基于 3D 方法的车内驾驶风格调查系统的设置

Dejie Ji, Maximilian Flormann, Joana M. Warnecke, Roman Henze, T. Deserno
{"title":"基于 3D 方法的车内驾驶风格调查系统的设置","authors":"Dejie Ji, Maximilian Flormann, Joana M. Warnecke, Roman Henze, T. Deserno","doi":"10.4271/2024-01-3001","DOIUrl":null,"url":null,"abstract":"Investigating human driver behavior enhances the acceptance of the autonomous driving and increases road safety in heterogeneous environments with human-operated and autonomous vehicles. The previously established driver fingerprint model, focuses on the classification of driving styles based on CAN bus signals. However, driving styles are inherently complex and influenced by multiple factors, including changing driving environments and driver states. To comprehensively create a driver profile, an in-car measurement system based on the Driver-Driven vehicle-Driving environment (3D) framework is developed. The measurement system records emotional and physiological signals from the driver, including the ECG signal and heart rate. A Raspberry Pi camera is utilized on the dashboard to capture the driver's facial expressions and a trained convolutional neural network (CNN) recognizes emotion. To conduct unobtrusive ECG measurements, an ECG sensor is integrated into the steering wheel. Additionally, the system accesses CAN bus signals from the vehicle to assess the driver’s driving style, extracting signals related to longitudinal and lateral control behavior from the Drive-CAN (A-CAN). Recognizing that variables from the driving environment can influence driving style, such as traffic signs and road conditions, a windshield-mounted webcam is integrated into the measurement system. This setup enables real-time detection of common traffic signs and assessment of road conditions, distinguishing between dry, wet, or icy road surfaces. Augmenting of the image data from camera, signals from in-car ADAS-sensors, such as the distance measured by the front radar in relation to neighboring vehicles, are integrated for a comprehensive analysis of driving style. The established measurement system is presently implemented in a test vehicle, poised to investigate the interplay between the 3D-parameters, with a focus on driving style of human driver.","PeriodicalId":510086,"journal":{"name":"SAE Technical Paper Series","volume":null,"pages":null},"PeriodicalIF":0.0000,"publicationDate":"2024-07-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Set-Up of an In-Car System for Investigating Driving Style on the Basis of the 3D-Method\",\"authors\":\"Dejie Ji, Maximilian Flormann, Joana M. Warnecke, Roman Henze, T. Deserno\",\"doi\":\"10.4271/2024-01-3001\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"Investigating human driver behavior enhances the acceptance of the autonomous driving and increases road safety in heterogeneous environments with human-operated and autonomous vehicles. The previously established driver fingerprint model, focuses on the classification of driving styles based on CAN bus signals. However, driving styles are inherently complex and influenced by multiple factors, including changing driving environments and driver states. To comprehensively create a driver profile, an in-car measurement system based on the Driver-Driven vehicle-Driving environment (3D) framework is developed. The measurement system records emotional and physiological signals from the driver, including the ECG signal and heart rate. A Raspberry Pi camera is utilized on the dashboard to capture the driver's facial expressions and a trained convolutional neural network (CNN) recognizes emotion. To conduct unobtrusive ECG measurements, an ECG sensor is integrated into the steering wheel. Additionally, the system accesses CAN bus signals from the vehicle to assess the driver’s driving style, extracting signals related to longitudinal and lateral control behavior from the Drive-CAN (A-CAN). Recognizing that variables from the driving environment can influence driving style, such as traffic signs and road conditions, a windshield-mounted webcam is integrated into the measurement system. This setup enables real-time detection of common traffic signs and assessment of road conditions, distinguishing between dry, wet, or icy road surfaces. Augmenting of the image data from camera, signals from in-car ADAS-sensors, such as the distance measured by the front radar in relation to neighboring vehicles, are integrated for a comprehensive analysis of driving style. The established measurement system is presently implemented in a test vehicle, poised to investigate the interplay between the 3D-parameters, with a focus on driving style of human driver.\",\"PeriodicalId\":510086,\"journal\":{\"name\":\"SAE Technical Paper Series\",\"volume\":null,\"pages\":null},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2024-07-02\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"SAE Technical Paper Series\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.4271/2024-01-3001\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"SAE Technical Paper Series","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.4271/2024-01-3001","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 0

