673 citations · 1.5k across the 39 of their papers we have counts for
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A Unified Generative Framework for Realistic Lidar Simulation in Autonomous Driving Systems
Hamed Haghighi, Mehrdad Dianati, Valentina Donzella +1
Simulation models for perception sensors are integral components of automotive simulators used for the virtual Verification and Validation (V\&V) of Autonomous Driving Systems (ADS…
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…
A Novel Computationally Efficient Group Signature for Anonymous and Secure V2X Communications
Jia Liu, Liqun Chen, Mehrdad Dianati +2
The use of vehicle-to-everything (V2X) communication is expected to significantly improve road safety and traffic management. We present an efficient protocol, called the AEE proto…
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…
Interruption-Aware Cooperative Perception for V2X Communication-Aided Autonomous Driving
Shunli Ren, Zixing Lei, Zi Wang +4
Cooperative perception can significantly improve the perception performance of autonomous vehicles beyond the limited perception ability of individual vehicles by exchanging inform…
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.…