Target Curvature Estimation Using Constrained EKF Bounds
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Solution Overview
Problem
Advanced Driver-Assistance Systems (ADAS) face challenges in accurately estimating target vehicle curvature due to its nonlinearity, leading to large relative errors in centripetal acceleration, which affects path prediction and collision avoidance.
Innovation Solution
The implementation of a constrained Constant Turn and Constant Acceleration (CCTCA) Extended Kalman Filter (EKF) that uses primary and secondary upper bounds derived from vehicle dynamics models, such as the Pacejka Magic Formula and tire friction force ellipse models, to constrain centripetal acceleration estimates, ensuring more accurate curvature estimation and path prediction.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If a standard Extended Kalman Filter (EKF) is used to estimate curvature, then the estimation process is simple and computationally efficient, but the curvature estimation accuracy deteriorates due to nonlinearity causing large relative errors in centripetal acceleration
Solution Approach 1:
The patent applies parameter changes by modifying the EKF to incorporate physical constraints on curvature and centripetal acceleration. Specifically, upper and lower bounds are imposed on these parameters based on vehicle dynamics characteristics, transforming the unconstrained EKF into a constrained version that maintains computational efficiency while improving estimation accuracy through physically realistic parameter ranges
Solution Approach 2:
The patent implements feedback by using the constrained curvature and centripetal acceleration estimates to continuously refine path predictions. The improved curvature estimates feed back into the motion model to enhance target trajectory prediction accuracy, creating a closed-loop system where estimation results are continuously validated and refined against physical constraints
2Reliability
If curvature estimation is improved through constrained EKF, then path prediction accuracy is enhanced, but computational complexity increases due to additional constraints and bounds checking
Solution Approach 1:
The patent applies preliminary action by pre-calculating and storing upper and lower bounds for curvature and centripetal acceleration based on vehicle dynamics models before the tracking process begins. These pre-computed constraints are then directly applied during real-time filtering without requiring complex on-the-fly calculations, thus improving reliability while minimizing additional computational burden during operation
Data Source
AI summary
This document describes target curvature estimation considering vehicle dynamics constraints. Some vehicle dynamics models use several kinematic states to predict the paths of targets identified by an object-tracking system. Rather than rely on predetermined industry measurements as the kinematic inputs to a model, an example system instead estimates some of these states using near real-time data output from a tracking filter. This can include estimating position and velocity states, as well as curvature and centripetal acceleration states. The example system is particularly suited at accurately predicting the curvature and centripetal acceleration states, which greatly improves the accuracy of the object-tracking system, and further improves safety in avoiding a target collision.