dandekar, Harika Vanam et al.· Scientific Reports· 0 citations
The advancements of modern artificial intelligence in education (AIEd) systems have greatly improved prediction accuracy and personal pacing. Traditional intelligent tutoring systems have the highest achievable aggregate predictive accuracy, a value that often is rooted in historical biases, mis-represents engagement signals from advantaged learner groups, and leaves vulnerable learner groups out of the picture. To address these challenges, we develop a mathematically robust framework of deep optimization with multiple objectives to ensure equity is maintained and carried forward across the tutoring lifecycle. We propose 5 main components: (i) demographic sensitivity gradient encoding (DSGE) for measures and limits direct demographic influence by computing gradient-level sensitivities of the learning loss with respect to latent demographic embeddings; (ii) counterfactual equity replay networks (CERN) which guided by the DSGE signal, CERN models the learning process through an explicit structural causal model and uses offline counterfactual simulation to quantify fairness-sensitive trajectory differences under stated identification assumptions. (iii) The engage-weighted fairness attention fusion dynamically balances student persistence and fairness risk, in order to not let high-level engagements mask concerns for fairness. (iv) Pareto-adaptive equity-accuracy-engagement optimizer adapts objective weights on the simplex space through a meta-gradient optimization which promotes stable convergence across the competing objectives under the adopted training procedure. In (v) equity-preserving policy distillation and validation, the high-capacity multi-objective model is compressed to a light-weight student model while ensuring that the student model preserves the equity of the multi-objective model when deployed. Our framework is validated using three real-world, publicly available educational datasets viz., EdNet, ASSISTments, and open university learning analytics dataset. Empirical results indicate that our framework achieves up to 67% lower learning gain disparity and 56% greater stability of dispersion in learning-gap distributions, while maintaining learning accuracy within 1.2% of the unconstrained, accuracy-only baselines.
A. Khan, Amit Pimpalkar, Tabassum H. Khan et al.· Scientific Reports· 0 citations
The proposed BlockE2T-MORL offers a scalable, privacy-preserving, and computationally lightweight solution for next-generation intelligent path planning in cloud-based autonomous systems.
Revati Raman Dewangan, D. Thombre, Vivek Parganiha et al.· Scientific Reports· 0 citations