Vision-Guided Forecasting -- Visual Context for Multi-Horizon Time Series Forecasting

TL;DR

This paper introduces a multi-modal forecasting approach for vehicle states in autonomous driving, combining visual and sensor data to improve prediction accuracy across multiple horizons, outperforming existing methods.

Contribution

It proposes three end-to-end architectures that fuse visual and sensor data for multi-horizon vehicle state forecasting, demonstrating significant performance improvements.

Findings

Vision features reduce forecasting error by over 50%.

Models with visual data outperform those without by large margins.

The approach achieves state-of-the-art results on public datasets.

Abstract

Autonomous driving gained huge traction in recent years, due to its potential to change the way we commute. Much effort has been put into trying to estimate the state of a vehicle. Meanwhile, learning to forecast the state of a vehicle ahead introduces new capabilities, such as predicting dangerous situations. Moreover, forecasting brings new supervision opportunities by learning to predict richer a context, expressed by multiple horizons. Intuitively, a video stream originated from a front-facing camera is necessary because it encodes information about the upcoming road. Besides, historical traces of the vehicle's states give more context. In this paper, we tackle multi-horizon forecasting of vehicle states by fusing the two modalities. We design and experiment with 3 end-to-end architectures that exploit 3D convolutions for visual features extraction and 1D convolutions for features extraction from speed and steering angle traces. To demonstrate the effectiveness of our method, we perform extensive experiments on two publicly available real-world datasets, Comma2k19 and the Udacity challenge. We show that we are able to forecast a vehicle's state to various horizons, while outperforming the current state-of-the-art results on the related task of driving state estimation. We examine the contribution of vision features, and find that a model fed with vision features achieves an error that is 56.6% and 66.9% of the error of a model that doesn't use those features, on the Udacity and Comma2k19 datasets respectively.