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 …
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 …
Interpretable end-to-end urban autonomous driving with latent deep reinforcement learning
Unlike popular modularized framework, end-to-end autonomous driving seeks to solve the
perception, decision and control problems in an integrated way, which can be more …
perception, decision and control problems in an integrated way, which can be more …
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 …
Learning autonomous control policy for intersection navigation with pedestrian interaction
Z Zhu, H Zhao - IEEE Transactions on Intelligent Vehicles, 2023 - ieeexplore.ieee.org
In recent years, great efforts have been devoted to deep imitation learning for autonomous
driving control, where raw sensory inputs are directly mapped to control actions. However …
driving control, where raw sensory inputs are directly mapped to control actions. However …
Cirl: Controllable imitative reinforcement learning for vision-based self-driving
Autonomous urban driving navigation with complex multi-agent dynamics is under-explored
due to the difficulty of learning an optimal driving policy. The traditional modular pipeline …
due to the difficulty of learning an optimal driving policy. The traditional modular pipeline …
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 …
Query-efficient imitation learning for end-to-end autonomous driving
One way to approach end-to-end autonomous driving is to learn a policy function that maps
from a sensory input, such as an image frame from a front-facing camera, to a driving action …
from a sensory input, such as an image frame from a front-facing camera, to a driving action …
Neat: Neural attention fields for end-to-end autonomous driving
Efficient reasoning about the semantic, spatial, and temporal structure of a scene is a crucial
prerequisite for autonomous driving. We present NEural ATtention fields (NEAT), a novel …
prerequisite for autonomous driving. We present NEural ATtention fields (NEAT), a novel …