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Ludovico-Carozza
2021
Henry Pulver, Francisco Eiras, Ludovico Carozza, Majd Hawasly, Stefano V. Albrecht, Subramanian Ramamoorthy
PILOT: Efficient Planning by Imitation Learning and Optimisation for Safe Autonomous Driving
IEEE/RSJ International Conference on Intelligent Robots and Systems, 2021
Abstract | BibTex | arXiv | Video
IROSautonomous-driving
Abstract:
Achieving a proper balance between planning quality, safety and efficiency is a major challenge for autonomous driving. Optimisation-based motion planners are capable of producing safe, smooth and comfortable plans, but often at the cost of runtime efficiency. On the other hand, naively deploying trajectories produced by efficient-to-run deep imitation learning approaches might risk compromising safety. In this paper, we present PILOT -- a planning framework that comprises an imitation neural network followed by an efficient optimiser that actively rectifies the network's plan, guaranteeing fulfilment of safety and comfort requirements. The objective of the efficient optimiser is the same as the objective of an expensive-to-run optimisation-based planning system that the neural network is trained offline to imitate. This efficient optimiser provides a key layer of online protection from learning failures or deficiency in out-of-distribution situations that might compromise safety or comfort. Using a state-of-the-art, runtime-intensive optimisation-based method as the expert, we demonstrate in simulated autonomous driving experiments in CARLA that PILOT achieves a seven-fold reduction in runtime when compared to the expert it imitates without sacrificing planning quality.
@inproceedings{pulver2020pilot,
title={{PILOT:} Efficient Planning by Imitation Learning and Optimisation for Safe Autonomous Driving},
author={Henry Pulver and Francisco Eiras and Ludovico Carozza and Majd Hawasly and Stefano V. Albrecht and Subramanian Ramamoorthy},
booktitle={IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)},
year={2021}
}
2020
Henry Pulver, Francisco Eiras, Ludovico Carozza, Majd Hawasly, Stefano V. Albrecht, Subramanian Ramamoorthy
PILOT: Efficient Planning by Imitation Learning and Optimisation for Safe Autonomous Driving
arXiv:2011.00509, 2020
Abstract | BibTex | arXiv
autonomous-driving
Abstract:
Achieving the right balance between planning quality, safety and runtime efficiency is a major challenge for autonomous driving research. Optimisation-based planners are typically capable of producing high-quality, safe plans, but at the cost of efficiency. We present PILOT, a two-stage planning framework comprising an imitation neural network and an efficient optimisation component that guarantees the satisfaction of requirements of safety and comfort. The neural network is trained to imitate an expensive-to-run optimisation-based planning system with the same objective as the efficient optimisation component of PILOT. We demonstrate in simulated autonomous driving experiments that the proposed framework achieves a significant reduction in runtime when compared to the optimisation-based expert it imitates, without sacrificing the planning quality.
@misc{pulver2020pilot,
title={{PILOT:} Efficient Planning by Imitation Learning and Optimisation for Safe Autonomous Driving},
author={Henry Pulver and Francisco Eiras and Ludovico Carozza and Majd Hawasly and Stefano V. Albrecht and Subramanian Ramamoorthy},
year={2020},
eprint={2011.00509},
archivePrefix={arXiv},
primaryClass={cs.RO}
}