# TLD-READY: Traffic Light Detection -- Relevance Estimation and Deployment Analysis

arXiv: https://arxiv.org/abs/2409.07284 · HTML: https://mlsanity.com/paper/2409.07284/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- Builds on 23 of its 69 references that are in the map.
- First posted on arXiv 11 Sep 2024.

## Builds on (most cited first, 23 of 23)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [Microsoft COCO: Common Objects in Context](https://mlsanity.com/paper/1405.0312/) (2014)
- [Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks](https://mlsanity.com/paper/1506.01497/) (2015)
- [End-to-End Object Detection with Transformers](https://mlsanity.com/paper/2005.12872/) (2020)
- [You Only Look Once: Unified, Real-Time Object Detection](https://mlsanity.com/paper/1506.02640/) (2015)
- [SSD: Single Shot MultiBox Detector](https://mlsanity.com/paper/1512.02325/) (2015)
- [YOLOv3: An Incremental Improvement](https://mlsanity.com/paper/1804.02767/) (2018)
- [YOLOv4: Optimal Speed and Accuracy of Object Detection](https://mlsanity.com/paper/2004.10934/) (2020)
- [YOLO9000: Better, Faster, Stronger](https://mlsanity.com/paper/1612.08242/) (2016)
- [YOLOv7: Trainable bag-of-freebies sets new state-of-the-art for real-time object detectors](https://mlsanity.com/paper/2207.02696/) (2022)
- [DETRs Beat YOLOs on Real-time Object Detection](https://mlsanity.com/paper/2304.08069/) (2023)
- [SCRDet++: Detecting Small, Cluttered and Rotated Objects via Instance-Level Feature Denoising and Rotation Loss Smoothing](https://mlsanity.com/paper/2004.13316/) (2020)
- [Robust Traffic Light Detection Using Salience-Sensitive Loss: Computational Framework and Evaluations](https://mlsanity.com/paper/2305.04516/) (2023)
- [Detecting Traffic Lights by Single Shot Detection](https://mlsanity.com/paper/1805.02523/) (2018)
- [Traffic Light Recognition Using Deep Learning and Prior Maps for Autonomous Cars](https://mlsanity.com/paper/1906.11886/) (2019)
- [CoCar NextGen: a Multi-Purpose Platform for Connected Autonomous Driving Research](https://mlsanity.com/paper/2404.17550/) (2024)
- [A Hierarchical Deep Architecture and Mini-Batch Selection Method For Joint Traffic Sign and Light Detection](https://mlsanity.com/paper/1806.07987/) (2018)
- [One Stack to Rule them All: To Drive Automated Vehicles, and Reach for the 4th level](https://mlsanity.com/paper/2404.02645/) (2024)
- [Towards Real-time Traffic Sign and Traffic Light Detection on Embedded Systems](https://mlsanity.com/paper/2205.02421/) (2022)
- [Traffic Light Recognition using Convolutional Neural Networks: A Survey](https://mlsanity.com/paper/2309.02158/) (2023)
- [Deep traffic light detection by overlaying synthetic context on arbitrary natural images](https://mlsanity.com/paper/2011.03841/) (2020)
- [DualCam: A Novel Benchmark Dataset for Fine-grained Real-time Traffic Light Detection](https://mlsanity.com/paper/2209.01357/) (2022)
- [Small, but important: Traffic light proposals for detecting small traffic lights and beyond](https://mlsanity.com/paper/2307.15191/) (2023)

## Built on by (most cited first, 5 of 5)

- [Enhancing LLM-based Autonomous Driving with Modular Traffic Light and Sign Recognition](https://mlsanity.com/paper/2511.14391/) (2025)
- [The ATLAS of Traffic Lights: A Reliable Perception Framework for Autonomous Driving](https://mlsanity.com/paper/2504.19722/) (2025)
- [Fool the Stoplight: Realistic Adversarial Patch Attacks on Traffic Light Detectors](https://mlsanity.com/paper/2506.04823/) (2025)
- [Enhancing Multimodal Large Language Models for Safety-Critical Driving Video Analysis](https://mlsanity.com/paper/2605.22185/) (2026)
- [One Perception, All Maneuvers: Directional Traffic Signal Understanding for Maneuver-Level Signal Intent Prediction](https://mlsanity.com/paper/2609.32316/) (2026)

## Abstract (arXiv)

Effective traffic light detection is a critical component of the perception stack in autonomous vehicles. This work introduces a novel deep-learning detection system while addressing the challenges of previous work. Utilizing a comprehensive dataset amalgamation, including the Bosch Small Traffic Lights Dataset, LISA, the DriveU Traffic Light Dataset, and a proprietary dataset from Karlsruhe, we ensure a robust evaluation across varied scenarios. Furthermore, we propose a relevance estimation system that innovatively uses directional arrow markings on the road, eliminating the need for prior map creation. On the DriveU dataset, this approach results in 96% accuracy in relevance estimation. Finally, a real-world evaluation is performed to evaluate the deployment and generalizing abilities of these models. For reproducibility and to facilitate further research, we provide the model weights and code: https://github.com/KASTEL-MobilityLab/traffic-light-detection.