Memory Guided Road Detection

TL;DR

This paper introduces a memory-guided architecture for road detection in self-driving cars that leverages temporal information from previous frames to improve speed and robustness without sacrificing accuracy.

Contribution

It proposes a dynamic memory mechanism that propagates shared features over time, enhancing road detection performance in autonomous driving systems.

Findings

Increased detection speed and robustness.

Reduced deviation from previous frame predictions.

Maintained high accuracy with temporal memory integration.

Abstract

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.