Vehicle On-Board Sensing Technology
With the development of automated driving technology, the level of automation has increased, and the functional safety and robustness of the system itself has received more and more attention. However, whether it is based on millimeter wave radar, laser radar or camera-based recognition algorithm, due to their working principle and the frequency of the wave used, it has certain detection and perception limitations. So far, no single sensor can achieve a 100% detection rate.
Multi-source sensor Data Fusion
Due to the increasing of the safety requirements of the automated driving functions and the existence of various sensor detection and recognition errors, it is imperative to improve the quality of the environment-perception without relying solely on hardware technology. Multi-source sensor data fusion technology has been introduced into the field of automated driving. Multi-source sensor data fusion technology applies various types of sensors to the detection and measurement of targets in the same area, and through the post-processing of multi-channel sensor inputs, the sensors become complementary redundant systems to each other. Thus, the disadvantages of an individual sensor are complemented.
In theory, the technology is positive for the detection and measurement effects. However, in application, the wrong parameterization, the wrong selection of an inappropriate algorithm, or the wrong choice of the location where an algorithm to be used may result in a decrease in the algorithm's effect, or even an adverse effect. PilotD has accumulated a large amount of know-how and project experience in the field of multi-source sensor data fusion. We provide customers mainly with services to solve the following problems and ensure a high level of fusion efficiency：
1) Application-oriented algorithm selection and improvement；
2) fusion point selection in fusion or multi-layer fusion;
3) Robustness testing and analysis of the algorithm under the influence of various external influence factors;
4) The working range and accuracy calibration of the fusion algorithm;
5) Dynamic fusion algorithm optimization method;
6) Sensor data preprocessing and prediction of detection results based on information analysis.
Environment Understanding Based on the above described sensor data fusion results, we provide further algorithms on environmental understanding layer. These include: target classification, target intent analysis, target trajectory prediction, target hazard level pre-judgment and other types of post-processing possibilities. Finally, the driving environment is more exactly presented in front of the vehicle ECU through the sensor data, so that the customer's automated driving or advanced driver assistance system can achieve better driving and control ability.