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autonomous-drivingSubramanian-Ramamoorthy
2023
Anthony Knittel, Majd Hawasly, Stefano V. Albrecht, John Redford, Subramanian Ramamoorthy
DiPA: Probabilistic Multi-Modal Interactive Prediction for Autonomous Driving
IEEE Robotics and Automation Letters, 2023
Abstract | BibTex | arXiv
RA-Lautonomous-drivingstate-estimation
Abstract:
Accurate prediction is important for operating an autonomous vehicle in
interactive scenarios. Prediction must be fast, to support multiple
requests from a planner exploring a range of possible futures. The
generated predictions must accurately represent the probabilities of
predicted trajectories, while also capturing different modes of
behaviour (such as turning left vs continuing straight at a junction).
To this end, we present DiPA, an interactive predictor that addresses
these challenging requirements. Previous interactive prediction methods
use an encoding of k-mode-samples, which under-represents the full
distribution. Other methods optimise closest-mode evaluations, which
test whether one of the predictions is similar to the ground-truth, but
allow additional unlikely predictions to occur, over-representing
unlikely predictions. DiPA addresses these limitations by using a
Gaussian-Mixture-Model to encode the full distribution, and optimising
predictions using both probabilistic and closest-mode measures. These
objectives respectively optimise probabilistic accuracy and the ability
to capture distinct behaviours, and there is a challenging trade-off
between them. We are able to solve both together using a novel training
regime. DiPA achieves new state-of-the-art performance on the
INTERACTION and NGSIM datasets, and improves over the baseline (MFP)
when both closest-mode and probabilistic evaluations are used. This
demonstrates effective prediction for supporting a planner on
interactive scenarios.
@article{brewitt2022verifiable,
title={{DiPA:} Probabilistic Multi-Modal Interactive Prediction for Autonomous Driving},
author={Anthony Knittel and Majd Hawasly and Stefano V. Albrecht and John Redford and Subramanian Ramamoorthy},
journal={IEEE Robotics and Automation Letters},
year={2023}
}
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}
}
Francisco Eiras, Majd Hawasly, Stefano V. Albrecht, Subramanian Ramamoorthy
A Two-Stage Optimization-based Motion Planner for Safe Urban Driving
IEEE Transactions on Robotics, 2022
Abstract | BibTex | arXiv | Publisher | Video
T-ROautonomous-driving
Abstract:
Recent road trials have shown that guaranteeing the safety of driving decisions is essential for the wider adoption of autonomous vehicle technology. One promising direction is to pose safety requirements as planning constraints in nonlinear, non-convex optimization problems of motion synthesis. However, many implementations of this approach are limited by uncertain convergence and local optimality of the solutions achieved, affecting overall robustness. To improve upon these issues, we propose a novel two-stage optimization framework: in the first stage, we find a solution to a Mixed-Integer Linear Programming (MILP) formulation of the motion synthesis problem, the output of which initializes a second Nonlinear Programming (NLP) stage. The MILP stage enforces hard constraints of safety and road rule compliance generating a solution in the right subspace, while the NLP stage refines the solution within the safety bounds for feasibility and smoothness. We demonstrate the effectiveness of our framework via simulated experiments of complex urban driving scenarios, outperforming a state-of-the-art baseline in metrics of convergence, comfort and progress.
@article{eiras2021twostage,
title = {A Two-Stage Optimization-based Motion Planner for Safe Urban Driving},
author = {Francisco Eiras and Majd Hawasly and Stefano V. Albrecht and Subramanian Ramamoorthy},
journal = {IEEE Transactions on Robotics},
volume = {38},
number = {2},
pages = {822--834},
year = {2022},
doi = {10.1109/TRO.2021.3088009}
}
Morris Antonello, Mihai Dobre, Stefano V. Albrecht, John Redford, Subramanian Ramamoorthy
Flash: Fast and Light Motion Prediction for Autonomous Driving with Bayesian Inverse Planning and Learned Motion Profiles
IEEE/RSJ International Conference on Intelligent Robots and Systems, 2022
Abstract | BibTex | arXiv
IROSautonomous-drivingstate-estimation
Abstract:
Motion prediction of road users in traffic scenes is critical for autonomous driving systems that must take safe and robust decisions in complex dynamic environments. We present a novel motion prediction system for autonomous driving. Our system is based on the Bayesian inverse planning framework, which efficiently orchestrates map-based goal extraction, a classical control-based trajectory generator and an ensemble of light-weight neural networks specialised in motion profile prediction. In contrast to many alternative methods, this modularity helps isolate performance factors and better interpret results, without compromising performance. This system addresses multiple aspects of interest, namely multi-modality, motion profile uncertainty and trajectory physical feasibility. We report on several experiments with the popular highway dataset NGSIM, demonstrating state-of-the-art performance in terms of trajectory error. We also perform a detailed analysis of our system's components, along with experiments that stratify the data based on behaviours, such as change lane versus follow lane, to provide insights into the challenges in this domain. Finally, we present a qualitative analysis to show other benefits of our approach, such as the ability to interpret the outputs.
