Nonlinear Parameter Estimation for Vehicle Sensor Fusion
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Solution Overview
Problem
Existing methods for fusing measurements from different information sources in driver assistance and self-driving vehicle systems face challenges in accurately estimating unknown parameters, leading to low precision and reduced robustness, especially when sensor parameters are nonlinearly included in measurement equations.
Innovation Solution
The method involves estimating unknown parameters directly from measurements by relating them through parameter-dependent measurement equations, independent of the filter state, allowing for more robust and precise object tracking without extending the filter state, using a Kalman filter for temporal filtering and updating parameters independently of the filter vector.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of information
If unknown parameters are included nonlinearly in measurement equations, then measurement information can be fully utilized, but parameter estimation precision deteriorates due to underdetermination of the estimation problem
Solution Approach 1:
The patent separates parameter estimation from state estimation into two independent processes. Parameter estimation is performed using only measurement equations without involving the filter state, while state estimation uses the filtered state. This segmentation prevents the underdetermination problem from affecting state estimation precision while still utilizing nonlinear parameter information.
Solution Approach 2:
The patent introduces an intermediary approach where parameters are estimated independently as preliminary values before being used in the state estimation process. This intermediary parameter estimation step acts as a bridge that allows nonlinear parameter information to be incorporated without directly interfering with the state estimation precision.
2Adaptability or versatility
If filter state is extended with unknown parameters, then parameter estimation can be performed jointly, but filter response time increases and filter becomes more sluggish
Solution Approach 1:
The patent divides the estimation process into two separate segments: parameter estimation and state estimation. By not extending the filter state with unknown parameters, the state estimation maintains its original computational efficiency and fast response time, while parameter estimation is performed separately using measurement equations.
Solution Approach 2:
The patent performs parameter estimation as a preliminary action before state estimation. By estimating parameters independently first and then using them in the state estimation process, the system avoids the computational burden of joint estimation while still achieving adaptive parameter incorporation.
3Reliability
If model assumptions are violated (e.g., constant velocity assumed but actual acceleration occurs), then innovation flows away into additional parameters, but model adaptation slows down and learned parameters become disrupted
Solution Approach 1:
The patent segments the estimation process so that parameter estimation and state estimation are performed independently. When model assumptions are violated, innovation in parameter estimation does not directly affect state estimation, preventing disruption of learned parameters while still allowing model adaptation through separate parameter updates.
Solution Approach 2:
The patent implements separate feedback loops for parameter estimation and state estimation. This allows the system to adapt to model violations through parameter updates without disrupting the state estimation process, maintaining both robustness to model violations and adaptation speed.
Data Source
AI summary
The invention relates to a method and a device for fusion of measurements from various information sources (I 1, I 2, . . . , I m) in conjunction with filtering of a filter vector, wherein the information sources (I 1, I 2, . . . , I m) comprise one or more environment detection sensor(s) of an ego vehicle,wherein in each case at least one measured quantity derived from the measurements is contained in the filter vector,wherein the measurements from at least one individual information source (I 1; I 2; . . . , I m) are mapped nonlinearly to the respective measured quantity, wherein at least one of these mapping operations depends on at least one indeterminate parameter,wherein the value to be determined of the at least one indeterminate parameter is estimated from the measurements of the different information sources (I 1, I 2, . . . , I m) andwherein the filter vector is not needed for estimating the at least one indeterminate parameter.