Resilient Branching MPC for Multi-Vehicle Traffic Scenarios Using Adversarial Disturbance Sequences
(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:
https://lup.lub.lu.se/record/bdd11bd8-c802-4616-87ab-abefad9108f3
- author
- Fors, Victor ; Olofsson, Björn LU and Frisk, Erik
- organization
- publishing date
- 2022
- 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}}, }