Lane-Type Hypothesis Fusion for Uncertainty-Aware Road Modeling
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Solution Overview
Problem
Existing road-perception systems are often inaccurate and fail to reliably model lanes and quantify uncertainty, which is a requirement for safety regulations in autonomous and assisted-driving systems.
Innovation Solution
A road-perception system that uses a belief parameter and plausibility parameter to consolidate lane-type and roadway hypotheses, incorporating Dempster-Shafer Theory to fuse information from vision, trails, and prior knowledge, allowing for robust uncertainty quantification and improved safety standards.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If existing road-perception systems are used to model lanes and identify lane types, then vehicle-based systems can obtain road geometry information, but the systems are inaccurate and cannot quantify uncertainty to satisfy safety regulations
Solution Approach 1:
The system transforms qualitative lane type identification into quantitative hypothesis evaluation by introducing belief parameters and plausibility parameters. Each lane type hypothesis (e.g., through lane, exit lane, turn lane) is assigned numerical confidence measures that can be compared against thresholds, enabling both accurate classification and explicit uncertainty quantification for safety-critical decisions
Solution Approach 2:
The patent introduces an intermediary hypothesis evaluation mechanism that mediates between raw sensor data and final lane type determination. By computing belief and plausibility parameters as intermediate quantities, the system provides a transparent bridge that allows downstream systems to assess the reliability of lane type identifications before making autonomous driving decisions
2Reliability
If multiple lane-type hypotheses are assigned to each lane with belief masses, then uncertainty can be quantified, but the system complexity increases due to hypothesis consolidation requirements
Solution Approach 1:
The system segments the hypothesis evaluation process into distinct computational stages: (1) generating multiple lane-type hypotheses for each lane, (2) computing belief masses for each hypothesis based on sensor evidence, (3) calculating belief and plausibility parameters, and (4) consolidating hypotheses into roadway-level conclusions. This segmentation makes the complex uncertainty quantification process manageable and modular
Solution Approach 2:
The patent adds a new dimension to lane type identification by introducing the belief-plausibility parameter space. Instead of simply classifying lanes into discrete types, the system evaluates each hypothesis in a two-dimensional uncertainty space, allowing for nuanced representation of confidence levels and facilitating systematic consolidation across multiple lanes
3Reliability
If belief parameters and plausibility parameters are determined for each lane-type hypothesis, then regulatory requirements are met, but computational processing time increases
Solution Approach 1:
The system performs preliminary hypothesis generation and belief mass computation based on available sensor data and prior knowledge before final lane type determination is required. By pre-computing belief and plausibility parameters for all plausible lane type hypotheses, the system prepares regulatory-compliant uncertainty measures in advance, reducing real-time processing demands when autonomous driving decisions must be made
Data Source
AI summary
This document describes techniques and systems to make determinations of lane-type and roadway hypotheses in a road model. The road-perception system can fuse various forms of evidence to determine lane-type hypotheses and respective belief masses associated with the lane-type hypotheses. The road-perception system the computes, using the belief masses, a belief parameter and a plausibility parameter associated with the lane-type hypotheses. One or more roadway hypotheses are then determined using the lane-type hypotheses. The road-perception system then uses the respective belief parameter and plausibility parameter associated with the lane-type hypotheses to compute a belief parameter and a plausibility parameter associated with the roadway hypotheses. In this way, the described techniques and systems can provide an accurate and reliable road model with quantified uncertainty.