Event-based Vision meets Deep Learning on Steering Prediction for Self-driving Cars

TL;DR

This paper introduces a deep learning approach that leverages event cameras for robust vehicle steering prediction, outperforming traditional camera-based methods especially in challenging lighting and fast motion scenarios.

Contribution

It adapts convolutional neural networks to event camera data and demonstrates superior performance over existing methods in autonomous driving tasks.

Findings

Event cameras enable robust steering prediction in challenging conditions.

Transfer learning improves event-based vision performance.

Our approach outperforms state-of-the-art camera-based algorithms.

Abstract

Event cameras are bio-inspired vision sensors that naturally capture the dynamics of a scene, filtering out redundant information. This paper presents a deep neural network approach that unlocks the potential of event cameras on a challenging motion-estimation task: prediction of a vehicle's steering angle. To make the best out of this sensor-algorithm combination, we adapt state-of-the-art convolutional architectures to the output of event sensors and extensively evaluate the performance of our approach on a publicly available large scale event-camera dataset (~1000 km). We present qualitative and quantitative explanations of why event cameras allow robust steering prediction even in cases where traditional cameras fail, e.g. challenging illumination conditions and fast motion. Finally, we demonstrate the advantages of leveraging transfer learning from traditional to event-based vision, and show that our approach outperforms state-of-the-art algorithms based on standard cameras.