Computer vision for autonomous vehicles: Problems, datasets and state of the art
Recent years have witnessed enormous progress in AI-related fields such as computer
vision, machine learning, and autonomous vehicles. As with any rapidly growing field, it …
vision, machine learning, and autonomous vehicles. As with any rapidly growing field, it …
A survey of end-to-end driving: Architectures and training methods
A Tampuu, T Matiisen, M Semikin… - … on Neural Networks …, 2020 - ieeexplore.ieee.org
Autonomous driving is of great interest to industry and academia alike. The use of machine
learning approaches for autonomous driving has long been studied, but mostly in the …
learning approaches for autonomous driving has long been studied, but mostly in the …
End-to-end autonomous driving: Challenges and frontiers
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 …
Deep reinforcement learning for autonomous driving: A survey
With the development of deep representation learning, the domain of reinforcement learning
(RL) has become a powerful learning framework now capable of learning complex policies …
(RL) has become a powerful learning framework now capable of learning complex policies …
Revisiting point cloud classification: A new benchmark dataset and classification model on real-world data
Deep learning techniques for point cloud data have demonstrated great potentials in solving
classical problems in 3D computer vision such as 3D object classification and segmentation …
classical problems in 3D computer vision such as 3D object classification and segmentation …
Habitat: A platform for embodied ai research
We present Habitat, a platform for research in embodied artificial intelligence (AI). Habitat
enables training embodied agents (virtual robots) in highly efficient photorealistic 3D …
enables training embodied agents (virtual robots) in highly efficient photorealistic 3D …
[图书][B] Synthetic data for deep learning
SI Nikolenko - 2021 - Springer
You are holding in your hands… oh, come on, who holds books like this in their hands
anymore? Anyway, you are reading this, and it means that I have managed to release one of …
anymore? Anyway, you are reading this, and it means that I have managed to release one of …
Learning by cheating
Vision-based urban driving is hard. The autonomous system needs to learn to perceive the
world and act in it. We show that this challenging learning problem can be simplified by …
world and act in it. We show that this challenging learning problem can be simplified by …
nuplan: A closed-loop ml-based planning benchmark for autonomous vehicles
In this work, we propose the world's first closed-loop ML-based planning benchmark for
autonomous driving. While there is a growing body of ML-based motion planners, the lack of …
autonomous driving. While there is a growing body of ML-based motion planners, the lack of …
A survey of deep RL and IL for autonomous driving policy learning
Z Zhu, H Zhao - IEEE Transactions on Intelligent Transportation …, 2021 - ieeexplore.ieee.org
Autonomous driving (AD) agents generate driving policies based on online perception
results, which are obtained at multiple levels of abstraction, eg, behavior planning, motion …
results, which are obtained at multiple levels of abstraction, eg, behavior planning, motion …