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 …
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 …
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 …
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 …
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 …
Multi-task learning with attention for end-to-end autonomous driving
K Ishihara, A Kanervisto, J Miura… - Proceedings of the …, 2021 - openaccess.thecvf.com
Autonomous driving systems need to handle complex scenarios such as lane following,
avoiding collisions, taking turns, and responding to traffic signals. In recent years …
avoiding collisions, taking turns, and responding to traffic signals. In recent years …
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 …
End-to-end urban driving by imitating a reinforcement learning coach
End-to-end approaches to autonomous driving commonly rely on expert demonstrations.
Although humans are good drivers, they are not good coaches for end-to-end algorithms …
Although humans are good drivers, they are not good coaches for end-to-end algorithms …