852 citations · 1.5k across the 82 of their papers we have counts for
22 papers · 1 filter
FisheyeMultiNet: Real-time Multi-task Learning Architecture for Surround-view Automated Parking System
Pullarao Maddu, Wayne Doherty, Ganesh Sistu +7
Automated Parking is a low speed manoeuvring scenario which is quite unstructured and complex, requiring full 360° near-field sensing around the vehicle. In this paper, we discuss…
Let's Get Dirty: GAN Based Data Augmentation for Camera Lens Soiling Detection in Autonomous Driving
Michal Uricar, Ganesh Sistu, Hazem Rashed +5
Wide-angle fisheye cameras are commonly used in automated driving for parking and low-speed navigation tasks. Four of such cameras form a surround-view system that provides a compl…
RST-MODNet: Real-time Spatio-temporal Moving Object Detection for Autonomous Driving
Mohamed Ramzy, Hazem Rashed, Ahmad El Sallab +1
Moving Object Detection (MOD) is a critical task for autonomous vehicles as moving objects represent higher collision risk than static ones. The trajectory of the ego-vehicle is pl…
FuseMODNet: Real-Time Camera and LiDAR based Moving Object Detection for robust low-light Autonomous Driving
Hazem Rashed, Mohamed Ramzy, Victor Vaquero +3
Moving object detection is a critical task for autonomous vehicles. As dynamic objects represent higher collision risk than static ones, our own ego-trajectories have to be planned…
FisheyeDistanceNet: Self-Supervised Scale-Aware Distance Estimation using Monocular Fisheye Camera for Autonomous Driving
Varun Ravi Kumar, Sandesh Athni Hiremath, Stefan Milz +4
Fisheye cameras are commonly used in applications like autonomous driving and surveillance to provide a large field of view (). However, they come at the cost of stro…
FisheyeMODNet: Moving Object detection on Surround-view Cameras for Autonomous Driving
Marie Yahiaoui, Hazem Rashed, Letizia Mariotti +5
Moving Object Detection (MOD) is an important task for achieving robust autonomous driving. An autonomous vehicle has to estimate collision risk with other interacting objects in t…
RGB and LiDAR fusion based 3D Semantic Segmentation for Autonomous Driving
Khaled El Madawy, Hazem Rashed, Ahmad El Sallab +3
LiDAR has become a standard sensor for autonomous driving applications as they provide highly precise 3D point clouds. LiDAR is also robust for low-light scenarios at night-time or…
SoilingNet: Soiling Detection on Automotive Surround-View Cameras
Michal Uricar, Pavel Krizek, Ganesh Sistu +1
Cameras are an essential part of sensor suite in autonomous driving. Surround-view cameras are directly exposed to external environment and are vulnerable to get soiled. Cameras ha…
WoodScape: A multi-task, multi-camera fisheye dataset for autonomous driving
Senthil Yogamani, Ciaran Hughes, Jonathan Horgan +14
Fisheye cameras are commonly employed for obtaining a large field of view in surveillance, augmented reality and in particular automotive applications. In spite of their prevalence…
MultiNet++: Multi-Stream Feature Aggregation and Geometric Loss Strategy for Multi-Task Learning
Sumanth Chennupati, Ganesh Sistu, Senthil Yogamani +1
Multi-task learning is commonly used in autonomous driving for solving various visual perception tasks. It offers significant benefits in terms of both performance and computationa…
Exploring Deep Spiking Neural Networks for Automated Driving Applications
Sambit Mohapatra, Heinrich Gotzig, Senthil Yogamani +2
Neural networks have become the standard model for various computer vision tasks in automated driving including semantic segmentation, moving object detection, depth estimation, vi…
Realistic Ultrasonic Environment Simulation Using Conditional Generative Adversarial Networks
Maximilian Pöpperl, Raghavendra Gulagundi, Senthil Yogamani +1
Recently, realistic data augmentation using neural networks especially generative neural networks (GAN) has achieved outstanding results. The communities main research focus is vis…
Capsule Neural Network based Height Classification using Low-Cost Automotive Ultrasonic Sensors
Maximilian Pöpperl, Raghavendra Gulagundi, Senthil Yogamani +1
High performance ultrasonic sensor hardware is mainly used in medical applications. Although, the development in automotive scenarios is towards autonomous driving, the ultrasonic…
NeurAll: Towards a Unified Visual Perception Model for Automated Driving
Ganesh Sistu, Isabelle Leang, Sumanth Chennupati +4
Convolutional Neural Networks (CNNs) are successfully used for the important automotive visual perception tasks including object recognition, motion and depth estimation, visual SL…
Yes, we GAN: Applying Adversarial Techniques for Autonomous Driving
Michal Uricar, Pavel Krizek, David Hurych +3
Generative Adversarial Networks (GAN) have gained a lot of popularity from their introduction in 2014 till present. Research on GAN is rapidly growing and there are many variants o…
Challenges in Designing Datasets and Validation for Autonomous Driving
Michal Uricar, David Hurych, Pavel Krizek +1
Autonomous driving is getting a lot of attention in the last decade and will be the hot topic at least until the first successful certification of a car with Level 5 autonomy. Ther…
Design of Real-time Semantic Segmentation Decoder for Automated Driving
Arindam Das, Saranya Kandan, Senthil Yogamani +1
Semantic segmentation remains a computationally intensive algorithm for embedded deployment even with the rapid growth of computation power. Thus efficient network design is a crit…
AuxNet: Auxiliary tasks enhanced Semantic Segmentation for Automated Driving
Sumanth Chennupati, Ganesh Sistu, Senthil Yogamani +1
Decision making in automated driving is highly specific to the environment and thus semantic segmentation plays a key role in recognizing the objects in the environment around the…
Exploring applications of deep reinforcement learning for real-world autonomous driving systems
Victor Talpaert, Ibrahim Sobh, B Ravi Kiran +4
Deep Reinforcement Learning (DRL) has become increasingly powerful in recent years, with notable achievements such as Deepmind's AlphaGo. It has been successfully deployed in comme…
Real-time Joint Object Detection and Semantic Segmentation Network for Automated Driving
Ganesh Sistu, Isabelle Leang, Senthil Yogamani
Convolutional Neural Networks (CNN) are successfully used for various visual perception tasks including bounding box object detection, semantic segmentation, optical flow, depth es…
Optical Flow augmented Semantic Segmentation networks for Automated Driving
Hazem Rashed, Senthil Yogamani, Ahmad El-Sallab +2
Motion is a dominant cue in automated driving systems. Optical flow is typically computed to detect moving objects and to estimate depth using triangulation. In this paper, our mot…
Multi-stream CNN based Video Semantic Segmentation for Automated Driving
Ganesh Sistu, Sumanth Chennupati, Senthil Yogamani
Majority of semantic segmentation algorithms operate on a single frame even in the case of videos. In this work, the goal is to exploit temporal information within the algorithm mo…