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The AutoNOMOS CN-Lab
The CN-Lab (Cognitive Navigation) develops the software for the controler and the behaviour of the autonomous car. This includes the algorithms for path planning, intersection recognition, obstacle avoidance, steer control and speed regulation.
For navigation, the car has to use the localization data from the GPS receiver, correct it by data from the cameras and the laser scaners and apply it to map data, stored as a digital road network definition file.
Using this combined data the vehicle can proceed from point to point on a collision free path. The goal is to develop a car that is capable of maneuvering in road traffic and reacting reliably to other road users and obstacles. The team members of CN-Lab are introduced here.
The controller contains all aspects of the engineering technology that is necessary to operate the car in an ever changing environment. Steering, acceleration, braking, parking must all be controlled in order to comply with the restrictions of the planned route. In order to produce as close as possible, a ‘human like’ driving experience, the controller will use techniques of machine learning in order to ‘learn’ and duplicate human driving actions and reactions.
Digital Road Map
The digital representation of the road network is based on a RNDF (Route Network Definition File). The RNDF contains information about the locations of all checkpoints, highways, roads, crossways, lanes. With this data a logical route can be planned.
The logical route taken by the car has to be adjusted continuously to the motion model of the car. This includes the car’s maneuvers like braking, turning, passing. The route also must be adjusted to the street regulations, i.e., to speed limits, traffic lights, stop signs.
Real car tests are costly and time consuming. Therefore, many driving functions are first tested in our simulated environment. A kinematic model of the car is deployed which simulates the car’s behavior in a virtual reality environment.