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pro vyhledávání: '"Koufos, A."'
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 (ADS), where these methods often struggle with incomplete coverage of re
Externí odkaz:
http://arxiv.org/abs/2409.17605
General-purpose motion planners for automated/autonomous vehicles promise to handle the task of motion planning (including tactical decision-making and trajectory generation) for various automated driving functions (ADF) in a diverse range of operati
Externí odkaz:
http://arxiv.org/abs/2406.05708
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 signals. Traditional motion planners were initially designed for spec
Externí odkaz:
http://arxiv.org/abs/2406.05575
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 implications. Introspection and self-assessment models that aim to detect
Externí odkaz:
http://arxiv.org/abs/2405.07600
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 safety-critical applications, such as automated driving systems (ADS). C
Externí odkaz:
http://arxiv.org/abs/2404.18326
Monitoring the integrity of object detection for errors within the perception module of automated driving systems (ADS) is paramount for ensuring safety. Despite recent advancements in deep neural network (DNN)-based object detectors, their susceptib
Externí odkaz:
http://arxiv.org/abs/2404.07685
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 in developing highly accurate object detectors based on Deep Neural Ne
Externí odkaz:
http://arxiv.org/abs/2403.01172
Autor:
Sormoli, MReza Alipour, Samadi, Amir, Mozaffari, Sajjad, Koufos, Konstantinos, Dianati, Mehrdad, Woodman, Roger
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. Unfortunately, contemporary learning-based approaches for motion prediction ex
Externí odkaz:
http://arxiv.org/abs/2309.10948
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 modifications required to cross the model's decision boundary. Current deep ge
Externí odkaz:
http://arxiv.org/abs/2307.15786
Autor:
Mozaffari, Sajjad, Sormoli, Mreza Alipour, Koufos, Konstantinos, Lee, Graham, Dianati, Mehrdad
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 methods cannot initiate prediction for a vehicle unless observed for a fixe
Externí odkaz:
http://arxiv.org/abs/2306.05478