Virtual simulation technology helps reduce costs and increase efficiency in automated driving tests, accelerating the development of the smart car industry
Multi-party cross-border entry into smart cars has promoted the rapid development of autonomous driving. Looking at the pace of advancement of major car companies, BMW, Tesla, Volkswagen, Ford, FAW, SAIC, Weilai, etc., have all planned to deploy L3 and above high-end autonomous driving from 2021, and upgrade to L3 autonomous driving. The first year has arrived.
OEM, tier 1, test and measurement companies and other ecosystem vendors have gradually improved their layout. At the 9th EEVIA China Electronics ICT Media Forum and 2021 Industry and Technology Outlook Seminar, Guo Yu, senior customer manager for the automotive industry at NI, deeply analyzed autonomous driving. The challenge of testing, and explained how NI built a closed loop of automated driving testing with years of accumulation in the field of automotive testing and the “one platform strategy”.
The development trend of autonomous driving poses four major challenges to testing
As autonomous driving advances to a higher level, the autonomous driving platform will take over from the human brain to make driving decisions, and significantly increase the requirements for algorithms and AI capabilities. By continuously optimizing the ADAS algorithm, it can better identify the target and improve autonomous driving. The safety of the car. The development trend of the autonomous driving industry is not only that, in order to respond to the demand for massive data processing, the electrical and electronic structure is also changing, from the original sensors with ECUs to the subsequent use of central domain controllers for processing. The popularization of the “software-defined car” concept means that software will be deeply involved in vehicle development and verification. In addition, the current status quo is that regulations related to autonomous driving are not perfect, and the scene libraries of various manufacturers are not perfect.
The impact of these trends in the autonomous driving industry on vehicle testing is reflected in four aspects:
First, due to the increase in the number of DUTs and the degree of integration, the complexity of the test increases.
Second, with the continuous introduction of new technologies such as millimeter wave and 5G, the number of sensors continues to increase, and there are higher requirements for the openness and flexibility of the test system.
Third, the market iteration speeds up, and the test time is compressed.
Fourth, with the continuous improvement of vehicle functions, the complexity of the system has become higher and higher, and the complexity and cost of testing have increased, but the price of the vehicle has continued to decline, forcing the OEM and tier 1 to change the traditional testing strategy. In order to achieve the expected profit target.
The figure below is the industry-wide automotive V-shaped development process. Guo Yu analyzed that the closer to the right is the product stage test, the higher the test cost. The previous test strategy was to devote more energy to the test link on the right, but as the level of autonomous driving moves to a higher level, the subsequent software-defined car, multi-sensor fusion and other technologies have increased the complexity of the system and promoted For developers to change the testing strategy, what is more necessary is to shift the focus of testing to the left. In the initial stage of design and development, such as the software stage, a large number of tests are required.