# A Digital Twin prototype for traffic sign recognition of a learning-enabled autonomous vehicle

arXiv: https://arxiv.org/abs/2402.09097 · HTML: https://mlsanity.com/paper/2402.09097/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 14 of its 43 references that are in the map.
- First posted on arXiv 14 Feb 2024.

## Builds on (most cited first, 14 of 14)

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- [A Survey on Deep Neural Network Compression: Challenges, Overview, and Solutions](https://mlsanity.com/paper/2010.03954/) (2020)
- [Predictive Digital Twin for Optimizing Patient-Specific Radiotherapy Regimens under Uncertainty in High-Grade Gliomas](https://mlsanity.com/paper/2308.12429/) (2023)
- [On Neural Network Equivalence Checking using SMT Solvers](https://mlsanity.com/paper/2203.11629/) (2022)
- [Sim2real for Autonomous Vehicle Control using Executable Digital Twin](https://mlsanity.com/paper/2208.06226/) (2022)
- [The Executable Digital Twin: merging the digital and the physics worlds](https://mlsanity.com/paper/2210.17402/) (2022)
- [Simulation-based Safety Assurance for an AVP System incorporating Learning-Enabled Components](https://mlsanity.com/paper/2311.03362/) (2023)
- [SoS contract verification using statistical model checking](https://mlsanity.com/paper/1311.3632/) (2013)
- [Co-simulation: State of the art](https://mlsanity.com/paper/1702.00686/) (2017)
- [Co-simulation of Continuous Systems: A Tutorial](https://mlsanity.com/paper/1809.08463/) (2018)
- [Towards Digital Twin-enabled DevOps for CPS providing Architecture-Based Service Adaptation & Verification at Runtime](https://mlsanity.com/paper/2204.02022/) (2022)
- [Comparison between the HUBCAP and DIGITBrain Platforms for Model-Based Design and Evaluation of Digital Twins](https://mlsanity.com/paper/2212.07829/) (2022)
- [Digital Twin as a Service (DTaaS): A Platform for Digital Twin Developers and Users](https://mlsanity.com/paper/2305.07244/) (2023)
- [Smart Energy Network Digital Twins: Findings from a UK-Based Demonstrator Project](https://mlsanity.com/paper/2311.11997/) (2023)

## Abstract (arXiv)

In this paper, we present a novel digital twin prototype for a learning-enabled self-driving vehicle. The primary objective of this digital twin is to perform traffic sign recognition and lane keeping. The digital twin architecture relies on co-simulation and uses the Functional Mock-up Interface and SystemC Transaction Level Modeling standards. The digital twin consists of four clients, i) a vehicle model that is designed in Amesim tool, ii) an environment model developed in Prescan, iii) a lane-keeping controller designed in Robot Operating System, and iv) a perception and speed control module developed in the formal modeling language of BIP (Behavior, Interaction, Priority). These clients interface with the digital twin platform, PAVE360-Veloce System Interconnect (PAVE360-VSI). PAVE360-VSI acts as the co-simulation orchestrator and is responsible for synchronization, interconnection, and data exchange through a server. The server establishes connections among the different clients and also ensures adherence to the Ethernet protocol. We conclude with illustrative digital twin simulations and recommendations for future work.