activity
20192022
most citedFast Synthetic LiDAR Rendering via Spherical UV Unwrapping of Equirectangular Z-Buffer Images

2 citations · 2 across the 5 of their papers we have counts for

collaborators

7 papers

cs.CV2022

Traffic Accident Risk Forecasting using Contextual Vision Transformers

Khaled Saleh, Artur Grigorev, Adriana-Simona Mihaita

Recently, the problem of traffic accident risk forecasting has been getting the attention of the intelligent transportation systems community due to its significant impact on traff…

cs.LG2022

Traffic incident duration prediction via a deep learning framework for text description encoding

Artur Grigorev, Adriana-Simona Mihaita, Khaled Saleh +1

Predicting the traffic incident duration is a hard problem to solve due to the stochastic nature of incident occurrence in space and time, a lack of information at the beginning of…

cs.CV2020

Pedestrian Trajectory Prediction using Context-Augmented Transformer Networks

Khaled Saleh

Forecasting the trajectory of pedestrians in shared urban traffic environments is still considered one of the challenging problems facing the development of autonomous vehicles (AV…

cs.CV20202 cited

Fast Synthetic LiDAR Rendering via Spherical UV Unwrapping of Equirectangular Z-Buffer Images

Mohammed Hossny, Khaled Saleh, Mohammed Attia +2

LiDAR data is becoming increasingly essential with the rise of autonomous vehicles. Its ability to provide 360deg horizontal field of view of point cloud, equips self-driving vehic…

cs.LG2020

Refined Continuous Control of DDPG Actors via Parametrised Activation

Mohammed Hossny, Julie Iskander, Mohammed Attia +1

In this paper, we propose enhancing actor-critic reinforcement learning agents by parameterising the final actor layer which produces the actions in order to accommodate the behavi…

cs.CV2019

Domain Adaptation for Vehicle Detection from Bird's Eye View LiDAR Point Cloud Data

Khaled Saleh, Ahmed Abobakr, Mohammed Attia +3

Point cloud data from 3D LiDAR sensors are one of the most crucial sensor modalities for versatile safety-critical applications such as self-driving vehicles. Since the annotations…