This paper proposed a novel conflict decision model for intelligent vehicles based on game theory with analyzing the interaction behaviors between vehicles at urban unsignalized intersections. The proposed model can help intelligent vehicles cross intersections safely and more efficiently. Firstly, we developed an inference model for types of interactions among vehicles based on fuzzy logic. Then, the driving data was collected at urban unsignalized intersections by subgrade sensors and a retrofit intelligent vehicle and it was used in verifying the proposed inference model. After that, a conflict decision model considering safety, efficiency and comfort for intelligent vehicles based on game theory, was proposed to select the optimal driving strategies. Finally, a simulation and verification platform was built using Matlab/Simulink & Prescan. And the validity and effectiveness of the model were proved by simulation experiments. The results show the decision model can effectively help vehicles avoid conflicts and save their time spent in crossing intersections by 15 percent.
This paper proposes multi-frequency inertial and visual data fusion for attitude estimation. The proposed strategy is based on the locally weighted linear regression (LWLR), multi-layer perception (MLP), and cubature Kalman filter (CKF). First, we analyze the discrepant-frequency and the attitude divergence problems. Second, we construct the filter equation for the visual and inertial data and attitude differential equation for inertial-only data, which are used to estimate the attitude in time series. Third, we employ LWLR to compute the vision discrepancies between actual vision data and fitted vision data. The vision discrepancy is used as the input of MLP training. In MLP, the discrepancy is used as weights of the sums through the activation function of the hidden layer. To address the divergence problem, which is inherent in a multifrequency fusion, the MLP is utilized to compensate for the inertial-only data. Finally, experimental results on different environments of pseudo-physical simulations show the superior performance of the proposed method in terms of the accuracy of attitude estimation and divergence capability.
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