4 citations · 7 across the 7 of their papers we have counts for
8 papers
Unified Pedestrian Path Prediction Using Inverse Reinforcement Learning
Šimon Sukup, Ariyan Bighashdel, Pavol Jancura
Pedestrian path prediction is crucial for enhancing the safety of autonomous vehicles and advanced driver-assistance systems. Previous studies explored different learning-task form…
REFNet++: Multi-Task Efficient Fusion of Camera and Radar Sensor Data in Bird's-Eye Polar View
Kavin Chandrasekaran, Sorin Grigorescu, Gijs Dubbelman +1
A realistic view of the vehicle's surroundings is generally offered by camera sensors, which is crucial for environmental perception. Affordable radar sensors, on the other hand, a…
Revisiting Radar Perception With Spectral Point Clouds
Hamza Alsharif, Jing Gu, Pavol Jancura +2
Radar perception models are trained with different inputs, from range-Doppler spectra to sparse point clouds. Dense spectra are assumed to outperform sparse point clouds, yet they…
A Resource Efficient Fusion Network for Object Detection in Bird's-Eye View using Camera and Raw Radar Data
Kavin Chandrasekaran, Sorin Grigorescu, Gijs Dubbelman +1
Cameras can be used to perceive the environment around the vehicle, while affordable radar sensors are popular in autonomous driving systems as they can withstand adverse weather c…
Scaling Up Quantization-Aware Neural Architecture Search for Efficient Deep Learning on the Edge
Yao Lu, Hiram Rayo Torres Rodriguez, Sebastian Vogel +2
Neural Architecture Search (NAS) has become the de-facto approach for designing accurate and efficient networks for edge devices. Since models are typically quantized for edge depl…
Off-Policy Action Anticipation in Multi-Agent Reinforcement Learning
Ariyan Bighashdel, Daan de Geus, Pavol Jancura +1
Learning anticipation in Multi-Agent Reinforcement Learning (MARL) is a reasoning paradigm where agents anticipate the learning steps of other agents to improve cooperation among t…
Coordinating Fully-Cooperative Agents Using Hierarchical Learning Anticipation
Ariyan Bighashdel, Daan de Geus, Pavol Jancura +1
Learning anticipation is a reasoning paradigm in multi-agent reinforcement learning, where agents, during learning, consider the anticipated learning of other agents. There has bee…
Deep Adaptive Multi-Intention Inverse Reinforcement Learning
Ariyan Bighashdel, Panagiotis Meletis, Pavol Jancura +1
This paper presents a deep Inverse Reinforcement Learning (IRL) framework that can learn an a priori unknown number of nonlinear reward functions from unlabeled experts' demonstrat…