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Found 24 papers, 4 papers with code
Physics-Based Causal Reasoning for Safe & Robust Next-Best Action Selection in Robot Manipulation Tasks
Safe and efficient object manipulation is a key enabler of many real-world robot applications.
Advancing Explainable Autonomous Vehicle Systems: A Comprehensive Review and Research Roadmap
Given the uncertainty surrounding how existing explainability methods for autonomous vehicles (AVs) meet the diverse needs of stakeholders, a thorough investigation is imperative to determine the contexts requiring explanations and suitable interaction strategies.
Real-time 3D semantic occupancy prediction for autonomous vehicles using memory-efficient sparse convolution
In autonomous vehicles, understanding the surrounding 3D environment of the ego vehicle in real-time is essential.
3D Semantic Occupancy Prediction
3D Semantic Segmentation
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Testing autonomous vehicles and AI: perspectives and challenges from cybersecurity, transparency, robustness and fairness
This study explores the complexities of integrating Artificial Intelligence (AI) into Autonomous Vehicles (AVs), examining the challenges introduced by AI components and the impact on testing procedures, focusing on some of the essential requirements for trustworthy AI.
RAG-Driver: Generalisable Driving Explanations with Retrieval-Augmented In-Context Learning in Multi-Modal Large Language Model
Recent advancements in Multi-Modal Large Language models (MLLMs) have shown promising potential in enhancing the explainability as a driving agent by producing control predictions along with natural language explanations.
LROC-PANGU-GAN: Closing the Simulation Gap in Learning Crater Segmentation with Planetary Simulators
We train a CycleGAN model to synthesise LROC from Planet and Asteroid Natural Scene Generation Utility (PANGU) images.
CC-SGG: Corner Case Scenario Generation using Learned Scene Graphs
Corner case scenarios are an essential tool for testing and validating the safety of autonomous vehicles (AVs).
Generating and Explaining Corner Cases Using Learnt Probabilistic Lane Graphs
We use reinforcement learning techniques to modify this policy and to generate realistic and explainable corner case scenarios which can be used for assessing the safety of AVs.
Towards Probabilistic Causal Discovery, Inference & Explanations for Autonomous Drones in Mine Surveying Tasks
Here we identify challenges relating to causality in the context of a drone system operating in a salt mine.
Towards a Causal Probabilistic Framework for Prediction, Action-Selection & Explanations for Robot Block-Stacking Tasks
Uncertainties in the real world mean that is impossible for system designers to anticipate and explicitly design for all scenarios that a robot might encounter.
Semantic Interpretation and Validation of Graph Attention-based Explanations for GNN Models
In this work, we propose a methodology for investigating the use of semantic attention to enhance the explainability of Graph Neural Network (GNN)-based models.
SEM-GAT: Explainable Semantic Pose Estimation using Learned Graph Attention
This paper proposes a Graph Neural Network(GNN)-based method for exploiting semantics and local geometry to guide the identification of reliable pointcloud registration candidates.
Effects of Explanation Specificity on Passengers in Autonomous Driving
In this paper, we investigate the effects of natural language explanations' specificity on passengers in autonomous driving.
Simulation-Based Counterfactual Causal Discovery on Real World Driver Behaviour
Being able to reason about how one's behaviour can affect the behaviour of others is a core skill required of intelligent driving agents.
CAR-DESPOT: Causally-Informed Online POMDP Planning for Robots in Confounded Environments
Robots operating in real-world environments must reason about possible outcomes of stochastic actions and make decisions based on partial observations of the true world state.
Textual Explanations for Automated Commentary Driving
In this work, a state-of-the-art (SOTA) prediction and explanation model is thoroughly evaluated and validated (as a benchmark) on the new Sense--Assess--eXplain (SAX).
Explainable Action Prediction through Self-Supervision on Scene Graphs
This work explores scene graphs as a distilled representation of high-level information for autonomous driving, applied to future driver-action prediction.
Evaluating Temporal Observation-Based Causal Discovery Techniques Applied to Road Driver Behaviour
Autonomous robots are required to reason about the behaviour of dynamic agents in their environment.
From Spoken Thoughts to Automated Driving Commentary: Predicting and Explaining Intelligent Vehicles' Actions
In the intelligent vehicle context, automated driving commentary can provide intelligible explanations about driving actions, thereby assisting a driver or an end-user during driving operations in challenging and safety-critical scenarios.
Explanations in Autonomous Driving: A Survey
With the hope to deploy autonomous vehicles (AV) on a commercial scale, the acceptance of AV by society becomes paramount and may largely depend on their degree of transparency, trustworthiness, and compliance with regulations.
Online Inference and Detection of Curbs in Partially Occluded Scenes with Sparse LIDAR
Hence, we believe that our LIDAR-based approach provides an efficient and effective way to detect visible and occluded curbs around the vehicles in challenging driving scenarios.
Generating All the Roads to Rome: Road Layout Randomization for Improved Road Marking Segmentation
Road markings provide guidance to traffic participants and enforce safe driving behaviour, understanding their semantic meaning is therefore paramount in (automated) driving.
The Right (Angled) Perspective: Improving the Understanding of Road Scenes Using Boosted Inverse Perspective Mapping
Many tasks performed by autonomous vehicles such as road marking detection, object tracking, and path planning are simpler in bird's-eye view.
Artificial Intelligence for Long-Term Robot Autonomy: A Survey
Autonomous systems will play an essential role in many applications across diverse domains including space, marine, air, field, road, and service robotics.
