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Distributed state estimation for autonomous vehicles in unknown environments: Enhancing situational awareness
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DOI：10.1016/j.automatica.2026.113039.png)
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Abstract
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This paper investigates the real-time distributed state estimation problem for autonomous vehicles in unknown environments. During the process of autonomous driving, the distributed information of the multi-vehicle system, composed of the ego vehicle and its surrounding vehicles, must be perceived and comprehended by the ego vehicle. This process is known as situational awareness, during which the exact state information of surrounding vehicles might not be available. Furthermore, the perception accuracy of the ego vehicle regarding its environment is significantly reduced by unknown-but-bounded noises and unknown inputs. To address this issue, an innovative situational awareness scheme, based on the distributed set-membership estimation method, is proposed in this paper. First, the estimation error is decoupled from unknown inputs by using a designed unknown input estimator, which significantly enhances state estimation performance. Next, the impact of sparse topology in a multi-vehicle system on the solution of distributed set-membership state estimator gain matrices is analyzed, and a sparse optimization method is employed to obtain satisfactory estimator gain matrices. The parameters affecting estimation performance are optimized using the Lagrange multiplier method. It is shown that the ultimate boundedness of the estimation error is ensured under the proposed state estimation scheme. Moreover, a new dynamic collision warning scheme for autonomous vehicles is established based on the designed real-time state estimation algorithm. Finally, the effectiveness of the proposed method is validated through a simulation example.
Keywords:
Distributed state estimation
Situational awareness
Autonomous vehicles
Unknown input estimator
Set-membership estimation
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