# EDGAR: An Autonomous Driving Research Platform -- From Feature Development to Real-World Application

arXiv: https://arxiv.org/abs/2309.15492 · HTML: https://mlsanity.com/paper/2309.15492/

## Key facts

- Cited by 11 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 27 Sep 2023.

## Built on by (most cited first, 11 of 11)

- [FRENETIX: A High-Performance and Modular Motion Planning Framework for Autonomous Driving](https://mlsanity.com/paper/2402.01443/) (2024)
- [LLM4AD: Large Language Models for Autonomous Driving -- Concept, Review, Benchmark, Experiments, and Future Trends](https://mlsanity.com/paper/2410.15281/) (2024)
- [A Safe Reinforcement Learning driven Weights-varying Model Predictive Control for Autonomous Vehicle Motion Control](https://mlsanity.com/paper/2402.02624/) (2024)
- [Adaptive Stochastic Nonlinear Model Predictive Control with Look-ahead Deep Reinforcement Learning for Autonomous Vehicle Motion Control](https://mlsanity.com/paper/2311.04303/) (2023)
- [Traffic-Rule-Compliant Trajectory Repair via Satisfiability Modulo Theories and Reachability Analysis](https://mlsanity.com/paper/2412.15837/) (2024)
- [Integrating End-to-End and Modular Driving Approaches for Online Corner Case Detection in Autonomous Driving](https://mlsanity.com/paper/2409.01178/) (2024)
- [CaLiV: LiDAR-to-Vehicle Calibration of Arbitrary Sensor Setups](https://mlsanity.com/paper/2504.01987/) (2025)
- [A Hierarchical Test Platform for Vision Language Model (VLM)-Integrated Real-World Autonomous Driving](https://mlsanity.com/paper/2506.14100/) (2025)
- [A Feedback-Control Framework for Efficient Dataset Collection from In-Vehicle Data Streams](https://mlsanity.com/paper/2511.03239/) (2025)
- [BikeActions: An Open Platform and Benchmark for Cyclist-Centric VRU Action Recognition](https://mlsanity.com/paper/2601.10521/) (2026)
- [Mitigating Concept Drift in QoS Prediction for Teleoperation of Autonomous Vehicles Using Historic Data](https://mlsanity.com/paper/2610.08297/) (2026)

## Abstract (arXiv)

While current research and development of autonomous driving primarily focuses on developing new features and algorithms, the transfer from isolated software components into an entire software stack has been covered sparsely. Besides that, due to the complexity of autonomous software stacks and public road traffic, the optimal validation of entire stacks is an open research problem. Our paper targets these two aspects. We present our autonomous research vehicle EDGAR and its digital twin, a detailed virtual duplication of the vehicle. While the vehicle's setup is closely related to the state of the art, its virtual duplication is a valuable contribution as it is crucial for a consistent validation process from simulation to real-world tests. In addition, different development teams can work with the same model, making integration and testing of the software stacks much easier, significantly accelerating the development process. The real and virtual vehicles are embedded in a comprehensive development environment, which is also introduced. All parameters of the digital twin are provided open-source at https://github.com/TUMFTM/edgar_digital_twin.