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

TL;DR

This paper introduces a digital twin prototype for autonomous vehicles that integrates multiple simulation components to perform traffic sign recognition and lane keeping, enhancing testing and development processes.

Contribution

The paper presents a novel digital twin architecture utilizing co-simulation standards and multiple specialized clients for autonomous vehicle functions.

Findings

Successful integration of vehicle, environment, control, and perception modules

Effective co-simulation with synchronization and data exchange

Illustrative simulations demonstrating digital twin capabilities

Abstract

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.