CN114987507A - 用于求取挂车的空间定向的方法、装置和存储介质 - Google Patents
用于求取挂车的空间定向的方法、装置和存储介质 Download PDFInfo
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Abstract
本发明涉及一种用于求取自主行驶的牵引车‑挂车组合(10)中的挂车(22)的空间定向的方法,在所述方法中,对多个不同类型的传感器(26、30、34)的传感器数据进行处理,使得在所述传感器数据中辨识出所述挂车(22)和周围环境对象,其中,将由所述不同类型的传感器(26、30、34)传送且经这样处理的传感器数据相互组合,并且根据经组合的这些传感器数据确定所述挂车(22)相较于所述周围环境对象的运动路线。本发明还涉及一种用于执行该方法的装置。本发明进一步涉及一种存储介质。
Description
技术领域
本发明涉及一种用于求取自主行驶的牵引车-挂车组合中的挂车的空间定向的方法。此外,本发明还涉及一种用于执行这种方法的装置。本发明进一步涉及一种存储介质。
背景技术
在自主行驶车辆,尤其是带有挂车的牵引车中重要的是:感测挂车状态,以便识别该挂车关于牵引车的折角、该挂车的位置、速度、尺寸等。对挂车状态的感测被用于多种目的。一方面,了解挂车状态对于规划载重车-挂车组合的无碰撞、安全的轨迹来说至关重要。另一方面,改进一般的对象识别和跟踪能力也是重要的。
WO 2019/084398 A1公开了一种用于求取自主行驶的铰接式车辆的牵引车和挂车之间的角度和间距的系统和方法。铰接式车辆包括多个传感器,除了间距和角度以外,利用这些传感器还可以识别周围环境和存在于该周围环境中的对象。铰接式车辆根据由此求取的数据被自主地控制。
由现有技术中同样已知,使用受电位器辅助的铰接角测量传感器,以便测量挂车的铰接角。在此,主要目标是识别和避免载重车-挂车组合的潜在折弯情况。一般情况下,这种传感器与高成本相关联并且仅适用于特定挂车类型。
发明内容
本发明的任务在于,提供一种用于求取自主行驶的牵引车-挂车组合中的挂车的空间定向的方法,利用该方法可以可靠且经济地确定不同类型挂车的定向。
为了解决该任务,说明了一种本发明的用于求取挂车的空间定向的方法。由各个优选实施方式能得出本发明的有利扩展方案。
提出一种用于求取自主行驶的牵引车-挂车组合中的挂车的空间定向的方法,在所述方法中,对多个不同类型的传感器的传感器数据进行处理,使得在这些传感器数据中辨识出挂车和周围环境对象,其中,将由不同类型传感器传送的且经这样处理的传感器数据相互组合,并且根据经组合的这些传感器数据来确定挂车相较于周围环境对象的运动路径。
在此,“空间定向”在本发明的意义下应理解为挂车相对于牵引车和相对于其余周围环境对象的定向。在此,不同类型的传感器是基于不同测量原理的传感器。优选,使用像例如摄像机传感器、激光雷达传感器、雷达传感器或超声波传感器这样的传感器。从通过这些传感器得到的传感器数据中辨识出挂车以及周围环境对象,例如人或其他交通参与者。
“传感器数据的组合”应理解为,使用来自于由所有类型传感器辨识出的挂车和周围环境对象,以便求取挂车的运动路径。在此,“运动路径”应理解为车道、行驶方向或运动方向。传感器的组合具有以下优点,一个传感器类型的弱点可以通过其他传感器类型来补偿。因此,由此能够实现:即使在有雾的情况下、在黑暗或下雨的情况下也可以辨识挂车和周围环境对象,使得仍能求取运动路径。由此,实现了尤其是对于自动行驶来说所需要的可靠性。
附加地,不需要设置冗余的传感器,因为通过不同类型的传感器已经提供了冗余。除了挂车的运动路径以外,优选附加地求取交通参与者的运动路径。这优选在与挂车运动路径的求取相同的步骤中求取。由此,可以确定挂车的运动路径,使得该挂车利用该运动路径能安全地驶过交通。
为了自动行驶,一般需要大量传感器。在本发明的一个优选实施方式中,利用为自主行驶所使用的传感器来求取传感器数据。这具有以下优点,为了求取挂车的空间定向不需要附加的传感器。由此,该方法可以经济地执行。优选,这些传感器全部都布置在牵引车上,使得不必存在牵引车与挂车之间的通信连接。这附加地简化了本方法。
