2 citations · 2 across the 4 of their papers we have counts for
4 papers · 1 filter
Pedestrian Crossing Intent Classification From Event-Based Vision Using Convolutional Spiking Neural Networks With Temporal Augmentation
Henok Teklu, Mustafa Sakhai, Maciej Wielgosz +1
Anticipating whether a pedestrian will cross the road is safety-critical for autonomous vehicles, requiring real-time inference under challenging conditions including motion blur,…
InterFuserDVS: Event-Enhanced Sensor Fusion for Safe RL-Based Decision Making
Mustafa Sakhaia, Kaung Sithua, Min Khant Soe Okea +1
Autonomous driving systems rely heavily on robust sensor fusion to perceive complex envi- ronments. Traditional setups using RGB cameras and LiDAR often struggle in high-dynamic- r…
DVS-PedX: Synthetic-and-Real Event-Based Pedestrian Dataset
Mustafa Sakhai, Kaung Sithu, Min Khant Soe Oke +1
Event cameras like Dynamic Vision Sensors (DVS) report micro-timed brightness changes instead of full frames, offering low latency, high dynamic range, and motion robustness. DVS-P…
Pedestrian intention prediction in Adverse Weather Conditions with Spiking Neural Networks and Dynamic Vision Sensors
Mustafa Sakhai, Szymon Mazurek, Jakub Caputa +2
This study examines the effectiveness of Spiking Neural Networks (SNNs) paired with Dynamic Vision Sensors (DVS) to improve pedestrian detection in adverse weather, a significant c…