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The need for a vehicle to perceive information about the external environmental as an independent intelligent individual has grown with the progress of intelligent driving from primary driver assistance to high-level autonomous driving. The ability of a common independent sensing unit to sense the external environment is limited by the sensor’s own...
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... extended fusion network presents an overall improvement of 25.36% in the number of detected targets compared with the reference network, and the improvement in the detection capability of large sample targets, such as cars and pedestrians, is around 20% ( Table 2). The comparative analysis of target detection effects described in the previous section showed that the fusion target detection algorithm based on the proposed extended network significantly improves the detection effect compared with that of a single type of sensor. ...Context 2
... extended fusion network presents an overall improvement of 25.36% in the number of detected targets compared with the reference network, and the improvement in the detection capability of large sample targets, such as cars and pedestrians, is around 20% ( Table 2). The comparative analysis of target detection effects described in the previous section showed that the fusion target detection algorithm based on the proposed extended network significantly improves the detection effect compared with that of a single type of sensor. ...Similar publications
The effectiveness of training-based object detection heavily depends on the amount of sample data. But in the field of remote sensing, the amount of sample data is difficult to meet the needs of network training due to the non-cooperative imaging modes and complex imaging conditions. Moreover, the imbalance of the sample data between different cate...
Citations
This paper proposes a novel vehicle state estimation (VSE) method that combines a physics-informed neural network (PINN) and an unscented Kalman filter on manifolds (UKF-M). This VSE aimed to achieve inertial measurement unit (IMU) calibration and provide comprehensive information on the vehicle's dynamic state. The proposed method leverages a PINN to eliminate IMU drift by constraining the loss function with ordinary differential equations (ODEs). Then, the UKF-M is used to estimate the 3D attitude, velocity, and position of the vehicle more accurately using a six-degrees-of-freedom vehicle model. Experimental results demonstrate that the proposed PINN method can learn from multiple sensors and reduce the impact of sensor biases by constraining the ODEs without affecting the sensor characteristics. Compared to the UKF-M algorithm alone, our VSE can better estimate vehicle states. The proposed method has the potential to automatically reduce the impact of sensor drift during vehicle operation, making it more suitable for real-world applications.
