Search Results for author:
Found 16 papers, 3 papers with code
PIP-Net: Pedestrian Intention Prediction in the Wild
Accurate pedestrian intention prediction (PIP) by Autonomous Vehicles (AVs) is one of the current research challenges in this field.
Evaluating Driver Readiness in Conditionally Automated Vehicles from Eye-Tracking Data and Head Pose
As automated driving technology advances, the role of the driver to resume control of the vehicle in conditionally automated vehicles becomes increasingly critical.
LSTM-based Preceding Vehicle Behaviour Prediction during Aggressive Lane Change for ACC Application
To this end, the proposed system was assessed on a simulated driving environment and compared with a feedforward Artificial Neural Network (ANN) model and Model Predictive Control (MPC) model.
Local and Global Contextual Features Fusion for Pedestrian Intention Prediction
The pedestrian features include body pose and local context features that represent the pedestrian's behaviour.
A Novel Model for Driver Lane Change Prediction in Cooperative Adaptive Cruise Control Systems
Accurate lane change prediction can reduce potential accidents and contribute to higher road safety.
Multi-level and multi-modal feature fusion for accurate 3D object detection in Connected and Automated Vehicles
The fused features are shared by a two-stage network: the region proposal network (RPN) and the detection head (DH).
Multi-Module G2P Converter for Persian Focusing on Relations between Words
In this paper, we investigate the application of end-to-end and multi-module frameworks for G2P conversion for the Persian language.
Traffic-Net: 3D Traffic Monitoring Using a Single Camera
Computer Vision has played a major role in Intelligent Transportation Systems (ITS) and traffic surveillance.
Curious Exploration and Return-based Memory Restoration for Deep Reinforcement Learning
Reward engineering and designing an incentive reward function are non-trivial tasks to train agents in complex environments.
Real-time Active Vision for a Humanoid Soccer Robot Using Deep Reinforcement Learning
In this paper, we present an active vision method using a deep reinforcement learning approach for a humanoid soccer-playing robot.
DeepSOCIAL: Social Distancing Monitoring and Infection Risk Assessment in COVID-19 Pandemic
Social distancing is a recommended solution by the World Health Organisation (WHO) to minimise the spread of COVID-19 in public places.
DeepHAZMAT: Hazardous Materials Sign Detection and Segmentation with Restricted Computational Resources
One of the most challenging and non-trivial tasks in robot-based rescue operations is the Hazardous Materials or HAZMATs sign detection in the operation field, to prevent further unexpected disasters.
Complex Human Action Recognition in Live Videos Using Hybrid FR-DL Method
To evaluate the proposed method, we use the UCF dataset for the benchmarking which is widely-used among researchers in action recognition research.
Deep-CAPTCHA: a deep learning based CAPTCHA solver for vulnerability assessment
In this paper, we present our customised deep neural network model, we review the research gaps, the existing challenges, and the solutions to cope with the issues.
Zero-Shot Learning and its Applications from Autonomous Vehicles to COVID-19 Diagnosis: A Review
The challenge of learning a new concept, object, or a new medical disease recognition without receiving any examples beforehand is called Zero-Shot Learning (ZSL).
Look at the Driver, Look at the Road: No Distraction! No Accident!
The paper proposes an advanced driver-assistance system that correlates the driver's head pose to road hazards by analyzing both simultaneously.