摘要

对人类驾驶员行为的研究可提高人们对自动驾驶的接受程度,并在有人类驾驶车辆和自动驾驶车辆的异构环境中提高道路安全性。之前建立的驾驶员指纹识别模型侧重于根据 CAN 总线信号对驾驶风格进行分类。然而,驾驶风格本身十分复杂,并受到多种因素的影响,包括不断变化的驾驶环境和驾驶员状态。为了全面建立驾驶员特征,我们开发了基于驾驶员驱动车辆-驾驶环境(3D)框架的车载测量系统。该测量系统可记录驾驶员的情绪和生理信号,包括心电图信号和心率。仪表盘上的树莓派(Raspberry Pi)摄像头用于捕捉驾驶员的面部表情,训练有素的卷积神经网络(CNN)可识别情绪。为了进行不显眼的心电图测量,方向盘上集成了一个心电图传感器。此外,该系统还能访问来自车辆的 CAN 总线信号,以评估驾驶员的驾驶风格,并从 Drive-CAN (A-CAN) 中提取与纵向和横向控制行为相关的信号。由于认识到驾驶环境中的变量会影响驾驶风格,例如交通标志和路况,因此测量系统中集成了一个安装在挡风玻璃上的网络摄像头。这种设置可以实时检测常见的交通标志和评估路况,区分干燥、潮湿或结冰的路面。除了摄像头提供的图像数据外,车载 ADAS 传感器(如前雷达测得的与邻近车辆的距离)提供的信号也被整合进来,以便对驾驶风格进行综合分析。已建立的测量系统目前正在测试车辆中实施,准备研究三维参数之间的相互作用,重点是人类驾驶员的驾驶风格。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Set-Up of an In-Car System for Investigating Driving Style on the Basis of the 3D-Method
Investigating human driver behavior enhances the acceptance of the autonomous driving and increases road safety in heterogeneous environments with human-operated and autonomous vehicles. The previously established driver fingerprint model, focuses on the classification of driving styles based on CAN bus signals. However, driving styles are inherently complex and influenced by multiple factors, including changing driving environments and driver states. To comprehensively create a driver profile, an in-car measurement system based on the Driver-Driven vehicle-Driving environment (3D) framework is developed. The measurement system records emotional and physiological signals from the driver, including the ECG signal and heart rate. A Raspberry Pi camera is utilized on the dashboard to capture the driver's facial expressions and a trained convolutional neural network (CNN) recognizes emotion. To conduct unobtrusive ECG measurements, an ECG sensor is integrated into the steering wheel. Additionally, the system accesses CAN bus signals from the vehicle to assess the driver’s driving style, extracting signals related to longitudinal and lateral control behavior from the Drive-CAN (A-CAN). Recognizing that variables from the driving environment can influence driving style, such as traffic signs and road conditions, a windshield-mounted webcam is integrated into the measurement system. This setup enables real-time detection of common traffic signs and assessment of road conditions, distinguishing between dry, wet, or icy road surfaces. Augmenting of the image data from camera, signals from in-car ADAS-sensors, such as the distance measured by the front radar in relation to neighboring vehicles, are integrated for a comprehensive analysis of driving style. The established measurement system is presently implemented in a test vehicle, poised to investigate the interplay between the 3D-parameters, with a focus on driving style of human driver.
求助全文
通过发布文献求助,成功后即可免费获取论文全文。 去求助
来源期刊
自引率
0.00%
发文量
0
×
引用
GB/T 7714-2015
复制
MLA
复制
APA
复制
导出至
BibTeX EndNote RefMan NoteFirst NoteExpress
×
提示
您的信息不完整,为了账户安全,请先补充。
现在去补充
×
提示
您因"违规操作"
具体请查看互助需知
我知道了
×
提示
确定
请完成安全验证×
copy
已复制链接
快去分享给好友吧!
我知道了
右上角分享
点击右上角分享
0
联系我们:info@booksci.cn Book学术提供免费学术资源搜索服务,方便国内外学者检索中英文文献。致力于提供最便捷和优质的服务体验。 Copyright © 2023 布克学术 All rights reserved.
京ICP备2023020795号-1
ghs 京公网安备 11010802042870号
Book学术文献互助
Book学术文献互助群
群 号:481959085
Book学术官方微信