CN113795416A - 用于预测并减轻由晕车引起的干扰的方法 - Google Patents
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Abstract
本发明涉及一种用于预测并减轻在车辆(1)的驾驶操作中乘员(2)受到的由晕车引起的干扰的方法,其中,该乘员(2)至少借助车辆摄像头(4)被检测。根据本发明,依据作用于乘员(2)的刺激、乘员(2)对晕车的个人易感性、在驾驶操作期间所做活动的类型来确定说明由晕车引起的干扰的出现概率的特征数,并且依据所确定的特征数向乘员(2)建议或自动启动用于预防由晕车引起的干扰的措施目录中的至少一项单独措施。
Description
技术领域
本发明涉及一种用于预测并减轻或避免乘员在车辆驾驶操作中由晕车引起的乘员干扰的方法,其中,至少借助车辆摄像头来监测乘员。
背景技术
从DE 10 2017 206 012 B3中知道了一种用于操作车辆的方法。该方法规定,至少一个第一传感器单元检测至少一位车辆乘员的至少一个身体部位的运动和/或与车辆乘员互动的至少一个对象的运动。借助控制单元,依据检测到的运动来控制至少一个驾驶系统。在此,检测到的运动是与时间相关的至少一个振动过程,该振动过程依据至少一个振动的幅值和/或频率被检测。
发明内容
本发明的目的是提供一种相比于现有技术有所改进的用于预测并减轻在车辆驾驶操作中乘员出现的由晕车引起的干扰的方法。
根据本发明,该任务通过如权利要求1所述的特征来完成。
本发明的有利设计方案是从属权利要求的主题。
本发明涉及一种用于预测并减轻或避免驾驶操作中乘员出现的由晕车引起的干扰的方法,其中,该乘员至少借助车辆摄像头被监测。根据本发明,依据作用于乘员的刺激、乘员的对晕车的个体易感性和在驾驶操作期间所做的活动的类型来确定特征数,用以表明或预测由晕车引起的干扰的出现概率。依据所确定的特征数,向乘员建议或自动启动用于预防由晕车引起的干扰的措施目录中的至少一项单独措施。
通过使用该方法,允许乘员能够高效地利用驾驶事件来进行活动,而不存在乘员出现由晕车引起的干扰的危险。对于尤其易感的人,该程序也可以被用以防止或缓解晕车而不需要做附加活动。
例如针对乘员作为可激活的防晕车程序将该方法用在车辆中,尤其是在车辆的自主驾驶操作中。
在此,该方法一方面包含针对由晕车引起的潜在乘员干扰的预测,另一方面包含用于预防出现由晕车引起的干扰的其它单独措施、即应对措施。
在该方法的一个实施方式中,根据乘员的分心度和专注度和/或关注需要、乘员的所需视觉动态、乘员的所需长期关注度和/或略微视线转移和/或在车辆内的乘员的坐姿,某类活动被归入活动分类、即被聚类。因此,所述活动关于其形式被概括,从而能以简化方式确定减轻由晕车引起的干扰的应对措施。
在该方法的另一个实施方式中,为了确定作用于乘员的若干刺激,借助车载加速度传感器系统采集信号,以确定车辆的车身运动、横向动态、摇摆运动和/或俯仰运动。由此可以借助于至少一个合适的应对措施例如像相应的底盘调整来加以抑制。
该方法的一个改进方案规定,为了确定作用于乘员的刺激,依据数字地图信息和/或依据车辆导航系统数据来针对车辆前方的行程确定车辆运动。因此,导致由晕车引起的干扰的车辆运动可被预测,从而可以事先采取相应的应对措施以便能明显降低出现由晕车引起的干扰的危险。
为了确定乘员易受由晕车引起的干扰的影响,在该方法的另一个可能实施方式中,依据填写的调查表来确定乘员的个体易感性。为此,乘员完成尤其标准化的调查表,其中,仅填一次调查表,并且所确定的易感性值随后被存储在相应乘员的档案中。如果乘员的易感性改变,则乘员的个体易感性值被调整。在此,所述调整是一个反复过程。
在该方法的一个可能改进方案中,依据针对车辆前方的行程所预测的车辆运动,为了减轻作用于乘员的刺激而向乘员建议采用替代路线。特别是当所有其它可供使用的应对措施不能防止出现由晕车引起的干扰且尤其是乘员将会遭受恶心和/或头痛时,建议驶上替代路线。
