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Resilient Branching MPC for Multi-Vehicle Traffic Scenarios Using Adversarial Disturbance Sequences

Fors, Victor ; Olofsson, Björn LU and Frisk, Erik (2022) In IEEE Transactions on Intelligent Vehicles 7(4). p.838-848
Abstract
An approach to resilient planning and control of autonomous vehicles in multi-vehicle traffic scenarios is proposed. The proposed method is based on model predictive control (MPC), where alternative predictions of the surrounding traffic are determined automatically such that they are intentionally adversarial to the ego vehicle. This provides robustness against the inherent uncertainty in traffic predictions. To reduce conservatism, an assumption that other agents are of no ill intent is formalized. Simulation results from highway driving scenarios show that the proposed method in real-time negotiates traffic situations out of scope for a nominal MPC approach and performs favorably to state-of-the-art reinforcement-learning approaches... (More)
An approach to resilient planning and control of autonomous vehicles in multi-vehicle traffic scenarios is proposed. The proposed method is based on model predictive control (MPC), where alternative predictions of the surrounding traffic are determined automatically such that they are intentionally adversarial to the ego vehicle. This provides robustness against the inherent uncertainty in traffic predictions. To reduce conservatism, an assumption that other agents are of no ill intent is formalized. Simulation results from highway driving scenarios show that the proposed method in real-time negotiates traffic situations out of scope for a nominal MPC approach and performs favorably to state-of-the-art reinforcement-learning approaches without requiring prior training. The results also show that the proposed method performs effectively, with the ability to prune disturbance sequences with a lower risk for the ego vehicle. (Less)
Please use this url to cite or link to this publication:
author
; and
organization
publishing date
type
Contribution to journal
publication status
published
subject
in
IEEE Transactions on Intelligent Vehicles
volume
7
issue
4
pages
838 - 848
publisher
IEEE - Institute of Electrical and Electronics Engineers Inc.
external identifiers
  • scopus:85128678443
ISSN
2379-8858
DOI
10.1109/TIV.2022.3168772
project
ELLIIT B14: Autonomous Force-Aware Swift Motion Control
RobotLab LTH
language
English
LU publication?
yes
id
bdd11bd8-c802-4616-87ab-abefad9108f3
alternative location
https://arxiv.org/abs/2112.09551
date added to LUP
2022-07-04 15:17:08
date last changed
2023-04-24 21:05:50
@article{bdd11bd8-c802-4616-87ab-abefad9108f3,
  abstract     = {{An approach to resilient planning and control of autonomous vehicles in multi-vehicle traffic scenarios is proposed. The proposed method is based on model predictive control (MPC), where alternative predictions of the surrounding traffic are determined automatically such that they are intentionally adversarial to the ego vehicle. This provides robustness against the inherent uncertainty in traffic predictions. To reduce conservatism, an assumption that other agents are of no ill intent is formalized. Simulation results from highway driving scenarios show that the proposed method in real-time negotiates traffic situations out of scope for a nominal MPC approach and performs favorably to state-of-the-art reinforcement-learning approaches without requiring prior training. The results also show that the proposed method performs effectively, with the ability to prune disturbance sequences with a lower risk for the ego vehicle.}},
  author       = {{Fors, Victor and Olofsson, Björn and Frisk, Erik}},
  issn         = {{2379-8858}},
  language     = {{eng}},
  number       = {{4}},
  pages        = {{838--848}},
  publisher    = {{IEEE - Institute of Electrical and Electronics Engineers Inc.}},
  series       = {{IEEE Transactions on Intelligent Vehicles}},
  title        = {{Resilient Branching MPC for Multi-Vehicle Traffic Scenarios Using Adversarial Disturbance Sequences}},
  url          = {{http://dx.doi.org/10.1109/TIV.2022.3168772}},
  doi          = {{10.1109/TIV.2022.3168772}},
  volume       = {{7}},
  year         = {{2022}},
}