@inproceedings{antonello2022flash,
title={Flash: Fast and Light Motion Prediction for Autonomous Driving with Bayesian Inverse Planning and Learned Motion Profiles},
author={Morris Antonello, Mihai Dobre, Stefano V. Albrecht, John Redford, Subramanian Ramamoorthy},
booktitle={IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)},
year={2022}
}
Anthony Knittel, Majd Hawasly, Stefano V. Albrecht, John Redford, Subramanian Ramamoorthy
DiPA: Diverse and Probabilistically Accurate Interactive Prediction
arXiv:2210.06106, 2022
Abstract | BibTex | arXiv
autonomous-drivingstate-estimation
Abstract:
Accurate prediction is important for operating an autonomous vehicle in interactive scenarios. Previous interactive predictors have used closest-mode evaluations, which test if one of a set of predictions covers the ground-truth, but not if additional unlikely predictions are made. The presence of unlikely predictions can interfere with planning, by indicating conflict with the ego plan when it is not likely to occur. Closest-mode evaluations are not sufficient for showing a predictor is useful, an effective predictor also needs to accurately estimate mode probabilities, and to be evaluated using probabilistic measures. These two evaluation approaches, eg. predicted-mode RMS and minADE/FDE, are analogous to precision and recall in binary classification, and there is a challenging trade-off between prediction strategies for each. We present DiPA, a method for producing diverse predictions while also capturing accurate probabilistic estimates. DiPA uses a flexible representation that captures interactions in widely varying road topologies, and uses a novel training regime for a Gaussian Mixture Model that supports diversity of predicted modes, along with accurate spatial distribution and mode probability estimates. DiPA achieves state-of-the-art performance on INTERACTION and NGSIM, and improves over a baseline (MFP) when both closest-mode and probabilistic evaluations are used at the same time.
@misc{brewitt2022verifiable,
title={{DiPA:} Diverse and Probabilistically Accurate Interactive Prediction},
author={Anthony Knittel and Majd Hawasly and Stefano V. Albrecht and John Redford and Subramanian Ramamoorthy},
year={2022},
eprint={2210.06106},
archivePrefix={arXiv},
primaryClass={cs.RO}
}
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}
}
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
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}
}
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}
}
Francisco Eiras, Majd Hawasly, Stefano V. Albrecht, Subramanian Ramamoorthy
Two-Stage Optimization-based Motion Planner for Safe Urban Driving
arXiv:2002.02215, 2020
Abstract | BibTex | arXiv
autonomous-driving
Abstract:
Recent road trials have shown that guaranteeing the safety of driving decisions is essential for the wider adoption of autonomous vehicle technology. One promising direction is to pose safety requirements as planning constraints in nonlinear, nonconvex optimization problems of motion synthesis. However, many implementations of this approach are limited by uncertain convergence and local optimality of the solutions achieved, affecting overall robustness. To improve upon these issues, we propose a novel two-stage optimization framework: in the first stage, we find a solution to a Mixed-Integer Linear Programming (MILP) formulation of the motion synthesis problem, the output of which initializes a second Nonlinear Programming (NLP) stage. The MILP stage enforces hard constraints of safety and road rule compliance generating a solution in the right subspace, while the NLP stage refines the solution within the safety bounds for feasibility and smoothness. We demonstrate the effectiveness of our framework via simulated experiments of complex urban driving scenarios, outperforming a state-of-the-art baseline in metrics of convergence, comfort and progress.
@misc{eiras2020twostage,
title={Two-Stage Optimization-based Motion Planner for Safe Urban Driving},
author={Francisco Eiras and Majd Hawasly and Stefano V. Albrecht and Subramanian Ramamoorthy},
year={2020},
eprint={2002.02215},
archivePrefix={arXiv},
primaryClass={cs.RO}
}