Event-Based Vision Meets Deep Learning on Steering Prediction for Self-Driving Cars

TLDR

Event cameras capture scene dynamics while filtering redundant information, enabling robust steering prediction even under challenging illumination and fast motion where conventional cameras fail. The study proposes a deep neural network that predicts a vehicle’s steering angle from event camera data. The authors adapt state‑of‑the‑art convolutional architectures to event sensor outputs and evaluate the model on a publicly available large‑scale event‑camera dataset of roughly 1,000 km. Transfer learning from conventional to event‑based vision improves performance, and the proposed approach surpasses state‑of‑the‑art algorithms that rely on standard cameras.

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.