673 citations · 1.5k across the 39 of their papers we have counts for
39 papers
Collaborative Multi-Agent Testing for Emergent Failure Discovery in Autonomous Driving Systems
Ruizhen Gu, Konstantinos Koufos, Donghwan Shin +2
Autonomous Driving Systems (ADS) can fail because of faults within individual modules as well as from interactions across perception, planning, and control. Yet existing ADS testin…
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…
Taming Transformers for Realistic Lidar Point Cloud Generation
Hamed Haghighi, Amir Samadi, Mehrdad Dianati +2
Diffusion Models (DMs) have achieved State-Of-The-Art (SOTA) results in the Lidar point cloud generation task, benefiting from their stable training and iterative refinement during…
Optical Flow Based Detection and Tracking of Moving Objects for Autonomous Vehicles
MReza Alipour Sormoli, Mehrdad Dianati, Sajjad Mozaffari +1
Accurate velocity estimation of surrounding moving objects and their trajectories are critical elements of perception systems in Automated/Autonomous Vehicles (AVs) with a direct i…
Run-time Introspection of 2D Object Detection in Automated Driving Systems Using Learning Representations
Hakan Yekta Yatbaz, Mehrdad Dianati, Konstantinos Koufos +1
Reliable detection of various objects and road users in the surrounding environment is crucial for the safe operation of automated driving systems (ADS). Despite recent progresses…
Benchmarking the Robustness of Panoptic Segmentation for Automated Driving
Yiting Wang, Haonan Zhao, Daniel Gummadi +3
Precise situational awareness is required for the safe decision-making of assisted and automated driving (AAD) functions. Panoptic segmentation is a promising perception technique…
Data-driven Camera and Lidar Simulation Models for Autonomous Driving: A Review from Generative Models to Volume Renderers
Hamed Haghighi, Xiaomeng Wang, Hao Jing +1
Perception sensors, particularly camera and Lidar, are key elements of Autonomous Driving Systems (ADS) that enable them to comprehend their surroundings to informed driving and co…
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.…
Prediction Based Decision Making for Autonomous Highway Driving
Mustafa Yildirim, Sajjad Mozaffari, Luc McCutcheon +2
Autonomous driving decision-making is a challenging task due to the inherent complexity and uncertainty in traffic. For example, adjacent vehicles may change their lane or overtake…
Fast and Robust Registration of Partially Overlapping Point Clouds
Eduardo Arnold, Sajjad Mozaffari, Mehrdad Dianati
Real-time registration of partially overlapping point clouds has emerging applications in cooperative perception for autonomous vehicles and multi-agent SLAM. The relative translat…
Early Lane Change Prediction for Automated Driving Systems Using Multi-Task Attention-based Convolutional Neural Networks
Sajjad Mozaffari, Eduardo Arnold, Mehrdad Dianati +1
Lane change (LC) is one of the safety-critical manoeuvres in highway driving according to various road accident records. Thus, reliably predicting such manoeuvre in advance is crit…
Visual Sensor Pose Optimisation Using Visibility Models for Smart Cities
Eduardo Arnold, Sajjad Mozaffari, Mehrdad Dianati +1
Visual sensor networks are used for monitoring traffic in large cities and are promised to support automated driving in complex road segments. The pose of these sensors, i.e. posit…