Highly automated driving requires intelligent and networked vehicles that can record their surroundings in detail and reliably using precise sensor technology. In addition to cameras, ultrasonic and lidar sensors, radar sensors are of great importance in the automobile of the future. Highest accuracy and reliability, e.g. for radar-based environment detection at long distances of more than 200 m, is essential. This is significantly influenced by signal scattering and attenuation, antenna quality and evaluation electronics, as well as algorithms. Due to these influences, the antenna, (secondary) radome (sensor cover) and evaluation electronics must be perfectly matched and integrated as a complete system as an essential prerequisite for precise detection. In order to minimize test and evaluation times, this also includes new approaches for functional testing through the use of artificial intelligence for sensor algorithms and sensor tests.
Aim of the project is the improvement of an automotive radar system along the manufacturing chain from the electronics architecture of the sensor, the development of an AI-supported test routine optimization of the sensor’s angular calibration, and the integration of resistive radome heating elements by means of additive functionalization on free-form packages. This is intended to increase the reliability of the radar system. A holistic approach is being pursued, which will be validated in the form of a demonstrator system at the end of the project.
- Improvement of automotive radar systems
- Holistic consideration of the production chain
- Optimization of the radar hardware architecture
- Sensor test time reduction through AI algorithms
- Integration of resistive heating elements by means of printed electronics on 3D geometries