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 …
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 …
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 …
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 …
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 …
Deductive reinforcement learning for visual autonomous urban driving navigation
Existing deep reinforcement learning (RL) are devoted to research applications on video
games, eg, The Open Racing Car Simulator (TORCS) and Atari games. However, it remains …
games, eg, The Open Racing Car Simulator (TORCS) and Atari games. However, it remains …