Reproduce “TLD-READY: Traffic Light Detection -- Relevance Estimation and Deployment Analysis” on OpenResearch
Turn your favorite coding agents into research agents that review literature, develop hypotheses, and run experiments.
Universal app for Apple silicon and Intel. Requires macOS 11 or later.
About this paper
Nikolai Polley, Svetlana Pavlitska, Yacin Boualili, Patrick Rohrbeck, Paul Stiller, Ashok Kumar Bangaru, J. Marius Zöllner
TLD-READY addresses traffic-light perception by detecting signals and determining which apply to the vehicle’s lane. It detects directional road arrows, classifies their relevance to the vehicle, and matches them to traffic-light pictograms. On a held-out DTLD test set, its relevance classifier reports 0.96 precision, 0.96 recall, and 0.96 accuracy. This is not an end-to-end or real-world performance result.
Summary by alphaXiv.
Tip: change arxiv.org to autoarxiv.org in any arXiv abstract link to open the paper here.