Optical Flow Matters: an Empirical Comparative Study on Fusing Monocular Extracted Modalities for Better Steering

TL;DR

This paper presents an empirical study demonstrating that fusing optical flow and other modalities from a single monocular camera significantly improves autonomous vehicle steering accuracy, outperforming existing methods.

Contribution

It introduces a comprehensive framework combining optical flow with RGB and depth data using various neural architectures, advancing monocular-based autonomous navigation.

Findings

Reduces steering estimation error by 31%

Outperforms state-of-the-art models without optical flow

Shows robustness and reliability in diverse driving conditions

Abstract

Autonomous vehicle navigation is a key challenge in artificial intelligence, requiring robust and accurate decision-making processes. This research introduces a new end-to-end method that exploits multimodal information from a single monocular camera to improve the steering predictions for self-driving cars. Unlike conventional models that require several sensors which can be costly and complex or rely exclusively on RGB images that may not be robust enough under different conditions, our model significantly improves vehicle steering prediction performance from a single visual sensor. By focusing on the fusion of RGB imagery with depth completion information or optical flow data, we propose a comprehensive framework that integrates these modalities through both early and hybrid fusion techniques. We use three distinct neural network models to implement our approach: Convolution Neural Network - Neutral Circuit Policy (CNN-NCP) , Variational Auto Encoder - Long Short-Term Memory (VAE-LSTM) , and Neural Circuit Policy architecture VAE-NCP. By incorporating optical flow into the decision-making process, our method significantly advances autonomous navigation. Empirical results from our comparative study using Boston driving data show that our model, which integrates image and motion information, is robust and reliable. It outperforms state-of-the-art approaches that do not use optical flow, reducing the steering estimation error by 31%. This demonstrates the potential of optical flow data, combined with advanced neural network architectures (a CNN-based structure for fusing data and a Recurrence-based network for inferring a command from latent space), to enhance the performance of autonomous vehicles steering estimation.