activity
20152026
most citedCoordination protocol for inter-operator spectrum sharing in co-primary 5G small cell networks

43 citations · 220 across the 31 of their papers we have counts for

collaborators

32 papers

cs.SE2026

Collaborative Multi-Agent Testing for Emergent Failure Discovery in Autonomous Driving Systems

Ruizhen Gu, Konstantinos Koufos, Donghwan Shin +2

Autonomous Driving Systems (ADS) can fail because of faults within individual modules as well as from interactions across perception, planning, and control. Yet existing ADS testin…

cs.CV2024

Good Data Is All Imitation Learning Needs

Amir Samadi, Konstantinos Koufos, Kurt Debattista +1

In this paper, we address the limitations of traditional teacher-student models, imitation learning, and behaviour cloning in the context of Autonomous/Automated Driving Systems (A…

cs.RO2024

Towards A General-Purpose Motion Planning for Autonomous Vehicles Using Fluid Dynamics

MReza Alipour Sormoli, Konstantinos Koufos, Mehrdad Dianati +1

General-purpose motion planners for automated/autonomous vehicles promise to handle the task of motion planning (including tactical decision-making and trajectory generation) for v…

cs.RO2024★ 1 cited

A Survey on Hybrid Motion Planning Methods for Automated Driving Systems

MReza Alipour Sormoli, Konstantinos Koufos, Mehrdad Dianati +1

Motion planning is an essential element of the modular architecture of autonomous vehicles, serving as a bridge between upstream perception modules and downstream low-level control…

cs.CV2024

Integrity Monitoring of 3D Object Detection in Automated Driving Systems using Raw Activation Patterns and Spatial Filtering

Hakan Yekta Yatbaz, Mehrdad Dianati, Konstantinos Koufos +1

The deep neural network (DNN) models are widely used for object detection in automated driving systems (ADS). Yet, such models are prone to errors which can have serious safety imp…

cs.LG2024

SAFE-RL: Saliency-Aware Counterfactual Explainer for Deep Reinforcement Learning Policies

Amir Samadi, Konstantinos Koufos, Kurt Debattista +1

While Deep Reinforcement Learning (DRL) has emerged as a promising solution for intricate control tasks, the lack of explainability of the learned policies impedes its uptake in sa…