| NEW INTEGRATED MULTI-ALGORITHM FUSION LOCALIZATION AND
TRAJECTORY TRACKING FRAMEWORK OF AUTONOMOUS
VEHICLES UNDER EXTREME CONDITIONS WITH
NON-GAUSSIAN NOISES |
| Cong Liu 1, Hui Liu 1,2, Lijin Han 1,2, Changle Xiang 1,2 |
1National Key Lab of Vehicular Transmission, Beijing Institute of Technology
2Advanced Technology Research Institute, Beijing Institute of Technology |
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| ABSTRACT |
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This paper proposes a novel integrated multi-algorithm fusion localization and trajectory tracking framework
for autonomous vehicles under extreme conditions with non-Gaussian noises. Firstly, in order to solve the problem that GPS
signals are interfered with non-Gaussian noises or lost, a localization method based on Particle Filter (PF) is designed, which
takes full advantage of the reference objects position information and vehicle driving state information, thus realizing the
self-localization for high-speed autonomous vehicles. Besides, considering the accumulated errors of the model-driven
Inertial Measurement Unit (IMU) in the long-horizon positioning prediction, an online future driving state prediction
algorithm based on multi-order variable-step Markov model (MM) is proposed to calculate the future vehicle position in
scenarios without reference. The fusion of these two methods can give full play to their respective advantages, thus improving
the accuracy and robustness of the whole localization algorithm in scenes with non-Gaussian noises. Then, the location
information and the future driving state are applied to the trajectory tracking controller based on adaptive model predictive
control (AMPC). Finally, the CarSim-Matlab/Simulink cGAOo-simulations results show the effectiveness of the proposed
framework when GPS signal is interfered with non-Gaussian noises, which further improve the positioning accuracy and
autonomous tracking stability. |
| Key Words:
Fusion localization, Autonomous vehicles, Non-Gaussian noises, Markov, Future driving state prediction |
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Corresponding Author.
Hui Liu , Email. 
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