# Jointly Learning Spatial, Angular, and Temporal Information for Enhanced Lane Detection

arXiv: https://arxiv.org/abs/2405.02792 · HTML: https://mlsanity.com/paper/2405.02792/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 4 of its 24 references that are in the map.
- First posted on arXiv 5 May 2024.

## Builds on (most cited first, 4 of 4)

- [Keep your Eyes on the Lane: Real-time Attention-guided Lane Detection](https://mlsanity.com/paper/2010.12035/) (2020)
- [VPGNet: Vanishing Point Guided Network for Lane and Road Marking Detection and Recognition](https://mlsanity.com/paper/1710.06288/) (2017)
- [Hybrid Light Field Imaging for Improved Spatial Resolution and Depth Range](https://mlsanity.com/paper/1611.05008/) (2016)
- [Trade-off Between Spatial and Angular Resolution in Facial Recognition](https://mlsanity.com/paper/2402.07263/) (2024)

## Abstract (arXiv)

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.