no code implementations • 13 May 2024 • Hakan Yekta Yatbaz, Mehrdad Dianati, Konstantinos Koufos, Roger Woodman
The deep neural network (DNN) models are widely used for object detection in automated driving systems (ADS).
1 code implementation • 28 Apr 2024 • Amir Samadi, Konstantinos Koufos, Kurt Debattista, Mehrdad Dianati
We evaluate the effectiveness of our framework in diverse domains, including ADS, Atari Pong, Pacman and space-invaders games, using traditional performance metrics such as validity, proximity and sparsity.
no code implementations • 11 Apr 2024 • Hakan Yekta Yatbaz, Mehrdad Dianati, Konstantinos Koufos, Roger Woodman
To address the real-time operation requirements in ADS, we also introduce a novel introspection method that combines activation patterns from multiple layers of the detector's backbone and report its performance.
no code implementations • 2 Mar 2024 • Hakan Yekta Yatbaz, Mehrdad Dianati, Konstantinos Koufos, Roger Woodman
The proposed approach pre-processes the neural activation patterns of the object detector's backbone using several different modes.
1 code implementation • 19 Sep 2023 • Mreza Alipour Sormoli, Amir Samadi, Sajjad Mozaffari, Konstantinos Koufos, Mehrdad Dianati, Roger Woodman
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.
no code implementations • 28 Jul 2023 • Amir Samadi, Amir Shirian, Konstantinos Koufos, Kurt Debattista, Mehrdad Dianati
A CF explainer identifies the minimum modifications in the input that would alter the model's output to its complement.
1 code implementation • 8 Jun 2023 • Sajjad Mozaffari, Mreza Alipour Sormoli, Konstantinos Koufos, Graham Lee, Mehrdad Dianati
In addition, we study the impact of the proposed prediction approach on motion planning and control tasks using extensive merging scenarios from the exiD dataset.
1 code implementation • 25 May 2023 • Amir Samadi, Konstantinos Koufos, Kurt Debattista, Mehrdad Dianati
Deep Reinforcement Learning (DRL) has demonstrated promising capability in solving complex control problems.
1 code implementation • 28 Mar 2023 • Sajjad Mozaffari, Mreza Alipour Sormoli, Konstantinos Koufos, Mehrdad Dianati
Due to the uncertain future behaviour of vehicles, multiple future behaviour modes are often plausible for a vehicle in a given driving scene.