A Vehicle-in-the-Loop Simulator with AI-Powered Digital Twins for Testing Automated Driving Controllers

TL;DR

This paper presents a practical vehicle-in-the-loop simulator using scaled physical cars and AI-powered digital twins to efficiently and accurately test automated driving controllers, reducing costs and improving fidelity.

Contribution

The development of a scalable, cost-effective simulator integrating scaled cars and AI-driven digital twins with formal safety guarantees is a novel approach.

Findings

The simulator effectively validates automated driving controllers.

Experimental results show high fidelity and safety in testing.

The system is easily extendable with existing software and control algorithms.

Abstract

Simulators are useful tools for testing automated driving controllers. Vehicle-in-the-loop (ViL) tests and digital twins (DTs) are widely used simulation technologies to facilitate the smooth deployment of controllers to physical vehicles. However, conventional ViL tests rely on full-size vehicles, requiring large space and high expenses. Also, physical-model-based DT suffers from the reality gap caused by modeling imprecision. This paper develops a comprehensive and practical simulator for testing automated driving controllers enhanced by scaled physical cars and AI-powered DT models. The scaled cars allow for saving space and expenses of simulation tests. The AI-powered DT models ensure superior simulation fidelity. Moreover, the simulator integrates well with off-the-shelf software and control algorithms, making it easy to extend. We use a filtered control benchmark with formal safety guarantees to showcase the capability of the simulator in validating automated driving controllers. Experimental studies are performed to showcase the efficacy of the simulator, implying its great potential in validating control solutions for autonomous vehicles and intelligent traffic.