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面向互联和自动驾驶车辆的功能测试场景库生成研究综述

Review on Functional Testing Scenario Library Generation for Connected and Automated Vehicles.

机构信息

College of Computer Science and Technology, Jilin University, Changchun 130012, China.

出版信息

Sensors (Basel). 2022 Oct 12;22(20):7735. doi: 10.3390/s22207735.

Abstract

The advancement of autonomous driving technology has had a significant impact on both transportation networks and people's lives. Connected and automated vehicles as well as the surrounding driving environment are increasingly exchanging information. The traditional open road test or closed field test, which has large costs, lengthy durations, and few diverse test scenarios, cannot satisfy the autonomous driving system's need for reliable and safe testing. Functional testing is the emphasis of the test since features such as frontal collision and traffic sign warning influence driving safety. As a result, simulation testing will undoubtedly emerge as a new technique for unmanned vehicle testing. A crucial aspect of simulation testing is the creation of test scenarios. With an emphasis on the map generating method and the dynamic scenario production method in the test scenarios, this article explains many scenarios and scenario construction techniques utilized in the process of self-driving car testing. A thorough analysis of the state of relevant research is conducted, and approaches for creating common scenarios as well as brand-new methods based on machine learning are emphasized.

摘要

自动驾驶技术的发展对交通网络和人们的生活都产生了重大影响。联网和自动驾驶车辆以及周围的驾驶环境正在越来越多地交换信息。传统的开放式道路测试或封闭式场地测试成本高、周期长、测试场景单一,无法满足自动驾驶系统对可靠和安全测试的需求。功能测试是测试的重点,因为正面碰撞和交通标志警告等功能会影响驾驶安全。因此,模拟测试无疑将成为无人驾驶车辆测试的新技术。模拟测试的一个关键方面是测试场景的创建。本文重点介绍了测试场景中的地图生成方法和动态场景生成方法,解释了自动驾驶汽车测试过程中使用的许多场景和场景构建技术。对相关研究的现状进行了深入分析,强调了创建通用场景的方法以及基于机器学习的全新方法。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/8291/9606858/7ed829ea5233/sensors-22-07735-g001.jpg

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