为了减轻由晕车引起的干扰,在该方法的另一个实施方式中规定,调整车辆的行驶动态,依据行车特点和/或交通流量来执行运动轨迹规划,依据座椅设定来稳固该乘员,控制车内照明,调整显示单元的设定和/或完成舒适性设定如芳香、温度、按摩等。
此外,该方法在一个设计方案中规定,为了验证所启动的单独措施的效果而将所启动的单独措施的调整参数反馈到与车辆相连的中央计算机单元中,由此,关于减轻由晕车引起的干扰的措施目录被改进。因此,与另一种提出的座椅设定相比,例如其中一种座椅设定可能导致完全没有头晕,而乘员会因所述提出的座椅设定头晕。因此缘故,进行所述反馈,使得该座椅设定附加地或替代地可被纳入措施目录中且因此提供给其它车辆的乘员。
附图说明
以下结合图来详述本发明的实施例。附图在此示出:
图1示意性示出车辆局部连同乘员的透视图。
具体实施方式
图1示出车辆1局部连同位于车座3上的乘员2。乘员2是车辆使用者、即车辆1的司机,其按照车辆1的人工驾驶方式完成驾驶任务。
车辆1具备用于自主/自动驾驶操作的辅助系统,因此乘员2能够在自主驾驶操作期间从事其它活动。根据图1中的本实施例,乘员2在车辆1自主驾驶至其规定目的地的期间读书。
因为乘员2(其也可以是副驾乘客或其它乘员2)从驾驶过程分心并且关注其它活动、尤其是读书,故存在出现由晕车引起的干扰的危险,其中,晕车被称为旅途病或晕动病。
如果车辆1不具备用于自主驾驶操作的辅助系统,副驾或车辆1后座区的其它乘员2也可能出现晕车。
晕车是指身体反应如脸色苍白、头痛、恶心、呕吐和头晕,这些是乘员2由于特别是在车辆1内的异常运动而产生。身体反应被称为由晕车引起的干扰或症状。
为了对乘员2来说能够将车辆1的驾驶操作安排得尽可能舒适,规定了下述的用于预测并减轻由晕车引起的干扰的方法。
在车辆1中,即在其乘员室中,设有车辆摄像头4,其在驾驶操作中连续采集图像数据,其中,该车辆摄像头4配置和定位成使得位于车辆1内的乘员2处于车辆摄像头4的检测范围内。作为其替代方式,安装在车辆1内的车辆摄像头4的数量可以改变,因而有至少两个车辆摄像头4。
该方法包括用于预测由晕车引起的干扰的算法,其中,为了确定特征数需要多个不同的输入参数。例如,该算法基于的是递归模型和/或与学习系统结合使用的聚类方法。
该算法在此由两个彼此独立的或合作的结构级来组成。
该算法的结构级1通过统计模型/随机模型限定,其通过线性混合模型在真实驾驶环境中来创建并验证。潜在变量被逐个(“正向选择”)纳入递归模型中。依据统计质量标准,表明拟合模型的潜在改善,进而还有变量与晕车表现的相关性。所识别的变量的相应表现现在关于其晕车诱发(即由此造成或加强晕车出现的程度)被分级并且被集成到统计模型中。相应的传感器长期或定期测量或预测变量的状态。如果通过人工调整或自动化启动来改变变量的表现,因此预测模型的状态也被调整。可长期给预测模型扩充或减少其它变量、其表现级、交互效果等。
结构级2是学习统计模型(机器学习),其在应用过程中依据包含个人偏好的汇集数据来改善或针对乘员来定制。通过训练数据组并基于正负反馈模型,进行相应的调整。
该算法的输入参数形成在驾驶操作期间作用于乘员2的刺激,其中,乘员2个人易感性以及在驾驶操作中由乘员2所做活动的类型、即所谓的附加活动形成其它的输入参数。
作为在驾驶操作尤其是自主驾驶操作期间作用于乘员2的刺激,测知或确定行驶动态特征参数,例如像座椅设定、行车特点和/或行车时间。
乘员2个体易感性例如借助至少一个调查表来确定,其例如可借助与中央计算机单元相连的移动式数据处理单元、尤其是智能手机和/或借助车辆1的信息娱乐系统来作答。
乘员2所做活动的类型(例如阅读、玩耍、望向窗外和/或乘员2是否在聊天)可以依据车辆摄像头4所采集的图像数据来确定。
依据输入参数来确定说明或预测乘员2按照多大概率出现由晕车引起的干扰的特征数。
为了抑制、尤其甚至完全杜绝由晕车引起的干扰,在车辆1内存储有用于基于特征数的预防措施目录,其具有多个单独措施、即应对措施。
单独措施的例子是行驶动态措施、路线规划的改变、座椅设定的改变和舒适性调整,其中,规定了关于算法的应对措施的反馈。
关于作用于乘员2的一个或多个刺激,确定一个或多个刺激的类型和持续时间。
