Semantic Road Mapping Using Weighted Lane Prediction

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Solution Overview

Problem

Current methods for generating semantic road maps are labor-intensive, time-consuming, and resource-intensive, with high inaccuracies, making them unsuitable for efficient autonomous vehicle operation.

Innovation Solution

A system that utilizes sensor data from vehicles to project trace and key points onto an orthogonal axis, applies a range of weighting functions to determine lane configurations, and selects the highest scoring subset to generate accurate lane characteristics, reducing resource intensity and improving accuracy.

Engineering Contradictions & Design Principles

VSEngineering Contradiction Analysis

1Measurement precision

If traditional methods are used to generate semantic road maps, then comprehensive road information can be obtained, but the process becomes labor-intensive and resource-intensive

Engineering Contradiction:

Improveaccuracy of lane characteristicsVSAvoidresource consumption

Core Design Contradiction:

Measurement precisionVSProductivity

Solution Approach 1:

The patent replaces manual mechanical methods of road map generation with an automated computer-based system that uses sensor data processing and algorithmic analysis to extract lane characteristics, eliminating labor-intensive operations while maintaining accuracy

Inventive Principle:

Principle #28Mechanics substitution (Replace mechanical system)

Solution Approach 2:

The system enables autonomous vehicles to self-generate semantic road map information by processing their own sensor data through the weighting function methodology, eliminating the need for external manual mapping operations

Inventive Principle:

Principle #25Self-service

2Measurement precision

If traditional methods are used to generate semantic road maps, then road characteristics can be mapped, but the process is time-consuming

Engineering Contradiction:

Improveaccuracy of lane predictionsVSAvoidtime consumption

Core Design Contradiction:

Measurement precisionVSLoss of time

Solution Approach 1:

The system performs preliminary data collection by continuously gathering sensor data during normal vehicle operation, so that when semantic map generation is needed, the information is already available for immediate processing without time-consuming field operations

Inventive Principle:

Principle #10Preliminary action

Solution Approach 2:

The patent replaces time-consuming manual surveying and mapping operations with automated real-time sensor data processing that continuously builds and updates semantic road map information during vehicle operation

Inventive Principle:

Principle #28Mechanics substitution (Replace mechanical system)

3Productivity

If simple methods are used for road map generation, then resource consumption is reduced, but accuracy of lane characteristics deteriorates

Engineering Contradiction:

Improveresource efficiencyVSAvoidaccuracy of lane characteristics

Core Design Contradiction:

ProductivityVSMeasurement precision

Solution Approach 1:

The system changes the parameters of analysis by applying multiple weighting functions with different characteristics to the sensor data, allowing the same efficient processing framework to achieve high accuracy through parameter optimization rather than through complex manual methods

Inventive Principle:

Principle #35Parameter changes

Solution Approach 2:

The patent applies different weighting functions to different portions of the sensor data based on local characteristics, allowing the system to efficiently process data with varying accuracy requirements in different road segments rather than using a uniform high-resource approach

Inventive Principle:

Principle #3Local quality

4Loss of information

If manual methods are used to create semantic maps, then detailed road information can be obtained, but labor intensity increases

Engineering Contradiction:

Improvecompleteness of road informationVSAvoidoperational complexity

Core Design Contradiction:

Loss of informationVSDevice complexity

Solution Approach 1:

The patent replaces manual information gathering operations with automated sensor systems that continuously collect comprehensive road data, and replaces manual analysis with algorithmic processing that extracts complete lane characteristics without human intervention

Inventive Principle:

Principle #28Mechanics substitution (Replace mechanical system)

Solution Approach 2:

The system uses multi-functional sensor arrays that serve multiple purposes - collecting data for both immediate navigation and long-term semantic map generation - eliminating the need for separate specialized equipment for different data collection tasks

Inventive Principle:

Principle #6Universality (Multi-functionality)

Data Source

PatentUS12516955B2System and method for generating a semantic map for a road

Publication Date: 2026.01.06 WOVEN BY TOYOTA INC

AI summary

Systems, methods, and other embodiments described herein relate to generating a semantic map for a road segment. In one embodiment, a method includes receiving sensor data related to a road segment, generating an orthogonal axis related to the road segment, and projecting the sensor data onto the orthogonal axis. The method includes generating a range of weighting functions based on potential characteristics of the road segment and determining a plurality of scores based on applying the range of weighting functions to the sensor data along the orthogonal axis. The method includes selecting one weighting function from the range of weighting functions based on one score of the plurality of scores, where the one score is a highest score. The method includes determining characteristics of the road segment based on the selected weighting function.