Memory Guided Road Detection
This addresses the need for reliable and efficient road detection in autonomous vehicles, representing an incremental improvement over existing methods.
The paper tackles the problem of lane prediction in self-driving cars by introducing a dynamic memory mechanism that propagates shared features over time, resulting in increased speed and robustness with minimal accuracy loss.
In self driving car applications, there is a requirement to predict the location of the lane given an input RGB front facing image. In this paper, we propose an architecture that allows us to increase the speed and robustness of road detection without a large hit in accuracy by introducing an underlying shared feature space that is propagated over time, which serves as a flowing dynamic memory. By utilizing the gist of previous frames, we train the network to predict the current road with a greater accuracy and lesser deviation from previous frames.