Urban driving with conditional imitation learning
Hand-crafting generalised decision-making rules for real-world urban autonomous driving is
hard. Alternatively, learning behaviour from easy-to-collect human driving demonstrations is …
hard. Alternatively, learning behaviour from easy-to-collect human driving demonstrations is …
Deep imitation learning for autonomous driving in generic urban scenarios with enhanced safety
The decision and planning system for autonomous driving in urban environments is hard to
design. Most current methods manually design the driving policy, which can be expensive to …
design. Most current methods manually design the driving policy, which can be expensive to …
End-to-end driving via conditional imitation learning
Deep networks trained on demonstrations of human driving have learned to follow roads
and avoid obstacles. However, driving policies trained via imitation learning cannot be …
and avoid obstacles. However, driving policies trained via imitation learning cannot be …
Chauffeurnet: Learning to drive by imitating the best and synthesizing the worst
Our goal is to train a policy for autonomous driving via imitation learning that is robust
enough to drive a real vehicle. We find that standard behavior cloning is insufficient for …
enough to drive a real vehicle. We find that standard behavior cloning is insufficient for …
Agile autonomous driving using end-to-end deep imitation learning
We present an end-to-end imitation learning system for agile, off-road autonomous driving
using only low-cost sensors. By imitating a model predictive controller equipped with …
using only low-cost sensors. By imitating a model predictive controller equipped with …
Imitation learning for agile autonomous driving
We present an end-to-end imitation learning system for agile, off-road autonomous driving
using only low-cost on-board sensors. By imitating a model predictive controller equipped …
using only low-cost on-board sensors. By imitating a model predictive controller equipped …
Hierarchical model-based imitation learning for planning in autonomous driving
E Bronstein, M Palatucci, D Notz… - 2022 IEEE/RSJ …, 2022 - ieeexplore.ieee.org
We demonstrate the first large-scale application of model-based generative adversarial
imitation learning (MGAIL) to the task of dense urban self-driving. We augment standard …
imitation learning (MGAIL) to the task of dense urban self-driving. We augment standard …
Imitation is not enough: Robustifying imitation with reinforcement learning for challenging driving scenarios
Imitation learning (IL) is a simple and powerful way to use high-quality human driving data,
which can be collected at scale, to produce human-like behavior. However, policies based …
which can be collected at scale, to produce human-like behavior. However, policies based …
Conditional affordance learning for driving in urban environments
Most existing approaches to autonomous driving fall into one of two categories: modular
pipelines, that build an extensive model of the environment, and imitation learning …
pipelines, that build an extensive model of the environment, and imitation learning …
Hierarchical interpretable imitation learning for end-to-end autonomous driving
End-to-end autonomous driving provides a simple and efficient framework for autonomous
driving systems, which can directly obtain control commands from raw perception data …
driving systems, which can directly obtain control commands from raw perception data …