Parallel learning: Overview and perspective for computational learning across Syn2Real and Sim2Real
The virtual-to-real paradigm, ie, training models on virtual data and then applying them to
solve real-world problems, has attracted more and more attention from various domains by …
solve real-world problems, has attracted more and more attention from various domains by …
Recent advancements in end-to-end autonomous driving using deep learning: A survey
End-to-End driving is a promising paradigm as it circumvents the drawbacks associated with
modular systems, such as their overwhelming complexity and propensity for error …
modular systems, such as their overwhelming complexity and propensity for error …
Waymax: An accelerated, data-driven simulator for large-scale autonomous driving research
Simulation is an essential tool to develop and benchmark autonomous vehicle planning
software in a safe and cost-effective manner. However, realistic simulation requires accurate …
software in a safe and cost-effective manner. However, realistic simulation requires accurate …
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 intelligent transportation systems: A survey
Latest technological improvements increased the quality of transportation. New data-driven
approaches bring out a new research direction for all control-based systems, eg, in …
approaches bring out a new research direction for all control-based systems, eg, in …
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 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 model-free reinforcement learning for urban driving using implicit affordances
M Toromanoff, E Wirbel… - Proceedings of the IEEE …, 2020 - openaccess.thecvf.com
Reinforcement Learning (RL) aims at learning an optimal behavior policy from its own
experiments and not rule-based control methods. However, there is no RL algorithm yet …
experiments and not rule-based control methods. However, there is no RL algorithm yet …
Gina-3d: Learning to generate implicit neural assets in the wild
Modeling the 3D world from sensor data for simulation is a scalable way of developing
testing and validation environments for robotic learning problems such as autonomous …
testing and validation environments for robotic learning problems such as autonomous …
Urban driver: Learning to drive from real-world demonstrations using policy gradients
In this work we are the first to present an offline policy gradient method for learning imitative
policies for complex urban driving from a large corpus of real-world demonstrations. This is …
policies for complex urban driving from a large corpus of real-world demonstrations. This is …