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Stefano-V.-Albrechtstate-estimationautonomous-driving
2024
Anthony Knittel, Majd Hawasly, Stefano V. Albrecht, John Redford, Subramanian Ramamoorthy
DiPA: Probabilistic Multi-Modal Interactive Prediction for Autonomous Driving
IEEE International Conference on Robotics and Automation, 2024
Abstract | BibTex | arXiv | Publisher
ICRAautonomous-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{Knittel2023dipa,
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},
volume={8},
number={8},
pages={4887--4894},
year={2023}
}
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 | Publisher
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{Knittel2023dipa,
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},
volume={8},
number={8},
pages={4887--4894},
year={2023}
}
2022
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}
}