23 citations · 30 across the 4 of their papers we have counts for
5 papers
Pedestrian Intention Prediction via Vision-Language Foundation Models
Mohsen Azarmi, Mahdi Rezaei, He Wang
Prediction of pedestrian crossing intention is a critical function in autonomous vehicles. Conventional vision-based methods of crossing intention prediction often struggle with ge…
Driver-Net: Multi-Camera Fusion for Assessing Driver Take-Over Readiness in Automated Vehicles
Mahdi Rezaei, Mohsen Azarmi
Ensuring safe transition of control in automated vehicles requires an accurate and timely assessment of driver readiness. This paper introduces Driver-Net, a novel deep learning fr…
Feature Importance in Pedestrian Intention Prediction: A Context-Aware Review
Mohsen Azarmi, Mahdi Rezaei, He Wang +1
Recent advancements in predicting pedestrian crossing intentions for Autonomous Vehicles using Computer Vision and Deep Neural Networks are promising. However, the black-box nature…
PIP-Net: Pedestrian Intention Prediction in the Wild
Mohsen Azarmi, Mahdi Rezaei, He Wang
Accurate pedestrian intention prediction (PIP) by Autonomous Vehicles (AVs) is one of the current research challenges in this field. In this article, we introduce PIP-Net, a novel…
Evaluating Driver Readiness in Conditionally Automated Vehicles from Eye-Tracking Data and Head Pose
Mostafa Kazemi, Mahdi Rezaei, Mohsen Azarmi
As automated driving technology advances, the role of the driver to resume control of the vehicle in conditionally automated vehicles becomes increasingly critical. In the SAE Leve…