Abstract
This paper introduces a novel approach for enhanced lane detection by integrating spatial, angular, and temporal information through light field imaging and novel deep learning models. Utilizing lenslet-inspired 2D light field representations and LSTM networks, our method significantly improves lane detection in challenging conditions. We demonstrate the efficacy of this approach with modified CNN architectures, showing superior per- formance over traditional methods. Our findings suggest this integrated data approach could advance lane detection technologies and inspire new models that leverage these multidimensional insights for autonomous vehicle percep- tion.
Abstract (translated)
本文提出了一种通过光场成像和一种新颖的深度学习模型将空间、角力和时间信息相结合的新方法,以增强道路检测。利用透镜阵列形式的2D光场表示和LSTM网络,我们的方法在具有挑战性的条件下显著提高了道路检测的精度。我们通过修改CNN架构来展示这种方法的有效性,并表明其显著优于传统方法。我们的研究结果表明,这种集成的数据方法可以推动道路检测技术的发展,并激发利用这些多维洞察力的新模型,以实现自动驾驶车辆感知。
URL
https://arxiv.org/abs/2405.02792
