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surveydeep-rlmulti-agent-rlagent-modellingad-hoc-teamworkautonomous-drivinggoal-recognitionexplainable-aicausalgeneralisationsecurityemergent-communicationiterated-learningintrinsic-rewardsimulatorstate-estimationdeep-learningtransfer-learning
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Subramanian-Ramamoorthygoal-recognitionautonomous-driving
2022
Majd Hawasly, Jonathan Sadeghi, Morris Antonello, Stefano V. Albrecht, John Redford, Subramanian Ramamoorthy
Perspectives on the System-level Design of a Safe Autonomous Driving Stack
AI Communications, 2022
Abstract | BibTex | arXiv | Publisher
AICsurveyautonomous-drivinggoal-recognitionexplainable-ai
Abstract:
Achieving safe and robust autonomy is the key bottleneck on the path towards broader adoption of autonomous vehicles technology. This motivates going beyond extrinsic metrics such as miles between disengagement, and calls for approaches that embody safety by design. In this paper, we address some aspects of this challenge, with emphasis on issues of motion planning and prediction. We do this through description of novel approaches taken to solving selected sub-problems within an autonomous driving stack, in the process introducing the design philosophy being adopted within Five. This includes safe-by-design planning, interpretable as well as verifiable prediction, and modelling of perception errors to enable effective sim-to-real and real-to-sim transfer within the testing pipeline of a realistic autonomous system.
@article{albrecht2022aic,
author = {Majd Hawasly and Jonathan Sadeghi and Morris Antonello and Stefano V. Albrecht and John Redford and Subramanian Ramamoorthy},
title = {Perspectives on the System-level Design of a Safe Autonomous Driving Stack},
journal = {AI Communications, Special Issue on Multi-Agent Systems Research in the UK},
year = {2022}
}
2021
Stefano V. Albrecht, Cillian Brewitt, John Wilhelm, Balint Gyevnar, Francisco Eiras, Mihai Dobre, Subramanian Ramamoorthy
Interpretable Goal-based Prediction and Planning for Autonomous Driving
IEEE International Conference on Robotics and Automation, 2021
Abstract | BibTex | arXiv | Video | Code
ICRAautonomous-drivinggoal-recognitionexplainable-ai
Abstract:
We propose an integrated prediction and planning system for autonomous driving which uses rational inverse planning to recognise the goals of other vehicles. Goal recognition informs a Monte Carlo Tree Search (MCTS) algorithm to plan optimal maneuvers for the ego vehicle. Inverse planning and MCTS utilise a shared set of defined maneuvers and macro actions to construct plans which are explainable by means of rationality principles. Evaluation in simulations of urban driving scenarios demonstrate the system's ability to robustly recognise the goals of other vehicles, enabling our vehicle to exploit non-trivial opportunities to significantly reduce driving times. In each scenario, we extract intuitive explanations for the predictions which justify the system's decisions.
@inproceedings{albrecht2020igp2,
title={Interpretable Goal-based Prediction and Planning for Autonomous Driving},
author={Stefano V. Albrecht and Cillian Brewitt and John Wilhelm and Balint Gyevnar and Francisco Eiras and Mihai Dobre and Subramanian Ramamoorthy},
booktitle={IEEE International Conference on Robotics and Automation (ICRA)},
year={2021}
}
Josiah P. Hanna, Arrasy Rahman, Elliot Fosong, Francisco Eiras, Mihai Dobre, John Redford, Subramanian Ramamoorthy, Stefano V. Albrecht
Interpretable Goal Recognition in the Presence of Occluded Factors for Autonomous Vehicles
IEEE/RSJ International Conference on Intelligent Robots and Systems, 2021
Abstract | BibTex | arXiv
IROSautonomous-drivinggoal-recognitionexplainable-ai
Abstract:
Recognising the goals or intentions of observed vehicles is a key step towards predicting the long-term future behaviour of other agents in an autonomous driving scenario. When there are unseen obstacles or occluded vehicles in a scenario, goal recognition may be confounded by the effects of these unseen entities on the behaviour of observed vehicles. Existing prediction algorithms that assume rational behaviour with respect to inferred goals may fail to make accurate long-horizon predictions because they ignore the possibility that the behaviour is influenced by such unseen entities. We introduce the Goal and Occluded Factor Inference (GOFI) algorithm which bases inference on inverse-planning to jointly infer a probabilistic belief over goals and potential occluded factors. We then show how these beliefs can be integrated into Monte Carlo Tree Search (MCTS). We demonstrate that jointly inferring goals and occluded factors leads to more accurate beliefs with respect to the true world state and allows an agent to safely navigate several scenarios where other baselines take unsafe actions leading to collisions.
@inproceedings{hanna2021interpretable,
title={Interpretable Goal Recognition in the Presence of Occluded Factors for Autonomous Vehicles},
author={Josiah P. Hanna and Arrasy Rahman and Elliot Fosong and Francisco Eiras and Mihai Dobre and John Redford and Subramanian Ramamoorthy and Stefano V. Albrecht},
booktitle={IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)},
year={2021}
}
2020
Stefano V. Albrecht, Cillian Brewitt, John Wilhelm, Balint Gyevnar, Francisco Eiras, Mihai Dobre, Subramanian Ramamoorthy
Interpretable Goal-based Prediction and Planning for Autonomous Driving
arXiv:2002.02277, 2020
Abstract | BibTex | arXiv
autonomous-drivinggoal-recognitionexplainable-ai
Abstract:
We propose an integrated prediction and planning system for autonomous driving which uses rational inverse planning to recognise the goals of other vehicles. Goal recognition informs a Monte Carlo Tree Search (MCTS) algorithm to plan optimal maneuvers for the ego vehicle. Inverse planning and MCTS utilise a shared set of defined maneuvers and macro actions to construct plans which are explainable by means of rationality principles. Evaluation in simulations of urban driving scenarios demonstrate the system's ability to robustly recognise the goals of other vehicles, enabling our vehicle to exploit non-trivial opportunities to significantly reduce driving times. In each scenario, we extract intuitive explanations for the predictions which justify the system's decisions.
@misc{albrecht2020integrating,
title={Interpretable Goal-based Prediction and Planning for Autonomous Driving},
author={Stefano V. Albrecht and Cillian Brewitt and John Wilhelm and Balint Gyevnar and Francisco Eiras and Mihai Dobre and Subramanian Ramamoorthy},
year={2020},
eprint={2002.02277},
archivePrefix={arXiv},
primaryClass={cs.RO}
}