Search Results for author: Lars Kunze

Found 25 papers, 5 papers with code

Extending Structural Causal Models for Use in Autonomous Embodied Systems

1 code implementation3 Jun 2024 Rhys Howard, Lars Kunze

To such an end we present a case study in which we describe a module-based autonomous driving system comprised of SCMs.

Attribute Autonomous Driving

Advancing Explainable Autonomous Vehicle Systems: A Comprehensive Review and Research Roadmap

no code implementations19 Mar 2024 Sule Tekkesinoglu, Azra Habibovic, Lars Kunze

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.

Autonomous Driving Data Integration +2

Testing autonomous vehicles and AI: perspectives and challenges from cybersecurity, transparency, robustness and fairness

no code implementations21 Feb 2024 David Fernández Llorca, Ronan Hamon, Henrik Junklewitz, Kathrin Grosse, Lars Kunze, Patrick Seiniger, Robert Swaim, Nick Reed, Alexandre Alahi, Emilia Gómez, Ignacio Sánchez, Akos Kriston

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.

Autonomous Vehicles Decision Making +1

RAG-Driver: Generalisable Driving Explanations with Retrieval-Augmented In-Context Learning in Multi-Modal Large Language Model

no code implementations16 Feb 2024 Jianhao Yuan, Shuyang Sun, Daniel Omeiza, Bo Zhao, Paul Newman, Lars Kunze, Matthew Gadd

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.

Autonomous Driving Decision Making +4

CC-SGG: Corner Case Scenario Generation using Learned Scene Graphs

no code implementations18 Sep 2023 George Drayson, Efimia Panagiotaki, Daniel Omeiza, Lars Kunze

Corner case scenarios are an essential tool for testing and validating the safety of autonomous vehicles (AVs).

Autonomous Driving

Generating and Explaining Corner Cases Using Learnt Probabilistic Lane Graphs

no code implementations25 Aug 2023 Enrik Maci, Rhys Howard, Lars Kunze

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.

Autonomous Vehicles

Towards a Causal Probabilistic Framework for Prediction, Action-Selection & Explanations for Robot Block-Stacking Tasks

no code implementations11 Aug 2023 Ricardo Cannizzaro, Jonathan Routley, Lars Kunze

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.

Causal Inference counterfactual

Semantic Interpretation and Validation of Graph Attention-based Explanations for GNN Models

no code implementations8 Aug 2023 Efimia Panagiotaki, Daniele De Martini, Lars Kunze

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.

Feature Importance Graph Attention +1

SEM-GAT: Explainable Semantic Pose Estimation using Learned Graph Attention

1 code implementation7 Aug 2023 Efimia Panagiotaki, Daniele De Martini, Georgi Pramatarov, Matthew Gadd, Lars Kunze

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.

Graph Attention Graph Neural Network +2

Effects of Explanation Specificity on Passengers in Autonomous Driving

no code implementations2 Jul 2023 Daniel Omeiza, Raunak Bhattacharyya, Nick Hawes, Marina Jirotka, Lars Kunze

In this paper, we investigate the effects of natural language explanations' specificity on passengers in autonomous driving.

Autonomous Driving Explanation Generation +1

Simulation-Based Counterfactual Causal Discovery on Real World Driver Behaviour

1 code implementation6 Jun 2023 Rhys Howard, Lars Kunze

Being able to reason about how one's behaviour can affect the behaviour of others is a core skill required of intelligent driving agents.

Causal Discovery counterfactual

CAR-DESPOT: Causally-Informed Online POMDP Planning for Robots in Confounded Environments

no code implementations13 Apr 2023 Ricardo Cannizzaro, Lars Kunze

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.

Decision Making

Textual Explanations for Automated Commentary Driving

1 code implementation12 Apr 2023 Marc Alexander Kühn, Daniel Omeiza, Lars Kunze

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).

Autonomous Vehicles Explanation Generation

Explainable Action Prediction through Self-Supervision on Scene Graphs

no code implementations7 Feb 2023 Pawit Kochakarn, Daniele De Martini, Daniel Omeiza, Lars Kunze

This work explores scene graphs as a distilled representation of high-level information for autonomous driving, applied to future driver-action prediction.

Autonomous Driving

From Spoken Thoughts to Automated Driving Commentary: Predicting and Explaining Intelligent Vehicles' Actions

no code implementations19 Apr 2022 Daniel Omeiza, Sule Anjomshoae, Helena Webb, Marina Jirotka, Lars Kunze

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.

counterfactual

Explanations in Autonomous Driving: A Survey

no code implementations9 Mar 2021 Daniel Omeiza, Helena Webb, Marina Jirotka, Lars Kunze

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.

Autonomous Driving Management

Online Inference and Detection of Curbs in Partially Occluded Scenes with Sparse LIDAR

no code implementations11 Jul 2019 Tarlan Suleymanov, Lars Kunze, Paul Newman

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.

Autonomous Vehicles Motion Planning

Generating All the Roads to Rome: Road Layout Randomization for Improved Road Marking Segmentation

no code implementations10 Jul 2019 Tom Bruls, Horia Porav, Lars Kunze, Paul Newman

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

no code implementations3 Dec 2018 Tom Bruls, Horia Porav, Lars Kunze, Paul Newman

Many tasks performed by autonomous vehicles such as road marking detection, object tracking, and path planning are simpler in bird's-eye view.

Autonomous Vehicles Object Tracking +1

Artificial Intelligence for Long-Term Robot Autonomy: A Survey

no code implementations13 Jul 2018 Lars Kunze, Nick Hawes, Tom Duckett, Marc Hanheide, Tomáš Krajník

Autonomous systems will play an essential role in many applications across diverse domains including space, marine, air, field, road, and service robotics.

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