为此规定,乘员2的行动、即所做活动及其持续时间依据车辆摄像头4所采集的图像数据来确定。活动类型(如有)例如根据分心度和专注度/关注需求、视觉动态的存在、就长久关注的必要性而言、例如依据略微视线转移和/或依据在车辆1内的乘员2坐姿来归总、即聚类。尤其是,将所述活动归入活动类型,其中,活动类型的活动关于上述性能的数量而言是彼此相同的。
关于平衡器官和本体感受,车辆侧照明传感器系统所测得的信号例如关于车身运动、横向动态、摇摆运动和/或俯仰运动来评估。
尤其是关于乘员2所经受的物理运动刺激来分析照明传感器系统所测得的信号。除了时域分析、例如均值计算和/或通过将滑动标准差与评估函数结合使用,可以实现附加的频域分析。在此,例如可以分析能量密度和/或功率密度。尤其是,就出现由晕车引起的干扰而言,在0.2赫兹范围内的频率被证明是临界的。
此外,车辆摄像头4的图像数据可以关于乘员2的头部运动被评估。如果确定乘员2头部与头枕5之间的相对运动比较大,则存在晕车危险。
为了获得关于例如在车辆1前方的行程上的行车特点的信息、所安排的行车时间和拥堵,采用地图数据和车辆1导航系统数据。借助所述信息,可以不仅考虑过去情况,也可以兼顾前瞻性,以便能预测将来车辆运动。
如上所述,乘员2就出现由晕车引起的干扰而言的个体内及个体间易感性通过回复至少一个标准化调查表来确定。因为所述易感性而确定的值仅在车辆1内被输入一次并且被存在乘员2档案中。如果通过使用学习系统识别到学习效果或识别到乘员2适应于某些情况,则调整乘员2在个体内及个体间易感性的所述值。由此作为反复过程来确定乘员2的个体内易感性值。
此外,为了预测出现由晕车引起的干扰而考虑当前健康状况和舒适标准。目前的健康状况或典型的晕车指标(如乘员2打哈欠、出汗和/或比较烦躁的来回滑动)借助由车辆摄像头4所采集的图像数据和/或依据由车辆1内的其它合适传感器所测得的信号被确定。除了检测头部姿势、头部加速度和/或头部运动外,也通过借助摄像头测量头部血液循环(温度测量)来检测紧张程度。
尤其是,脑电流的测量、所谓的EEG用于预测由晕车引起的干扰,因为借助脑电流可以识别用于刺激前庭核和网状结构的触发信号。这种数据被辅以基于行为的测量和典型的生理数据。
这些数据与如车辆1空调的舒适标准一起被集成到该算法中。
所述算法的单独输入参数的表现形式和表现强度是依据学习系统被转入到随机预测模型中的。
随着所述方法的使用者数量增加,将可以形成群类,其允许比较精确地预测潜在的晕车表现。
除了学习系统方面外,统计学预测模型还包括例如先前对与晕车表现相关的幅值和频率的关联性的认识。
如上所述,措施目录中的单独措施将用于预防出现由晕车引起的干扰。在此,该措施目录包含多种不同的调整参数和影响参数以及相应强度特征。
可人工进行或自动进行的单独措施激活在此通过如下提议进行,该提议基于在预测模型中被超过的特征数阈值。
车辆1的行驶动态影响到纵向、横向和竖向动力学,其中,为了减轻行驶动态,尤其可以进行俯仰和摇摆稳定,可以调整后轴转向和/或可调整底盘特性曲线以影响竖向动力学。
关于路线规划,可以让乘员2驶上替代路线,其不引起或引起至少轻微的由晕车引起的干扰。为了避免出现由晕车引起的干扰,驾驶模式具有少量弯道,避免城市交通和盘道。此外,所选行车路线或替代路线应该具有小交通量,因此不具有拥堵,从而能缩短驾驶持续时间。
还规定,调整路线规划系统,其中,对于纵向动态规定了用于制动和加速过程的极限值和阈值,并且进行提前干预,从而可以实现车辆1的均匀加速和减速。关于横向动态,调整在弯道行驶时的转向角度。
作为用于减少出现由晕车引起的干扰的其它应对措施,可以改变在车辆座椅3处的座椅设定。例如,乘员2的头部可以通过头枕5前移而被支承。
车座3和/或其座椅靠背6可以移动到对乘员2有利的位置例如平躺位置,其中,附加地或替代地,可以启动行驶动态座椅气囊和/或安全带张紧器以将乘员2稳固在其位姿。
可以调整在车辆1内的灯光以预防出现由晕车引起的干扰,在这里,在车辆1内借助动感灯、尤其是动感光带来促成动态。如果乘员2如该实施例所示地读书,则保证书本7所在区域被适当照亮。
