RAG-based Explainable Prediction of Road Users Behaviors for Automated Driving using Knowledge Graphs and Large Language Models
MM Hussien, AN Melo, AL Ballardini… - arXiv preprint arXiv …, 2024 - arxiv.org
Prediction of road users' behaviors in the context of autonomous driving has gained
considerable attention by the scientific community in the last years. Most works focus on …
considerable attention by the scientific community in the last years. Most works focus on …
A framework for modeling knowledge graphs via processing natural descriptions of vehicle-pedestrian interactions
The full-scale deployment of autonomous driving demands successful interaction with
pedestrians and other vulnerable road users, which requires an understanding of their …
pedestrians and other vulnerable road users, which requires an understanding of their …
Vehicle Lane Change Prediction based on Knowledge Graph Embeddings and Bayesian Inference
Prediction of vehicle lane change maneuvers has gained a lot of momentum in the last few
years. Some recent works focus on predicting a vehicle's intention by predicting its trajectory …
years. Some recent works focus on predicting a vehicle's intention by predicting its trajectory …
Rag-driver: Generalisable driving explanations with retrieval-augmented in-context learning in multi-modal large language model
Robots powered by'blackbox'models need to provide human-understandable explanations
which we can trust. Hence, explainability plays a critical role in trustworthy autonomous …
which we can trust. Hence, explainability plays a critical role in trustworthy autonomous …
Attention-based interrelation modeling for explainable automated driving
Automated driving desires better performance on tasks like motion planning and interacting
with pedestrians in mixed-traffic environments. Deep learning algorithms can achieve high …
with pedestrians in mixed-traffic environments. Deep learning algorithms can achieve high …
Multi-agent driving behavior prediction across different scenarios with self-supervised domain knowledge
How to make precise multi-agent trajectory prediction is a crucial problem in the context of
autonomous driving. It is significant to have the ability to predict surrounding road …
autonomous driving. It is significant to have the ability to predict surrounding road …
Learning an interpretable model for driver behavior prediction with inductive biases
To plan safe maneuvers and act with foresight, autonomous vehicles must be capable of
accurately predicting the uncertain future. In the context of autonomous driving, deep neural …
accurately predicting the uncertain future. In the context of autonomous driving, deep neural …
Knowledge graphs for automated driving
Automated Driving (AD) datasets, when used in combination with deep learning techniques,
have enabled significant progress on difficult AD tasks such as perception, trajectory …
have enabled significant progress on difficult AD tasks such as perception, trajectory …
A knowledge graph-based approach for situation comprehension in driving scenarios
Making an informed and right decision poses huge challenges for drivers in day-to-day
traffic situations. This task vastly depends on many subjective and objective factors …
traffic situations. This task vastly depends on many subjective and objective factors …
[HTML][HTML] Towards explainable motion prediction using heterogeneous graph representations
Motion prediction systems play a crucial role in enabling autonomous vehicles to navigate
safely and efficiently in complex traffic scenarios. Graph Neural Network (GNN)-based …
safely and efficiently in complex traffic scenarios. Graph Neural Network (GNN)-based …