Augmented Reality-Enhanced Digital Twin for Automated Vehicle Testing

Optimization of energy flow in thermal management of electric vehicles based on real vehicle testing and digital twin simulation

Developing a vehicle is always a long and complex task. This is especially true for autonomous cars. Tasks performed by the driver are taken over by the vehicle and must be performed with maximum reliability. Developing these systems is a difficult task, especially due to limited testing capabilities. Testing a vehicle in a closed environment is safe and controlled, but the variety of test scenarios is limited. By using mixed reality environments, one can create a diverse environment around a real test vehicle, with traffic, obstacles, and unexpected situations. The real movement of the test vehicle allows testing decision and motion planning level vehicular functions. Information from the virtual world can be considered as input to the vehicle sensor. Mixed reality or digital twin simulation environments can greatly assist the autonomous vehicle development process and also serve as a basis for validation procedures for such systems. This article introduces a mixed reality simulation environment that integrates a real test vehicle into a virtual environment, can handle other real and virtual obstacles, contains traffic simulations, and visualizes all of these.

With the rapid development of 5G infrastructure, cloud computing and other technologies to enhance the rapid development of autonomous vehicles. How to quickly, safely and efficiently test and evaluate the autonomous vehicles is the only way for that to have the ability to drive on real public road. This paper build a set of automatic driving simulation test system based on digital twin technology, describing the necessary steps taken for the system design, system implementation, and testing of a autonomous vehicle. In the system the real traffic scene in the digital space re-emerged, to achieve the real vehicle running in the real environment, while mapping to the virtual environment to achieve the vehicle in the loop, making it possible to define driving test scenarios for different types of tractor and trailer combinations and providing the free test site for autonomous vehicles. At the same time, the system provides an open interface, to verify and test various autonomous algorithms. The sensor device of the tested autonomous system is flexibly configured, and the test cost is low and the efficiency is high.

<div class="section abstract"><div class="htmlview paragraph">With the advancement of automated driving system levels, corner scenarios characterized by low probability and high risk have become critical for the safety validation of automated vehicles. However, due to the typical long-tail distribution of such scenarios, data-driven mining approaches face significant challenges in achieving efficient generation. To address this issue, this study proposes a feature-optimized combination-based method for generating corner scenarios in automated driving systems. Key scenario features related to functional failures are first identified using a combined approach of system theoretic process analysis (STPA) and hazard and operability analysis (HAZOP). Based on these features, an adaptive genetic algorithm is employed to optimize feature combinations and generate large numbers of corner scenario types that meet specified constraints. The proposed method is validated using cut-in and pedestrian-crossing scenarios as baseline cases. The results show that this method enables large-scale generation of corner scenario types grounded in regulatory scenarios and provides significant support for the development of comprehensive corner scenario libraries for automated vehicle testing.</div></div>

Automated driving is a promising tool for reducing traffic accidents. While some companies claim that many cutting-edge automated driving functions have been developed, how to evaluate the safety of automated vehicles remains an open question, which has become a crucial bottleneck. Scenario-based testing has been introduced to test automated vehicles, and much progress has been achieved. While data-driven and knowledge-based approaches are hot research topics, this survey is mainly about Data-Driven Scenario Generation (DDSG) for automated vehicle testing. Rather than describe the contributions of every study respectively, in this survey, methodologies from various studies are anatomized as solutions for several significant problems and compared with each other. This way, scholars and engineers can quickly find state-of-the-art approaches to the issues they might encounter. Furthermore, several critical challenges that might hinder DDSG are described, and responding solutions are presented at the end of this survey.

This study presents a real-world performance assessment of a 48 V mild-hybrid urban bus equipped with a crankshaft starter–generator (CSG, denoted as KSG in German terminology), together with model-based validation for KSG Active operation. The 17.8-ton Euro VI test vehicle uses a 160 F supercapacitor module operated within a 38–52 V DC/DC converter voltage window (≈40 Wh usable) to buffer transient high-power events in stop-and-go duty. A controlled A/B comparison (KSG Active vs. KSG Passive) was performed using repeated 0–50–0 km/h launch cycles (15 test cycles per mode). Vehicle CAN signals were recorded using a datalogger and analyzed in Vector vSignalyzer 19.0. Field results show a 17.1% reduction in fuel consumption (32.21 to 26.70 L/100 km) and a 30.4% reduction in time-averaged ICE power demand (58.90 to 40.99 kW). A MATLAB/Simulink R2020a longitudinal dynamics digital twin was developed and validated for the KSG Active mode only against 20 Hz CAN measurements, achieving NRMSE below 5% for key variables. The findings should be interpreted as a controlled same-vehicle comparison under repeatable test-track conditions rather than as a certification-grade fleet-level benchmark.

One of the next-generation rotorcraft proposed is a compound helicopter with a pusher propeller. A pusher propeller was designed and is going to be tested on ground. For the test, a facility is to be determined. Digital twin was developed and applied to determine the test facility.

With China's automotive exports rising to the top globally, the diverse overseas market environments impose higher demands on vehicle adaptability. This paper proposes a "Global Environmental Quality Vehicle Testing and Evaluation System," constructing a multidimensional testing framework covering climate adaptability, road condition adaptability, and extreme disaster resilience, addressing the shortcomings of traditional environmental testing in scenario coverage and dynamic coupling validation. The study integrates digital twin technology and multi-objective optimization methods to establish dynamic operating condition models, with experimental validation confirming the system's effectiveness. Additionally, technology marketing strategies based on test results can significantly enhance brand competitiveness. This research provides theoretical and technical support for the globalization strategy of the automotive industry, offering both engineering practicality and academic innovation value.

Digital twins theory can provide an effective solution for the complex whole vehicle testing, which includes the dynamics and scenarios. The parallel system is the most advanced method for implementing parallel control theory. In this paper, it introduces the virtual systems established by digital modeling the autonomous driving vehicle and scenario, it introduces a connection between virtual system and the real system. There are two engineering applications based on digital twins and parallel intelligence to test the whole vehicle efficiently,its testing advantages and testing coverage.