此外,可以在车辆1内的显示单元、尤其是信息娱乐系统的显示单元上显示人造水平线和/或十字线,其中,所示内容的动态根据行驶动态被调整。
关于舒适性设定,基于地图数据和导航系统数据来确定哪个行驶路段最适合相应的活动。如果知道乘员2想要在车辆1的自主驾驶操作期间进行哪个活动,则根据驾驶持续时间和行程向乘员2推荐适于进行活动的行车路段。
车辆1的空调设定可以被如下改变,即,气流被冷却并且吹向乘员2的头部,在这里,香氛可被激活并且使例如胡椒薄荷或姜的气味在车辆1内流动,以避免由晕车引起的干扰。
还可以规定,乘员2的呼吸通过声音设定和/或车辆座椅3的按摩系统的按摩节奏被影响,以至少减轻出现由晕车引起的干扰的危险。
单独措施的加权是借助学习系统进行的,并且也被辅以个体偏好。在此,措施目录包括原先已知的单独措施、即已知的应对措施,其借助所执行的先证研究已被调查并验证。
此外,针对乘员2依据特征数所选并执行的单独措施作为其它输入参数又被纳入算法。通过这种方式能评估特殊措施以何种程度实现了改善。所述认识被改善以用于进一步改善算法。因此,措施在预测模型中被同样一并考虑,从而特征值被长期计算并更新。
通过所存在的乘员2个人档案和/或借助学习系统(它为了反馈而识别相应乘员2和/或类群之中及之外的其它人已选择了哪些应对措施),所述方法被反复改善。为此,该学习系统与中央计算机单元相连,其中,该学习系统可以是中央计算机单元的组成部分。
相应的应对措施被推荐给乘员2或被自动激活。当存在关于应对措施的详细信息、少量信息或不存在相关信息时进行自动激活,而当存在关于应对措施的详细信息或需要激活程序时,建议可供人工激活的应对措施。
Claims (8)
1.一种用于预测并减轻在车辆(1)驾驶操作中乘员(2)受到的由晕车引起的干扰的方法,其中,该乘员(2)至少借助车辆摄像头(4)被检测,
其特征是,
-依据作用于该乘员(2)的刺激、该乘员(2)对晕车的个人易感性、在所述驾驶操作期间所做活动的类型来确定表明由晕车引起的干扰的出现概率的特征数,并且
-依据所确定的特征数向该乘员(2)建议或自动启动用于预防由晕车引起的干扰的措施目录中的至少一项单独措施。
2.根据权利要求1所述的方法,其特征是,所述活动类型按照如下项被归入活动分类:
-该乘员(2)的分心度和关注度和/或关注需求,
-该乘员(2)的所需视觉动态,
-该乘员(2)需要长期关注和/或略微移开视线,和/或
-该车辆(1)内的乘员(2)的坐姿。
3.根据权利要求1或2所述的方法,其特征是,为了确定所述作用于该乘员(2)的刺激,借助车载加速传感器系统来采集信号以确定该车辆(1)的车身运动、横向运动、摇摆运动和/或俯仰运动。
4.根据前述权利要求之一所述的方法,其特征是,为了确定所述作用于该乘员(2)的刺激,依据数字地图的信息和/或依据该车辆(1)导航系统数据针对在该车辆(1)前方的行程来确定车辆运动。
5.根据前述权利要求之一所述的方法,其特征是,依据填写的调查表来确定该乘员(2)的个人易感性。
6.根据前述权利要求之一所述的方法,其特征是,为了减轻所述作用于该乘员(2)的刺激,依据针对在该车辆(1)前方的行程所预测的车辆运动向该乘员(2)推荐驾驶替代路线。
7.根据前述权利要求之一所述的方法,其特征是,为了减轻所述作用于该乘员(2)的刺激,
-调整该车辆(1)的驾驶动态,
-路线规划按照驾驶模式和/或交通流量来执行,
-该乘员(2)依据座椅设定被稳定,
-该车辆(1)内的照明被控制,
-显示单元的设定被调整,和/或
-完成舒适性调整。
8.根据前述权利要求之一所述的方法,其特征是,为了验证所启动的单独措施的作用,将所启动的单独措施的调整参数反馈到与该车辆(1)相连的中央计算机单元。
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US20220219705A1 (en) | 2022-07-14 |
WO2020229389A1 (de) | 2020-11-19 |
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