  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.
