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Knowledge-integrated machine learning for materials: lessons from gameplaying and robotics
K Hippalgaonkar, Q Li, X Wang, JW Fisher III… - Nature Reviews …, 2023 - nature.com
As materials researchers increasingly embrace machine-learning (ML) methods, it is natural
to wonder what lessons can be learned from other fields undergoing similar developments …
to wonder what lessons can be learned from other fields undergoing similar developments …
Convex optimization for trajectory generation: A tutorial on generating dynamically feasible trajectories reliably and efficiently
D Malyuta, TP Reynolds, M Szmuk… - IEEE Control …, 2022 - ieeexplore.ieee.org
Reliable and efficient trajectory generation methods are a fundamental need for
autonomous dynamical systems. The goal of this article is to provide a comprehensive …
autonomous dynamical systems. The goal of this article is to provide a comprehensive …
End-to-end autonomous driving: Challenges and frontiers
L Chen, P Wu, K Chitta, B Jaeger… - IEEE Transactions on …, 2024 - ieeexplore.ieee.org
The autonomous driving community has witnessed a rapid growth in approaches that
embrace an end-to-end algorithm framework, utilizing raw sensor input to generate vehicle …
embrace an end-to-end algorithm framework, utilizing raw sensor input to generate vehicle …
Behavior-1k: A benchmark for embodied ai with 1,000 everyday activities and realistic simulation
We present BEHAVIOR-1K, a comprehensive simulation benchmark for human-centered
robotics. BEHAVIOR-1K includes two components, guided and motivated by the results of an …
robotics. BEHAVIOR-1K includes two components, guided and motivated by the results of an …
Autonomous vehicles and intelligent automation: Applications, challenges, and opportunities
Intelligent Automation (IA) in automobiles combines robotic process automation and artificial
intelligence, allowing digital transformation in autonomous vehicles. IA can completely …
intelligence, allowing digital transformation in autonomous vehicles. IA can completely …
Evaluating quality in human-robot interaction: A systematic search and classification of performance and human-centered factors, measures and metrics towards an …
E Coronado, T Kiyokawa, GAG Ricardez… - Journal of Manufacturing …, 2022 - Elsevier
Industry 5.0 constitutes a change of paradigm where the increase of economic benefits
caused by a never-ending increment of production is no longer the only priority. Instead …
caused by a never-ending increment of production is no longer the only priority. Instead …
Automotive LiDAR technology: A survey
Nowadays, and more than a decade after the first steps towards autonomous driving, we
keep heading to achieve fully autonomous vehicles on our roads, with LiDAR sensors being …
keep heading to achieve fully autonomous vehicles on our roads, with LiDAR sensors being …
Graph neural networks for intelligent transportation systems: A survey
S Rahmani, A Baghbani, N Bouguila… - IEEE Transactions on …, 2023 - ieeexplore.ieee.org
Graph neural networks (GNNs) have been extensively used in a wide variety of domains in
recent years. Owing to their power in analyzing graph-structured data, they have become …
recent years. Owing to their power in analyzing graph-structured data, they have become …
Explainable artificial intelligence for autonomous driving: A comprehensive overview and field guide for future research directions
S Atakishiyev, M Salameh, H Yao, R Goebel - IEEE Access, 2024 - ieeexplore.ieee.org
Autonomous driving has achieved significant milestones in research and development over
the last two decades. There is increasing interest in the field as the deployment of …
the last two decades. There is increasing interest in the field as the deployment of …
Scene transformer: A unified architecture for predicting multiple agent trajectories
J Ngiam, B Caine, V Vasudevan, Z Zhang… - arxiv preprint arxiv …, 2021 - arxiv.org
Predicting the motion of multiple agents is necessary for planning in dynamic environments.
This task is challenging for autonomous driving since agents (eg vehicles and pedestrians) …
This task is challenging for autonomous driving since agents (eg vehicles and pedestrians) …