Search Results for author:
Found 9 papers, 5 papers with code
Integrity Monitoring of 3D Object Detection in Automated Driving Systems using Raw Activation Patterns and Spatial Filtering
The deep neural network (DNN) models are widely used for object detection in automated driving systems (ADS).
SAFE-RL: Saliency-Aware Counterfactual Explainer for Deep Reinforcement Learning Policies
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.
Run-time Monitoring of 3D Object Detection in Automated Driving Systems Using Early Layer Neural Activation Patterns
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.
Run-time Introspection of 2D Object Detection in Automated Driving Systems Using Learning Representations
The proposed approach pre-processes the neural activation patterns of the object detector's backbone using several different modes.
A Novel Deep Neural Network for Trajectory Prediction in Automated Vehicles Using Velocity Vector Field
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.
SAFE: Saliency-Aware Counterfactual Explanations for DNN-based Automated Driving Systems
A CF explainer identifies the minimum modifications in the input that would alter the model's output to its complement.
Trajectory Prediction with Observations of Variable-Length for Motion Planning in Highway Merging scenarios
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.
Counterfactual Explainer Framework for Deep Reinforcement Learning Models Using Policy Distillation
Deep Reinforcement Learning (DRL) has demonstrated promising capability in solving complex control problems.
Multimodal Manoeuvre and Trajectory Prediction for Automated Driving on Highways Using Transformer Networks
Due to the uncertain future behaviour of vehicles, multiple future behaviour modes are often plausible for a vehicle in a given driving scene.
