Autonomous‐driving vehicle test technology based on virtual reality

Yao, Shouwen; Zhang, Jiahao; Hu, Ziran; Wang, Yu; Zhou, Xilin

doi:10.1049/joe.2018.8303

Cited by 24 publications

(9 citation statements)

References 5 publications

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“…Na et al (19) presented a method of building an autonomous driving simulation environment by building OpenDRIVE data using RoadRunner software and simply applying the data to the Carla (20) simulator. Yao et al (21) presented a virtual-reality-based test platform for autonomous driving built with AirSim (22) software and the UE4 engine to reduce the cost and risk of road testing for autonomous driving. The platform establishes a model library that includes a vehicle dynamics model, a sensor model, and a traffic environment model.…”

Section: Related Workmentioning

confidence: 99%

Development of System for Collecting User-specified Training Data for Autonomous Driving Based on Virtual Road Environment

Kim¹,

Jang²

2022

Sensors and Materials

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Deep learning technologies that use road images to recognize autonomous driving environments have been actively developed. Such deep-learning-based autonomous driving technologies need a large amount of training data that can represent various road, traffic, and weather environments. However, there have been many difficulties in terms of time and cost in collecting training data that can represent various road environments. Therefore, in this study, we attempt to build a virtual road environment and develop a system for collecting training data based on the virtual environment. To build a virtual environment identical to the real world, we convert and use two kinds of existing geospatial data: high-definition 3D buildings and highdefinition roads. We also develop a system for collecting training data running in the virtual environment. The implementation results of the proposed system show that it is possible to build a virtual environment identical to the real world and to collect specific training data quickly and at any time from the virtual environment with various user-specified settings.

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Section: Related Workmentioning

confidence: 99%

Development of System for Collecting User-specified Training Data for Autonomous Driving Based on Virtual Road Environment

Kim¹,

Jang²

2022

Sensors and Materials

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“…In this paper, a virtual reality-(VR-) based driving simulator is used to obtain the operation data of different drivers, roads, and environments. e driving simulator was developed by the Vision Simulation Laboratory of Beijing Institute of Technology (BIT) before [36], and the system architecture is illustrated in Figure 4.…”

Section: Acquisition Of Training Dataset Dataset Is the Basis Formentioning

confidence: 99%

Real-Time Energy Management Strategy Based on Driver-Action-Impact MPC for Series Hybrid Electric Vehicles

Ruan

2020

Complexity

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Precise prediction of future vehicle information can improve the control efficiency of hybrid electric vehicles. Nowadays, most prediction models use previous information of vehicles to predict future driving velocity, which cannot reflect the impact of the driver and the environment. In this paper, a real-time energy management strategy (EMS) based on driver-action-impact MPC is proposed for series hybrid electric vehicles. The proposed EMS consists of two modules: the velocity prediction module and the real-time MPC module. In the velocity prediction module, a long short-term memory (LSTM) neural network model, which is trained by the traffic data derived from a VR-based driving simulator, is adopted to predict the future driving information by using driver action information and current vehicle’s velocity. The obtained future driving velocity is treated as the inputs of the real-time MPC module, which outputs the control variables to act on the underlying controllers of power components by solving a standard quadratic programming (QP) problem. Compared with the rule-based strategy, a 5.6% average reduction of fuel consumption is obtained. The effectiveness of real-time computation of the EMS is validated and verified through a hardware-in-the-loop test platform.

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“…In Reference [29], considering the relationship between different types of vehicle kinematic simulation software, Minnerup and Knoll combine different simulation software to meet more simulation scenarios' requirement. A large-scale complex traffic network test environment is simulated in [30] based on Microsoft AirSim. The vehicle kinematic model, virtual reality environment model, automatic driving software, and radar sensors are combined to form their autonomous driving test platform in a virtual environment.…”

Section: Introductionmentioning

confidence: 99%

An Advanced System-Level Testing for Roadside Multimodal Sensing and Processing in IoV

Wang

Bao

et al. 2022

Wireless Communications and Mobile Computing

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Currently, there are mature test methods for specific sensing devices or processing devices in the Internet of Vehicles (IoV). However, when a system is combined with these different types of devices and algorithms for real scenarios, the existing device-level test results cannot reflect the comprehensive functional or performance requirements of the IoV applications at the system level. Therefore, novel application-oriented system-level evaluation indexes and test methods are needed. To this end, we extract the data processing functional entities into specific and quantifiable evaluation indexes by considering the IoV application functions and performance requirements. Then, we build a roadside sensing and processing test system in a real test zone to collect and process these evaluation indexes into accurate multidimensional ground-truth. According to the actual test results of multiple manufacturers’ solutions, our proposed test method is verified to effectively evaluate the performance of the system-level solutions in real IoV application scenarios. The unprecedented evaluation indexes, system-level test method, and the actual test results in this paper can provide an advanced reference for academics and industry.

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Autonomous‐driving vehicle test technology based on virtual reality

Cited by 24 publications

References 5 publications

Development of System for Collecting User-specified Training Data for Autonomous Driving Based on Virtual Road Environment

Development of System for Collecting User-specified Training Data for Autonomous Driving Based on Virtual Road Environment

Real-Time Energy Management Strategy Based on Driver-Action-Impact MPC for Series Hybrid Electric Vehicles

An Advanced System-Level Testing for Roadside Multimodal Sensing and Processing in IoV

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