公开(公告)号：WO2004008648A2

公开(公告)日：2004-01-22

申请号：PCT/US2003/022182

申请日：2003-07-15

Applicant: AUTOMOTIVE SYSTEMS LABORATORY, INC.

Inventor： CONG, Shan ,  SHEN, Shi ,  HONG, Lang

IPC: H04B

CPC classification number: G01S13/931 ,  B60K31/0008 ,  B60K31/0083 ,  B60T2210/24 ,  B60T2260/08 ,  B60W2520/14 ,  B60W2550/143 ,  B60W2550/302 ,  B60W2550/304 ,  B60W2550/306 ,  G01S13/58 ,  G01S13/723 ,  G01S13/86 ,  G01S2013/9325 ,  G01S2013/9353 ,  G01S2013/9375

Abstract: A first Kalman filter (52) estimates true measures of yaw rate (ω) and vehicle speed (U) from associated noisy measures thereof generated by respective sensors (16, 18) in a host vehicle (12), and a second Kalman filter (54) estimates therefrom parameters (C 0 , C 1 ) of a clothoid model of road curvature. Measures of range ( r ), range rate (S) and azimuth angle (ŋ) from a target state estimation subsystem (44), e.g. a radar system (14), are processed by an extended Kalman filter (56) to provide an unconstrained estimate of the state of a target vehicle (36). Associated road constrained target state estimates are generated for one or more roadway lanes (38, 40), and are compared- either individually or in combination- with the unconstrained estimate. If a constrained target state estimate corresponds to the unconstrained estimate, then the state of the target vehicle is generated by fusing the unconstrained and constrained estimates; and otherwise is given by the unconstrained estimate alone.

Abstract translation: 第一卡尔曼滤波器（52）根据由主车辆（12）中的各个传感器（16,18）产生的相关噪声测量来估计偏航角速度（ω）和车速（U） ），并且第二卡尔曼滤波器（54）从中估计道路曲率的回旋曲线模型的参数（C 0，C 1，C 1）。 来自目标状态估计子系统（44）的距离（r），距离率（S）和方位角（ŋ）的测量，例如， 雷达系统（14）由扩展卡尔曼滤波器（56）处理以提供目标车辆（36）的状态的无约束估计。 为一个或多个道路车道（38,40）产生相关的道路约束目标状态估计值，并且与单独或组合地与非约束估计值进行比较。 如果受约束的目标状态估计对应于无约束估计，则通过融合无约束和约束估计来生成目标车辆的状态; 否则只能由无约束的估计值给出。

公开(公告)号：WO2005062984A2

公开(公告)日：2005-07-14

申请号：PCT/US2004/043695

申请日：2004-12-24

Applicant: AUTOMOTIVE SYSTEMS LABORATORY, INC. ,  CONG, Shan ,  SHEN, Shi ,  HONG, Lang

Inventor： CONG, Shan ,  SHEN, Shi ,  HONG, Lang

IPC: G01S13/58 ,  G01S13/72 ,  G01S13/93

CPC classification number: G01S13/58 ,  G01S13/723 ,  G01S13/931 ,  G01S2013/9353

Abstract: A processor (26) using a first Kalman filter (52, 52.1) estimates a host vehicle state from speed (U) and yaw rate, the latter of which may be from a yaw rate sensor (16) if speed (U) is greater than a threshold, and, if less, from a steer angle sensor and speed (U). Road curvature parameters (C0, C1) are estimated from a curve fit of a host vehicle trajectory or from a second Kalman filter (54, 54.1) for which a state variable may be responsive to a plurality of host state variables (72, 74). Kalman filters (52, 52.1, 54, 54.1) may incorporate adaptive sliding windows. Curvature of a most likely road type is estimated with an interacting multiple model (IMM) algorithm (2400) using models of different road types. A road curvature fusion subsystem (96) provides for fusing road curvature estimates from a plurality of curvature estimators (42.1, 42.2, 42.N) using either host vehicle state, a map database (88) responsive to vehicle location (86), or measurements of a target vehicle (36) with a radar system (14).

Abstract translation: 使用第一卡尔曼滤波器（52,52.1）的处理器（26）从速度（U）和横摆率估计主车辆状态，如果速度（U）更大，后者可以来自偏航率传感器（16） 并且如果较小，则从转向角传感器和速度（U）。 根据主车辆轨迹的曲线拟合或状态变量可响应于多个主机状态变量（72,74）的第二卡尔曼滤波器（54,54.1）来估计道路曲率参数（C0，C1） 。 卡尔曼滤波器（52,52.1,54,54.1）可以包括自适应滑动窗口。 使用不同道路类型的模型，使用交互式多模型（IMM）算法（2400）估计最可能的道路类型的曲率。 道路曲率融合子系统（96）提供使用主车辆状态，响应于车辆位置（86）的地图数据库（88）来​​自多个曲率估计器（42.1,42.2，42.N）来融合路面曲率估计，或 用雷达系统（14）对目标车辆（36）进行测量。