How can the reliability of the camera system be assessed in an autonomous vehicle?
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The reliability of the camera system in an autonomous vehicle can be assessed using various methods. One approach is to calibrate the camera intrinsically and periodically re-calibrate it to account for mechanical perturbations . Another method involves using a deep-learning-based self-calibration strategy that learns from driving scenes and is validated against checkerboard reprojection error . Additionally, the extrinsic calibration of visual-inertial sensors can be performed to ensure accurate perception of the environment . Furthermore, a novel evaluation metric called the detection quality index (DQI) can be used to assess the performance of camera-based object detection algorithms and provide frame-by-frame feedback on detection quality . These methods contribute to evaluating and improving the reliability of the camera system in autonomous vehicles.
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16 Oct 2022 1 Citations | The paper does not provide information on how the reliability of the camera system can be assessed in an autonomous vehicle. The paper focuses on proposing a deep-learning-based self-calibration strategy for vehicular cameras. |
| The paper does not provide information on how the reliability of the camera system can be assessed in an autonomous vehicle. The paper focuses on camera calibration using deep learning techniques. | |
| The paper does not provide information on how the reliability of the camera system can be assessed in an autonomous vehicle. |
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