Project description
The desire for highly automated driving requires intelligent and networked vehicles that can detect 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 car of the future. Maximum accuracy and reliability, e.g. for radar-based environment detection at long distances of over 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 to functional testing through the use of artificial intelligence for sensor algorithms and sensor tests.
The aim of the project is to improve an automotive radar system along the production chain from the electronic architecture of the sensor, the development of an AI-supported test routine optimization of the angular calibration of the sensor and the integration of resistive radome heating elements by means of additive functionalization on free-form housings. This is intended to increase the reliability of the radar system. A holistic approach is being pursued, which is to be validated in the form of a demonstrator system at the end of the project.
Project goals
- Improvement of automotive radar systems
- Holistic view of the production chain
- Optimization of the radar hardware architecture
- Sensor test time reduction through AI algorithms
- Integration of resistive heating elements using printed electronics on 3D geometries
