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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…