On the road to autonomous driving, Audi continues powering ahead at top speed. The company is exhibiting an innovative pre-development project at the world’s most important symposium for artificial intelligence (AI) – the NIPS conference in Long Beach, California (USA). The project uses a mono camera that uses AI to generate an extremely precise 3D model of a vehicle’s environment. The conference is co-sponsored by Audi and is underway now.
The new Audi A8 is the first car in the world developed for conditional automated driving at Level 3 (SAE). The Audi AI traffic jam pilot handles the task of driving in slow-moving traffic up to 37mph, provided that laws in the market allow it and the driver selects it. A requirement for automated driving is a mapped image of the environment that is as precise as possible – at all times. Artificial intelligence is a key technology for this.
A project team from the Audi subsidiary Audi Electronics Venture (AEV) now is presenting a mono camera at the Conference and Workshop on Neural Information Processing Systems (NIPS) that uses artificial intelligence to generate an extremely precise 3D model of the environment. This technology makes it possible to capture the exact surroundings of the car.
A conventional front camera acts as the sensor. It captures the area in front of the car within an angle of about 120 degrees and delivers 15 images per second at a resolution of 1.3 megapixels. These images are then processed in a neural network.
This is where semantic segmenting occurs, in which each pixel is classified into one of 13 object classes. This enables the system to identify and differentiate other cars, trucks, houses, road markings, people and traffic signs.
The system also uses neural networks for distance information. The visualisation is performed here via ISO lines – virtual boundaries that define a constant distance. This combination of semantic segmenting and estimates of depth produces a precise 3D model of the actual environment.
Audi engineers had previously trained the neural network with the help of “unsupervised learning.” In contrast to supervised learning, unsupervised learning is a method of learning from observations of circumstances and scenarios that does not require pre-sorted and classified data. The neural network received numerous videos to view of road situations that had been recorded with a stereo camera. As a result, the network learned to independently understand rules, which it uses to produce 3D information from the images of the mono camera. The project of AEV holds great potential for the interpretation of traffic situations.