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ICRAautonomous-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
Filippos Christianos, Peter Karkus, Boris Ivanovic, Stefano V. Albrecht, Marco Pavone
Planning with Occluded Traffic Agents using Bi-Level Variational Occlusion Models
IEEE International Conference on Robotics and Automation, 2023
Abstract | BibTex | arXiv
ICRAdeep-rlautonomous-driving
Abstract:
Reasoning with occluded traffic agents is a significant open challenge for planning for autonomous vehicles. Recent deep learning models have shown impressive results for predicting occluded agents based on the behaviour of nearby visible agents; however, as we show in experiments, these models are difficult to integrate into downstream planning. To this end, we propose Bi-level Variational Occlusion Models (BiVO), a two-step generative model that first predicts likely locations of occluded agents, and then generates likely trajectories for the occluded agents. In contrast to existing methods, BiVO outputs a trajectory distribution which can then be sampled from and integrated into standard downstream planning. We evaluate the method in closed-loop replay simulation using the real-world nuScenes dataset. Our results suggest that BiVO can successfully learn to predict occluded agent trajectories, and these predictions lead to better subsequent motion plans in critical scenarios.
@inproceedings{christianos2023planning,
title={Planning with Occluded Traffic Agents using Bi-Level Variational Occlusion Models},
author={Filippos Christianos and Peter Karkus and Boris Ivanovic and Stefano V. Albrecht and Marco Pavone},
booktitle={International Conference on Robotics and Automation (ICRA)},
year={2023}
}
Cillian Brewitt, Massimiliano Tamborski, Cheng Wang, Stefano V. Albrecht
Verifiable Goal Recognition for Autonomous Driving with Occlusions
ICRA Workshop on Scalable Autonomous Driving, 2023
Abstract | BibTex | arXiv
ICRAautonomous-drivinggoal-recognitionexplainable-ai
Abstract:
Goal recognition (GR) allows the future behaviour of vehicles to be more accurately predicted. GR involves inferring the goals of other vehicles, such as a certain junction exit. In autonomous driving, vehicles can encounter many different scenarios and the environment is partially observable due to occlusions. We present a novel GR method named Goal Recognition with Interpretable Trees under Occlusion (OGRIT). We demonstrate that OGRIT can handle missing data due to occlusions and make inferences across multiple scenarios using the same learned decision trees, while still being fast, accurate, interpretable and verifiable. We also present the inDO and rounDO datasets of occluded regions used to evaluate OGRIT.
@misc{brewitt2023verifiable,
title={Verifiable Goal Recognition for Autonomous Driving with Occlusions},
author={Cillian Brewitt and Massimiliano Tamborski and Cheng Wang and Stefano V. Albrecht},
booktitle={ICRA 2023 Workshop on Scalable Autonomous Driving},
year={2023}
}
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}
}