Adding Navigation to the Equation: Turning Decisions for End-to-End Vehicle Control

Resource type
Christian Hubschneider and André Bauer and Michael Weber and J. Marius Zöllner
Yokohama, Japan
Book title
2017 IEEE 20th International Conference on Intelligent Transportation Systems (ITSC): Workshop (ITSC2017 - Workshop)
Navigation and obstacle avoidance are two problems that are not easily incorporated into direct control of autonomous vehicles solely based on visual input. However, they are required if lane following given proper lane markings is not enough to incorporate trained systems into larger architectures. This paper presents a method to allow for obstacle avoidance while driving using a single, front-facing camera as well as navigation capabilities such as taking turns at junctions and lane changes by feeding turn indicator signals into a Convolutional Neural Network. Both situations share the difficulty intrinsic to single camera setups of limited field of views. This problem is handled by using a spatial history of input images to extend the field of view regarding static obstacles. The trained model named DriveNet is evaluated in real world driving scenarios, using the same model for lateral vehicle control to both dynamically drive around obstacles as well as perform lane changing and turning in intersections.
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Published by
Christian Hubschneider