Example-Based Clear Path Detection for Autonomous Vehicles

Solution Overview

Problem

Current autonomous driving systems face challenges in efficiently processing complex road conditions and navigating around objects, requiring significant computational power and often bulky equipment to identify a clear path for vehicle operation.

Innovation Solution

A method that uses camera images to define a clear path by analyzing features, matching current images with sample images, and determining a clear path based on likelihood analysis, reducing the need for individual object classification and utilizing radar imaging systems to enhance navigation confidence.

Engineering Contradictions & Design Principles

VSEngineering Contradiction Analysis

1Measurement precision

If traditional methods catalog and provisionally identify all perceived navigational concerns and classify objects in visual images, then accurate clear path identification is achieved, but processing time increases and computational power requirements increase

Engineering Contradiction:

Improveclear path identification accuracyVSAvoidprocessing time

Core Design Contradiction:

Measurement precisionVSLoss of time

Solution Approach 1:

The patent extracts only the critical features necessary for clear path identification rather than processing all visual data. By focusing on specific road surface characteristics, lane markings, and relevant environmental features, the system achieves accurate clear path detection without the computational burden of complete object classification, thereby reducing processing time while maintaining identification accuracy.

Inventive Principle:

Principle #2Taking out (Extraction)

Solution Approach 2:

The patent segments the visual scene into distinct regions of interest (road surface, sky, obstacles, lane markings) and processes each segment with appropriate algorithms. This segmentation allows the system to apply simplified processing to the road surface area while using more sophisticated object recognition only where necessary, balancing accuracy with processing efficiency.

Inventive Principle:

Principle #1Segmentation

2Measurement precision

If traditional methods catalog and provisionally identify all perceived navigational concerns and classify objects in visual images, then accurate clear path identification is achieved, but computational power requirements increase

Engineering Contradiction:

Improveclear path identification accuracyVSAvoidcomputational power

Core Design Contradiction:

Measurement precisionVSPower

Solution Approach 1:

The patent extracts only the critical features necessary for clear path identification rather than processing all visual data. By focusing on specific road surface characteristics, lane markings, and relevant environmental features, the system achieves accurate clear path detection without the computational burden of complete object classification, thereby reducing processing time while maintaining identification accuracy.

Inventive Principle:

Principle #2Taking out (Extraction)

Solution Approach 2:

The patent applies partial processing by focusing computational resources only on the road surface and immediately relevant features rather than performing exhaustive analysis of all objects in the scene. This partial action approach maintains sufficient accuracy for clear path identification while significantly reducing the computational power required compared to complete scene understanding.

Inventive Principle:

Principle #16Partial or excessive action

3Reliability

If comprehensive object classification is performed to distinguish between different objects such as trees and pedestrians, then navigation safety is improved, but device complexity and cost increase

Engineering Contradiction:

Improvenavigation safetyVSAvoidequipment complexity

Core Design Contradiction:

ReliabilityVSDevice complexity

Solution Approach 1:

The patent applies different processing qualities to different regions of the scene. The road surface area receives detailed analysis for clear path identification, while peripheral objects receive less intensive processing. This local quality approach ensures navigation safety by thoroughly analyzing the drivable area while using simpler processing for surrounding objects, thereby reducing overall device complexity.

Inventive Principle:

Principle #3Local quality

Solution Approach 2:

Instead of classifying all objects and then determining the clear path, the patent inverts the approach by directly identifying the clear path through road surface analysis and then determining what objects lie within or outside that path. This inversion reduces the need for comprehensive object classification while maintaining navigation safety.

Inventive Principle:

Principle #13The other way round (Inversion)

Data Source

PatentUS8803966B2Clear path detection using an example-based approach

Publication Date: 2014.08.12 GM GLOBAL TECHNOLOGY OPERATIONS LLC

AI summary

A method for detecting a clear path of travel for a vehicle using a current image generated by a camera includes defining an exemplary clear path for each of a plurality of sample images, identifying features within each of the plurality of sample images, monitoring the current image generated by the camera, identifying features within the current image, matching the current image to at least one of the sample images based upon the identified features within the current image and the identified features within the plurality of sample images, determining a clear path of travel based upon the matching and the exemplary clear path for each of the matched sample images, and utilizing the clear path of travel to navigate the vehicle.