Logistics Vehicle 3D Mapping for Autonomous Object Detection

Resolve Bottlenecks,
Find Innovative SolutionsGenerate Solutions

Solution Overview

Problem

Autonomous vehicles (AVs) require significant resource consumption and time for deployment, with limited mapping functionality, leading to potential catastrophic consequences when sensors fail to detect objects in unmapped environments, especially in adverse conditions.

Innovation Solution

Equipping logistics vehicles with sensors like radar and lidar to generate 3D maps of geographical environments during normal operations, which are then stored for use by AVs, enhancing object detection capabilities and reducing wear and tear on sensors and vehicles.

Engineering Contradictions & Design Principles

VSEngineering Contradiction Analysis

1Reliability

If autonomous vehicles perform mapping operations themselves, then mapping functionality is achieved, but resource consumption and deployment time increase significantly

Engineering Contradiction:

Improveobject detection reliabilityVSAvoidresource consumption

Core Design Contradiction:

ReliabilityVSUse of energy by moving object

Solution Approach 1:

Logistics vehicles equipped with sensors perform mapping operations during their normal shipping operations, serving their own mapping needs while performing logistics tasks. This eliminates the need for dedicated mapping vehicles and reduces overall resource consumption.

Inventive Principle:

Principle #25Self-service

Solution Approach 2:

Logistics vehicles are equipped with sensors that serve dual purposes: performing their primary logistics function and simultaneously collecting mapping data. This multi-functionality reduces the need for separate mapping operations and decreases overall resource consumption.

Inventive Principle:

Principle #6Universality (Multi-functionality)

2Reliability

If autonomous vehicles perform mapping operations themselves, then mapping functionality is achieved, but deployment time increases significantly

Engineering Contradiction:

Improveobject detection reliabilityVSAvoiddeployment time

Core Design Contradiction:

ReliabilityVSLoss of time

Solution Approach 1:

Mapping operations are performed continuously during normal logistics operations rather than as separate dedicated missions. The sensors on logistics vehicles continuously collect mapping data as they traverse environments during routine shipping operations, eliminating idle mapping time.

Inventive Principle:

Principle #20Continuity of useful action

Solution Approach 2:

Mapping data is collected in advance during normal logistics operations before autonomous vehicles need to deploy. This preliminary action ensures that mapping functionality is already in place when AVs are ready for deployment, reducing overall deployment time.

Inventive Principle:

Principle #10Preliminary action

3Reliability

If traditional mapping approaches are used, then mapping is achieved, but mapping functionality remains limited and coverage is insufficient

Engineering Contradiction:

Improveobject detection reliabilityVSAvoidmapping coverage

Core Design Contradiction:

ReliabilityVSAdaptability or versatility

Solution Approach 1:

A fleet of logistics vehicles equipped with sensors provides widespread geographic coverage as they perform diverse shipping operations across different environments. This universal approach ensures comprehensive mapping coverage that adapts to various geographical areas and conditions.

Inventive Principle:

Principle #6Universality (Multi-functionality)

Solution Approach 2:

The system transitions from single-vehicle or dedicated mapping approaches to a multi-vehicle fleet approach, adding the dimension of spatial distribution and temporal continuity. This enables comprehensive coverage of three-dimensional environments through multiple perspectives and extended time periods.

Inventive Principle:

Principle #17Another dimension (Dimensionality change)

Applied Scientific Principles

This section explains which scientific principles are used to turn an abstract innovation direction into a practical engineering solution.

Function Achieved in This Case

This approach allows AVs to detect objects more effectively in various environments, reducing the likelihood of catastrophic events and minimizing unnecessary resource consumption and wear, while providing fuller and more data-rich maps for safer navigation.

Implementation Method 1

Equipping logistics vehicles with sensors like radar and lidar to generate 3D maps of geographical environments

Methodology Applied

Scientific EffectRadar: Radar

Implementation Method 2

Equipping logistics vehicles with sensors like radar and lidar to generate 3D maps of geographical environments

Methodology Applied

Scientific EffectLidar: LIDAR

Data Source

PatentUS20250019188A1Logistics operation environment mapping for autonomous vehicles

Publication Date: 2025.01.16 UNITED PARCEL SERVICE OF AMERICAN INC

AI summary

An indication that one or more physical objects have been detected in a first geographical environment is received via one or more sensors. The one or more sensors are coupled to a logistics vehicle as the logistics vehicle performs one or more shipping operations. Based at least in part on the receiving of the indication that one or more physical objects have been detected, a mapping of the first geographical environment is caused to be generated. The mapping includes at least an image representation of the first geographical environment associated with the first geographical environment. The mapping is stored. The stored mapping is for use by an autonomous vehicle or partially autonomous vehicle for detecting objects in the first geographical environment.