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Found 19 papers, 10 papers with code
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.
Taming Transformers for Realistic Lidar Point Cloud Generation
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 sampling.
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.
Benchmarking the Robustness of Panoptic Segmentation for Automated Driving
Motivated by such a need, this work proposes a unifying pipeline to assess the robustness of panoptic segmentation models for AAD, correlating it with traditional image quality.
Review of the Learning-based Camera and Lidar Simulation Methods for Autonomous Driving Systems
This paper reviews the current state-of-the-art in learning-based sensor simulation methods and validation approaches, focusing on two main types of perception sensors: cameras and Lidars.
Contrastive Learning-Based Framework for Sim-to-Real Mapping of Lidar Point Clouds in Autonomous Driving Systems
Motivated by this potential, this paper focuses on sim-to-real mapping of Lidar point clouds, a widely used perception sensor in automated driving systems.
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.
Interruption-Aware Cooperative Perception for V2X Communication-Aided Autonomous Driving
To achieve comprehensive recovery, we design a communication-adaptive multi-scale spatial-temporal prediction model to extract multi-scale spatial-temporal features based on V2X communication conditions and capture the most significant information for the prediction of the missing information.
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.
Robust Collaborative 3D Object Detection in Presence of Pose Errors
Collaborative 3D object detection exploits information exchange among multiple agents to enhance accuracy of object detection in presence of sensor impairments such as occlusion.
Prediction Based Decision Making for Autonomous Highway Driving
This paper proposes a Prediction-based Deep Reinforcement Learning (PDRL) decision-making model that considers the manoeuvre intentions of surrounding vehicles in the decision-making process for highway driving.
Fast and Robust Registration of Partially Overlapping Point Clouds
The proposed method achieves on-par performance with state-of-the-art methods on the KITTI dataset, and outperforms existing methods for low overlapping point clouds.
Early Lane Change Prediction for Automated Driving Systems Using Multi-Task Attention-based Convolutional Neural Networks
Lane change (LC) is one of the safety-critical manoeuvres in highway driving according to various road accident records.
Visual Sensor Pose Optimisation Using Visibility Models for Smart Cities
Visual sensor networks are used for monitoring traffic in large cities and are promised to support automated driving in complex road segments.
Deep Learning-based Vehicle Behaviour Prediction For Autonomous Driving Applications: A Review
Motivated by this increased popularity, we provide a comprehensive review of the state-of-the-art of deep learning-based approaches for vehicle behaviour prediction in this paper.
Cooperative Perception for 3D Object Detection in Driving Scenarios using Infrastructure Sensors
In contrast, the late fusion scheme fuses the independently detected bounding boxes from multiple spatially diverse sensors.
