43 citations · 220 across the 31 of their papers we have counts for
5 papers · 1 filter
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…
Trajectory Prediction with Observations of Variable-Length for Motion Planning in Highway Merging scenarios
Sajjad Mozaffari, Mreza Alipour Sormoli, Konstantinos Koufos +2
Accurate trajectory prediction of nearby vehicles is crucial for the safe motion planning of automated vehicles in dynamic driving scenarios such as highway merging. Existing metho…
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.…