Simulation enables training of deep learning algorithms in virtual driving tests

Simulation enables training of deep learning algorithms in virtual driving tests

Feature articles |
By Christoph Hammerschmidt

Artificial intelligence is already used in many different fields of the automotive industry as a term for the mechanical imitation of thought and learning structures in the human brain: For example, to make decisions on driver assistance systems or for object classification and interpretation, powered by the sensors installed on the vehicle. Real driving videos with appropriate metadata are made available to the system during the learning process so that a vehicle can really drive independently and automatically with the aid of AI algorithms. Different road markings, road users, buildings, parked cars, etc. with various visibility and weather conditions such as fog, rain, day or night complete the scenarios, so that appropriate reactions can be learned. Neural networks trained in this way can be used in the vehicle in real time to identify other road users or traffic signs, for example, and to trigger suitable actions even in complex traffic situations.

With the simulation solution CarMaker from the software company IPG Automotive, car electronics developers can create reproducible data virtually and automatically label them directly with 100 percent accuracy. With real data this is only possible with enormous additional effort. Different scenarios, object lists for decision or route planning algorithms or automatically labelled video data for object recognition algorithms can be used to train the neural networks. The huge amount of real test drives actually required for this is thus minimized through the use of virtual journeys; time and costs are reduced.

The software also makes it possible to integrate and test AI algorithms in different stages and characteristics in scenarios – over the entire development period. “The use of CarMaker for virtual testing of the fully trained algorithms in real scenarios and in the context of the entire vehicle enables a higher degree of maturity in the application of the algorithm in the real vehicle. If the software solution is also used on high-performance computing clusters, larger test catalogs can be covered simultaneously, automatically and with subsequent evaluation and test report generation,” explains Dominik Dörr, Business Development Manager Driver Assistance and Automated Driving at IPG Automotive.

CarMaker uses the Robot Operating Systems (ROS) to exchange information between individual processes and algorithms across computers. This open source middleware platform was originally developed for robotics, but is increasingly used for the development of automated motion functions.

CarMaker complements a ROS-based virtual vehicle development. The openness of both systems allows a problem-free coupling, and the architecture of both systems allows the implementation of all configurations required by the customer. In the CarMaker environment there is a ROS node as a shared library for this purpose. The AI algorithms can exchange information via this ROS node and are integrated into the CarMaker environment, which makes tests of algorithms in the holistic software environment possible. Separate observations and tests of individual parts of the algorithms within the simulation environment are also possible. CarMaker simulates the missing environment and offers the necessary flexibility for typical system and test designs.

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