One-Shot Learning of Visual Path Navigation for Autonomous Vehicles
Zhongying CuiZhu Francois Charette Amin Ghafourian Debo Shi Matthew Cui Anjali Krishnamachar Iman Soltani
Autonomous driving presents many challenges due to the large number of
scenarios the autonomous vehicle (AV) may encounter. End-to-end deep learning
models are comparatively simplistic models that can handle a broad set of
scenarios. However, end-to-end models require large amounts of diverse data to
perform well. This paper presents a novel deep neural network that performs
image-to-steering path navigation that helps with the data problem by adding
one-shot learning to the system. Presented with a previously unseen path, the
vehicle can drive the path autonomously after being shown the path once and
without model retraining. In fact, the full path is not needed and images of
the road junctions is sufficient. In-vehicle testing and offline testing are
used to verify the performance of the proposed navigation and to compare
different candidate architectures.