在本发明的另一优选实施方式中,使用估计滤波器来确定挂车的运动路径。优选,使用卡尔曼滤波器作为估计滤波器。利用这样的估计滤波器来估计无法直接测量的系统参量,同时优化地降低测量误差。由此,通过这样的估计滤波器来提高确定运动路径的精确度。
优选,根据挂车尺寸、支撑点的位置和牵引车转向角,在传感器数据中辨识出挂车。根据这些数据可以改进在本方法开始时对挂车的初始辨识。轮速也可以被用作另外的值。
在一个有利扩展方案中,利用维度估计算法从各个传感器数据中确定挂车的维度。一般情况下,挂车的维度或者说延伸尺度是已知的。尽管如此,仍能够通过维度估计算法实现,从传感器数据中估计挂车的维度。由此,也可以使用不同大小的挂车。优选,在行驶开始时探测挂车,使得维度估计算法仅必须执行一次。
有利地,在传感器数据中对挂车的辨识通过深度学习算法来执行。深度学习算法具有以下优点:可以在训练之后实现高辨识率。由此,改进了该方法的可靠性。
在另一有利实施方式中,计算出系数,该系数表明:多么可靠地在各个传感器数据中辨识出挂车。在此,根据例如传感器数据的质量、已知的挂车形状、天气条件和当前的传感器功能状态,该系数确定:多么可靠地识别出挂车。由此,可以计算一个值,利用该值可以作出关于识别可靠性的结论,使得在组合这些传感器类型时在带有提高的系数的传感器类型下执行的这种挂车辨识比在带有较低系数的传感器类型下执行的这种挂车辨识被更高地加权。相应地,可以更精确且可靠地辨识挂车。
根据一个符合目的的实施方式,如果系数低于预先确定的值,则摒弃在对应的传感器数据中辨识挂车。在此,该值表明挂车辨识所需要的最小可靠性。相应地,通过选择特定的值可以设定所需要的最小可靠性,使得不可靠的挂车识别不会导致结果变差。与此相应地,由此可以改进挂车辨识的可靠性和精确度。
根据另一符合目的的实施方式,在传感器数据中对挂车的辨识基于先前求取的挂车运动路径。在此,从之前的测量中得出运动路径。根据挂车的运动路径可以预测:挂车在这些测量的时刻应大约在哪里。由此,可以排除由于与预测挂车位置有很大偏差而导致的错误测量。尤其,即使例如在视线较差的情况下仍然可以辨识出挂车。总之,由此简化了求取并且提高了可靠性。
优选,挂车辨识在牵引车的被探测到的状态期间执行。在此,经探测的牵引车状态是这样的状态:在该状态中可以基于牵引车的测量值,例如转向角、速度等,推断出挂车的位置。例如,可以认为:在转向角在一段确定的时间为0°并且速度确定的情况下,挂车与牵引车布置在一条线上。在这种状况下,同样简化了该辨识。
本发明所基于的任务附加地通过一种用于执行本发明方法的装置来解决,其中,该装置包括至少两个不同类型的传感器和处理单元,所述处理单元用以在传感器数据中辨识挂车并且用以根据传感器数据求取挂车的运动路径。这样的装置具有针对该方法所提及的优点。
附图说明
在附图中示出并且在下面的说明中更详细地阐述本发明的实施例。附图示出:
图1示出具有用于执行本发明方法的本发明装置的一个实施例的牵引车-挂车组合,以及
图2示出本发明的用以求取挂车的空间定向的方法的一个实施例。
具体实施方式
在图1中示出牵引车-挂车组合10,该牵引车-挂车组合具有用于执行本发明方法的装置的一个实施例。在此,牵引车-挂车组合10由牵引车14和经由支撑点18附接到该牵引车上的挂车22组成。在该实施例中,牵引车14具有至少两个摄像机传感器26,这些摄像机传感器布置在牵引车14的外后视镜上并且朝后方定向。在视线良好的情况下,通过这些摄像机传感器26能探测到挂车22。
在牵引车14上附加地布置至少两个雷达传感器30,这些雷达传感器同样为了探测挂车22而朝后方定向。在该实施例中,朝挂车22的方向还布置有至少一个激光雷达传感器34。全部的传感器26、30、34与设置在牵引车14中的处理单元38连接,使得全部这些传感器26、30、34的传感器数据可以被传送给处理单元38用以被进一步处理。
