1 citations · 3 across the 12 of their papers we have counts for
5 papers · 1 filter
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…
A Novel Deep Neural Network for Trajectory Prediction in Automated Vehicles Using Velocity Vector Field
MReza Alipour Sormoli, Amir Samadi, Sajjad Mozaffari +3
Anticipating the motion of other road users is crucial for automated driving systems (ADS), as it enables safe and informed downstream decision-making and motion planning. Unfortun…
SAFE: Saliency-Aware Counterfactual Explanations for DNN-based Automated Driving Systems
Amir Samadi, Amir Shirian, Konstantinos Koufos +2
A CF explainer identifies the minimum modifications in the input that would alter the model's output to its complement. In other words, a CF explainer computes the minimum modifica…
Counterfactual Explainer Framework for Deep Reinforcement Learning Models Using Policy Distillation
Amir Samadi, Konstantinos Koufos, Kurt Debattista +1
Deep Reinforcement Learning (DRL) has demonstrated promising capability in solving complex control problems. However, DRL applications in safety-critical systems are hindered by th…
Multimodal Manoeuvre and Trajectory Prediction for Automated Driving on Highways Using Transformer Networks
Sajjad Mozaffari, Mreza Alipour Sormoli, Konstantinos Koufos +1
Predicting the behaviour (i.e., manoeuvre/trajectory) of other road users, including vehicles, is critical for the safe and efficient operation of autonomous vehicles (AVs), a.k.a.…