One Stack to Rule them All: To Drive Automated Vehicles, and Reach for the 4th level
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Abstract (arXiv)
Most automated driving functions are designed for a specific task or vehicle. Most often, the underlying architecture is fixed to specific algorithms to increase performance. Therefore, it is not possible to deploy new modules and algorithms easily. In this paper, we present our automated driving stack which combines both scalability and adaptability. Due to the modular design, our stack allows for a fast integration and testing of novel and state-of-the-art research approaches. Furthermore, it is flexible to be used for our different testing vehicles, including modified EasyMile EZ10 shuttles and different passenger cars. These vehicles differ in multiple ways, e.g. sensor setups, control systems, maximum speed, or steering angle limitations. Finally, our stack is deployed in real world environments, including passenger transport in urban areas. Our stack includes all components needed for operating an autonomous vehicle, including localization, perception, planning, controller, and additional safety modules. Our stack is developed, tested, and evaluated in real world traffic in multiple test sites, including the Test Area Autonomous Driving Baden-W"urttemberg.
Key facts. Cited by 5 papers in the map (data through 7 Oct 2026). First posted on arXiv 3 Apr 2024.
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Citing papers per year: 2024: 1; 2025: 3; 2026: 1.
5 papers.
- 2024TLD-READY: Traffic Light Detection -- Relevance Estimation and Deployment Analysiscited by 5
- 2025The ATLAS of Traffic Lights: A Reliable Perception Framework for Autonomous Drivingcited by 2
- 2025LimSim Series: An Autonomous Driving Simulation Platform for Validation and Enhancementcited by 0
- 2025Self-Supervised Pretraining for Aerial Road Extractioncited by 0
- 2026Real-World On-Vehicle Evaluation of Embedding-Based Anomaly Detectioncited by 0
- 2026Real-World On-Vehicle Evaluation of Embedding-Based Anomaly Detection19 May 2026
- 2025The ATLAS of Traffic Lights: A Reliable Perception Framework for Autonomous Driving28 Apr 2025
- 2025Self-Supervised Pretraining for Aerial Road Extraction31 Mar 2025
- 2025LimSim Series: An Autonomous Driving Simulation Platform for Validation and Enhancement13 Feb 2025
- 2024TLD-READY: Traffic Light Detection -- Relevance Estimation and Deployment Analysis11 Sep 2024