Armstrong Aboah

Maged Shoman, Armstrong Aboah, Abdulateef Daud et al.

Because traffic characteristics display stochastic nonlinear spatiotemporal dependencies, traffic prediction is a challenging task. In this paper develop a graph convolution gated recurrent unit (GC GRU N) network to extract the essential Spatio temporal features. we use Seattle loop detector data aggregated over 15 minutes and reframe the problem through space and time. The model performance is compared o benchmark models; Historical Average, Long Short Term Memory (LSTM), and Transformers. The proposed model ranked second with the fastest inference time and a very close performance to first place (Transformers). Our model also achieves a running time that is six times faster than transformers. Finally, we present a comparative study of our model and the available benchmarks using metrics such as training time, inference time, MAPE, MAE and RMSE. Spatial and temporal aspects are also analyzed for each of the trained models.

Neema Jakisa Owor, Joshua Kofi Asamoah, Tanner Wambui Muturi et al.

Fisheye cameras offer an efficient solution for wide-area traffic surveillance by capturing large fields of view from a single vantage point. However, the strong radial distortion and nonuniform resolution inherent in fisheye imagery introduce substantial challenges for standard object detectors, particularly near image boundaries where object appearance is severely degraded. In this work, we present a detection framework designed to operate robustly under these conditions. Our approach employs a simple yet effective pre and post processing pipeline that enhances detection consistency across the image, especially in regions affected by severe distortion. We train several state-of-the-art detection models on the fisheye traffic imagery and combine their outputs through an ensemble strategy to improve overall detection accuracy. Our method achieves an F1 score of0.6366 on the 2025 AI City Challenge Track 4, placing 8thoverall out of 62 teams. These results demonstrate the effectiveness of our framework in addressing issues inherent to fisheye imagery.

Tanner Muturi, Blessing Agyei Kyem, Joshua Kofi Asamoah et al.

Spatial reasoning in large-scale 3D environments such as warehouses remains a significant challenge for vision-language systems due to scene clutter, occlusions, and the need for precise spatial understanding. Existing models often struggle with generalization in such settings, as they rely heavily on local appearance and lack explicit spatial grounding. In this work, we introduce a dedicated spatial reasoning framework for the Physical AI Spatial Intelligence Warehouse dataset introduced in the Track 3 2025 AI City Challenge. Our approach enhances spatial comprehension by embedding mask dimensions in the form of bounding box coordinates directly into the input prompts, enabling the model to reason over object geometry and layout. We fine-tune the framework across four question categories namely: Distance Estimation, Object Counting, Multi-choice Grounding, and Spatial Relation Inference using task-specific supervision. To further improve consistency with the evaluation system, normalized answers are appended to the GPT response within the training set. Our comprehensive pipeline achieves a final score of 73.0606, placing 4th overall on the public leaderboard. These results demonstrate the effectiveness of structured prompt enrichment and targeted optimization in advancing spatial reasoning for real-world industrial environments.

Anthony Dontoh, Stephanie Ivey, Logan Sirbaugh et al.

Distracted driving continues to be a significant cause of road traffic injuries and fatalities worldwide, even with advancements in driver monitoring technologies. Recent developments in machine learning (ML) and deep learning (DL) have primarily focused on visual data to detect distraction, often neglecting the complex, multimodal nature of driver behavior. This systematic review assesses 74 peer-reviewed studies from 2019 to 2024 that utilize ML/DL techniques for distracted driving detection across visual, sensor-based, multimodal, and emerging modalities. The review highlights a significant prevalence of visual-only models, particularly convolutional neural networks (CNNs) and temporal architectures, which achieve high accuracy but show limited generalizability in real-world scenarios. Sensor-based and physiological models provide complementary strengths by capturing internal states and vehicle dynamics, while emerging techniques, such as auditory sensing and radio frequency (RF) methods, offer privacy-aware alternatives. Multimodal architecture consistently surpasses unimodal baselines, demonstrating enhanced robustness, context awareness, and scalability by integrating diverse data streams. These findings emphasize the need to move beyond visual-only approaches and adopt multimodal systems that combine visual, physiological, and vehicular cues while keeping in checking the need to balance computational requirements. Future research should focus on developing lightweight, deployable multimodal frameworks, incorporating personalized baselines, and establishing cross-modality benchmarks to ensure real-world reliability in advanced driver assistance systems (ADAS) and road safety interventions.