Towards Explainable Motion Prediction using Heterogeneous Graph Representations

7 December 2022

Papers citing "Towards Explainable Motion Prediction using Heterogeneous Graph Representations"

7 / 7 papers shown

Title
Towards trustworthy multi-modal motion prediction: Holistic evaluation and interpretability of outputs Sandra Carrasco Limeros Sylwia Majchrowska Joakim Johnander Christoffer Petersson Miguel Ángel Sotelo David Fernández Llorca 25 11 0 28 Oct 2022
Testing predictive automated driving systems: lessons learned and future recommendations Ruben Izquierdo Gonzalo Carlota Salinas Maldonado J. Ruiz Ignacio Parra Alonso David Fernández Llorca Miguel Ángel Sotelo 16 10 0 25 Apr 2022
CF-GNNExplainer: Counterfactual Explanations for Graph Neural Networks Ana Lucic Maartje ter Hoeve Gabriele Tolomei Maarten de Rijke Fabrizio Silvestri 110 142 0 05 Feb 2021
Explainability in Graph Neural Networks: A Taxonomic Survey Hao Yuan Haiyang Yu Shurui Gui Shuiwang Ji 162 589 0 31 Dec 2020
What-If Motion Prediction for Autonomous Driving Siddhesh Khandelwal William Qi Jagjeet Singh Andrew Hartnett Deva Ramanan 114 115 0 24 Aug 2020
Spatially-Aware Graph Neural Networks for Relational Behavior Forecasting from Sensor Data Sergio Casas Cole Gulino Renjie Liao R. Urtasun AI4CE 160 211 0 18 Oct 2019
Towards A Rigorous Science of Interpretable Machine Learning Finale Doshi-Velez Been Kim XAI FaML 225 3,658 0 28 Feb 2017