Ensemble Gaussian Road Modeling for Real-Time Geometry Estimation
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Solution Overview
Problem
Existing road modeling techniques in advanced driver-assistance systems fail to provide flexible and efficient representation of road geometries, leading to delayed or inaccurate outputs that can result in unsafe vehicle behaviors.
Innovation Solution
Utilizing ensemble Gaussian processes to model roads by partitioning the road modeling problem into smaller sub-problems, aggregating data from various sensors, and determining uncertainty values, allowing for robust and computationally efficient road modeling.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If grid-based road models or parameterized road models are used, then road geometry representation is provided, but flexibility for robustly representing road geometries is insufficient
Solution Approach 1:
The patent segments the road modeling problem by dividing the road into multiple longitudinal positions and modeling each position independently using Gaussian process regression. This segmentation allows the system to handle complex road geometries flexibly while maintaining computational efficiency through parallel processing of individual segments.
Solution Approach 2:
The patent transforms the road modeling approach by changing from fixed grid-based or parameterized models to a probabilistic model using Gaussian processes with learned kernel functions. This parameter change enables the model to adapt to different road geometry types dynamically, providing both flexibility and robustness through uncertainty quantification.
2Productivity
If traditional road modeling methods are used, then road models are generated, but processing speed is insufficient to support real-time driving requirements
Solution Approach 1:
By segmenting the road into discrete longitudinal positions and modeling each position separately with Gaussian processes, the system enables parallel computation across multiple positions. This segmentation dramatically improves processing speed while maintaining accurate representation of the entire road geometry.
Solution Approach 2:
The patent replaces traditional mechanical or deterministic road modeling approaches with a probabilistic computational system based on Gaussian process regression. This substitution enables faster processing through efficient kernel-based computations and provides real-time road models suitable for autonomous driving applications.
3Productivity
If computational efficiency is improved through simplified models, then processing speed increases, but measurement precision of road geometry decreases
Solution Approach 1:
The patent introduces uncertainty parameters through Gaussian process regression, transforming deterministic road models into probabilistic ones. This parameter change allows the system to maintain computational efficiency while improving measurement precision by quantifying and accounting for uncertainties in road geometry estimates.
Solution Approach 2:
The system incorporates uncertainty information as feedback into the road modeling process. By using the predicted mean and variance from Gaussian process regression, the model continuously refines its estimates of road geometry, improving precision while maintaining computational efficiency through the probabilistic framework.
Data Source
AI summary
This document describes road modeling with ensemble Gaussian processes. A road is modeled at a first time using at least one Gaussian process regression (GPR). A kernel function is determined based on a sample set of detections received from one or more vehicle systems. Based on the kernel function, a respective mean lateral position associated with a particular longitudinal position is determined for each GPR of the at least one GPR. The respective mean lateral position for each of the at least one GPR is aggregated to determine a combined lateral position associated with the particular longitudinal position. A road model is then output including the combined lateral position associated with the particular longitudinal position. In this way, a robust and computationally efficient road model may be determined to aid in vehicle safety and performance.