Human-centric autonomous systems with llms for user command reasoning
The evolution of autonomous driving has made remarkable advancements in recent years,
evolving into a tangible reality. However, a human-centric large-scale adoption hinges on …
evolving into a tangible reality. However, a human-centric large-scale adoption hinges on …
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 …
Traffic-domain video question answering with automatic captioning
Video Question Answering (VidQA) exhibits remarkable potential in facilitating advanced
machine reasoning capabilities within the domains of Intelligent Traffic Monitoring and …
machine reasoning capabilities within the domains of Intelligent Traffic Monitoring and …
A survey on knowledge graph-based methods for automated driving
Deep learning methods have made remarkable breakthroughs in machine learning in
general and in automated driving (AD) in particular. However, there are still unsolved …
general and in automated driving (AD) in particular. However, there are still unsolved …
A study of situational reasoning for traffic understanding
Intelligent Traffic Monitoring (ITMo) technologies hold the potential for improving road
safety/security and for enabling smart city infrastructure. Understanding traffic situations …
safety/security and for enabling smart city infrastructure. Understanding traffic situations …
Knowledge-based entity prediction for improved machine perception in autonomous systems
R Wickramarachchi, C Henson… - IEEE Intelligent …, 2022 - ieeexplore.ieee.org
Knowledge-based entity prediction (KEP) is a novel task that aims to improve machine
perception in autonomous systems. KEP leverages relational knowledge from …
perception in autonomous systems. KEP leverages relational knowledge from …
Intelligent traffic monitoring with hybrid ai
E Qasemi, A Oltramari - arXiv preprint arXiv:2209.00448, 2022 - arxiv.org
Challenges in Intelligent Traffic Monitoring (ITMo) are exacerbated by the large quantity and
modalities of data and the need for the utilization of state-of-the-art (SOTA) reasoners. We …
modalities of data and the need for the utilization of state-of-the-art (SOTA) reasoners. We …
Knowledge Graph-Based Integration of Autonomous Driving Datasets
Autonomous Driving (AD) datasets, when used in combination with deep learning
techniques, have enabled significant progress on difficult AD tasks such as perception …
techniques, have enabled significant progress on difficult AD tasks such as perception …
Utilizing Background Knowledge for Robust Reasoning over Traffic Situations
Understanding novel situations in the traffic domain requires an intricate combination of
domain-specific and causal commonsense knowledge. Prior work has provided sufficient …
domain-specific and causal commonsense knowledge. Prior work has provided sufficient …
Graph-Based Explainable AI: A Comprehensive Survey
Graph-based learning models learn structure-aware and node-level representations through
relational associations between data points, enhancing predictions and explainability. The …
relational associations between data points, enhancing predictions and explainability. The …