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A Sequential Method for Automotive Millimeter-Wave Radar Self-Calibration Based on Optimization Shanghai Geometrical Perception and Learning Co,. Limited
- Format:
- Book
- Conference/Event
- Author/Creator:
- Pan, Song, author.
- Conference Name:
- SAE 2023 Intelligent and Connected Vehicles Symposium (2023-09-22 : Nanchang, China)
- Language:
- English
- Physical Description:
- 1 online resource cm
- Place of Publication:
- Warrendale, PA SAE International 2023
- Summary:
- Implementation calibration of automotive radar systems plays a fundamental but crucial role to guarantee sensor performance. The commonly used method relies on the environment such as a specific test station for static calibration or a straight metal guardrail for dynamic calibration. In this paper, a sequential method for estimating the radar angle misalignment derived from the Lagrange Multiplier Method in solving an optimization problem is proposed. The sequential method, which requires radar measurements and vehicle speed measurements as input, is more environment-free and can yield a consistent estimation. A simulation study is conducted to validate the consistency and analyze the influence of noise. The result shows that the radar azimuth measurement noise has little influence that the bias could be compensated and the effect of non-gaussianity is negligible. The radar velocity measurement noise bias and vehicle speed measurement noise bias have a linear effect whose coefficient depends on the radar orientation on the angle misalignment estimation. A real road testing is carried out and the result demonstrates that the method proposed could provide an estimation with an error no more than 0.26 degrees. An artificial error with 1 degree is added to the calibration result of the proposed method to study the miscalibration influence on sensor fusion based on the neural net. The result shows that the precision and recall deteriorate significantly with the artificial error, especially for the small targets like pedestrians, the reduction could reach 14% for precision and 17% for recall
- Notes:
- Vendor supplied data
- Publisher Number:
- 2023-01-7044
- Access Restriction:
- Restricted for use by site license
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