图2中根据本发明的一个实施例示出用于求取挂车22的空间定向的方法。在此,该方法的各部分可以在图1中所示出的处理单元38中执行。在第一步骤A中,求取三种不同的传感器26、30、34的传感器数据。这些传感器数据尤其是其中感测到牵引车-挂车组合10的后部区域的数据。由此,可以求取能用于求取挂车22的空间定向的数据。除了这些数据以外,还可以求取牵引车-挂车组合10的里程表数据(Odometriedaten)和牵引车转向角。尽管利用这些数据不感测挂车22本身,但这些数据可以有助于对挂车的辨识。
在接下来的步骤B中,传感器数据被处理。在该步骤中,在各个传感器数据中辨识出挂车22和周围环境对象。在此,周围环境对象例如可以是其他交通参与者或道路建设物(Straβenbebauung)。在此,例如使用深度学习方法来辨识挂车22,该深度学习方法已被训练为针对各个传感器类型来识别挂车22。
在进一步的步骤中,根据这样求取到的挂车22来计算系数F,该系数表明:可以多么可靠地在这些传感器数据中辨识出挂车22。在此,根据例如传感器数据的质量、挂车22的已知形状、天气条件和传感器26、30、34的当前功能状态,该因数F确定:多么可靠地识别出了挂车22。如果由此得出的系数F大于或等于预限定的值W(j),则这些传感器数据被转发。而如果系数F小于值W,则相应的传感器数据不会被转发,以防使整体结果变差。
在步骤D中,将被转发的传感器数据相互组合,使得在最后的步骤E中,可以从经这样组合的这些传感器数据中确定挂车22相对于所识别的周围环境对象的运动路径。为了在步骤B中更简单地辨识挂车22,将在步骤E中所识别的运动路径用于步骤B以进行辅助。
Claims (14)
1.一种用于求取自主行驶的牵引车-挂车组合(10)中的挂车(22)的空间定向的方法,在所述方法中,对多个不同类型的传感器(26、30、34)的传感器数据进行处理,使得在所述传感器数据中辨识出所述挂车(22)和周围环境对象,其中,将由所述不同类型的传感器(26、30、34)传送且经这样处理的传感器数据相互组合,并且根据经组合的这些传感器数据确定所述挂车(22)相较于所述周围环境对象的运动路线。
2.根据权利要求1所述的方法,其特征在于,利用为自主行驶所使用的传感器来求取所述传感器数据。
3.根据权利要求1或2所述的方法,其特征在于,使用估计滤波器来确定所述挂车(22)的运动路径。
4.根据前述权利要求中任一项所述的方法,其特征在于,根据挂车尺寸、支撑点(18)的位置和牵引车转向角,在所述传感器数据中辨识所述挂车(22)。
5.根据权利要求4的方法,其特征在于,利用维度估计算法从各个传感器数据中确定所述挂车(22)的维度。
6.根据前述权利要求中任一项所述的方法,其特征在于,在所述传感器数据中对所述挂车(22)的辨识通过深度学习算法来执行。
7.根据前述权利要求中任一项所述的方法,其特征在于,计算出系数(F),该系数表明:多么可靠地在各个传感器数据中辨识出所述挂车(22)。
8.根据权利要求7所述的方法,其特征在于,如果所述系数(F)低于预先确定的值(W),则摒弃在对应的传感器数据中辨识所述挂车(22)。
9.根据前述权利要求中任一项所述的方法,其特征在于,在所述传感器数据中对所述挂车(22)的辨识基于所述挂车(22)的先前求取的运动路径。
10.根据前述权利要求中任一项所述的方法,其特征在于,对所述挂车(22)的辨识在所述牵引车(14)的被探测到的状态期间执行。
11.用于执行根据前述权利要求中任一项所述的方法的装置,所述装置包括至少两个不同类型的传感器(26、30、34)和处理单元(38),所述处理单元用以在传感器数据中辨识挂车(22)并且用以根据所述传感器数据来求取所述挂车(22)的运动路径。
12.一种计算机程序,该计算机程序包含机器可读的指令,当在一个或多个计算机上执行所述指令时,所述指令安排所述一个或多个计算机执行根据权利要求1至10中任一项所述的方法。
13.一种机器可读的数据载体和/或下载产品,所述数据载体和/或下载产品具有根据权利要求12所述的计算机程序。
14.一种计算机,该计算机具有根据权利要求12所述的计算机程序,和/或具有根据权利要求13所述的机器可读的数据载体和/或下载产品。
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