Aiming at the deficiencies and deficiencies in the existing intelligent networked car event evaluation system, an innovative evaluation system based on cloud system automatic scoring is proposed. Conducting competitions through cloud servers is an effective way for international teams to participate in competitions and technical exchanges when international exchanges are affected by the epidemic. Taking typical ADAS functions as an example, the construction ideas, technical routes, program design, and achievement interface settings of the cloud automation evaluation system are researched and constructed. The effect of the cloud-based automated evaluation system for typical ADAS functions shows the convenience of cloud-based competition refereeing and the extensiveness of application-oriented objects.
Scenario definition for Automated Driving Function Validation is the state-of-the-art research area for autonomous driving, unification of the scenario description methods and language is the crucial step for the scenario definition. Based on the simulation scenario database established by China Automotive Technology and Research Center (CATARC) using natural language processing methods, to connect the human readable scenario definition with programmable scenario coding, provide the bridge between scenario definition with simulation testing, for better unify between scenario definition and virtual scenario building. With the participation of many enterprises, the corresponding test of automatic driving has become an access condition for the implementation of the corresponding technologies of automatic driving. Enterprises and institutions all over the world are trying to establish a unified scenario database to achieve the wide sharing and unification.
Multiple source sensor fusion is the foundation of motion planning for autonomous driving system, which is the crucial part in improving the performances for unmanned operational system. In this article, based on the deep learning platform CATARC constructed, applied with Udacity’s Lincoln MKZ multiple sensor data, implemented with Robotic Operation System, Computer Vision, PointCloud Library, Deep Neural Networks and Extended Kalman Filter, constructed a low-cost object pose estimation data fusion solution, aiming at technic support for the industrialization of autonomous driving technologies.
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