Event-Aided Time-to-Collision Estimation for Autonomous Driving
<|reference_start|>Event-Aided Time-to-Collision Estimation for Autonomous Driving: 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 efficient and accurate geometric model fitting on event data in a coarse-to-fine manner. The first step is a robust linear solver based on a novel geometric measurement that overcomes the partial observability of event-based normal flow. The second step further refines 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 efficiency and accuracy.<|reference_end|>
@article{li2024event-aided,
    title={Event-Aided Time-to-Collision Estimation for Autonomous Driving},
    author={Jinghang Li, Bangyan Liao, Xiuyuan LU, Peidong Liu, Shaojie Shen, Yi
  Zhou},
    journal={arXiv preprint arXiv:2407.07324},
    year={2024},
    archivePrefix={arXiv},
    eprint={2407.07324},
    primaryClass={cs.CV}
}