43 citations · 220 across the 31 of their papers we have counts for
7 papers · 1 filter
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…
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…
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…
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…
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…
Run-time Monitoring of 3D Object Detection in Automated Driving Systems Using Early Layer Neural Activation Patterns
Hakan Yekta Yatbaz, Mehrdad Dianati, Konstantinos Koufos +1
Monitoring the integrity of object detection for errors within the perception module of automated driving systems (ADS) is paramount for ensuring safety. Despite recent advancement…