Event-Aided Time-to-Collision Estimation for Autonomous Driving (ECCV 2024)

Predicting a potential collision with leading vehicles is an essential functionality of any autonomous/assisted driving system. One bottleneck of existing vision-based solutions is that their updating rate is limited to the frame rate of standard cameras used. In this paper, we present a novel method that estimates the time to collision using a neuromorphic event-based camera, a biologically inspired visual sensor that can sense at exactly the same rate as scene dynamics. The core of the proposed algorithm consists of a two-step approach for efffcient and accurate geometric model fftting on event data in a coarse-to-ffne manner. The ffrst step is a robust linear solver based on a novel geometric measurement that overcomes the partial observability of event-based normal ffow. The second step further reffnes the resulting model via a spatio-temporal registration process formulated as a nonlinear optimization problem. Experiments on both synthetic and real data demonstrate the effectiveness of the proposed method, outperforming other alternative methods in terms of efffciency and accuracy.

Authors:
Jinghang Li, Bangyan Liao, Xiuyuan Lu, Peidong Liu, Shaojie Shen, Yi Zhou

PDF: https://arxiv.org/pdf/2407.07324
Project Page: nail-hnu.github.io/EventAidedTTC/

Contact:
Jinghang Li ([email protected]) or Yi Zhou ([email protected])