Roadside Assistance Vehicle Prepositioning for Autonomous Fleets

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Solution Overview

Problem

Autonomous vehicles often require roadside assistance but lack a driver to intervene or address issues, leading to inefficiencies and high costs in existing dispatching methods.

Innovation Solution

A method and system using a clustering approach to pre-position roadside assistance vehicles within a service area for a fleet of autonomous vehicles, optimizing the assignment of vehicles to cluster locations based on distances and availability.

Engineering Contradictions & Design Principles

VSEngineering Contradiction Analysis

1Loss of time

If roadside assistance vehicles are dispatched using traditional methods, then assistance can be provided to autonomous vehicles, but response times are unpredictable and operational efficiency is reduced

Engineering Contradiction:

Improveresponse timeVSAvoidoperational efficiency

Core Design Contradiction:

Loss of timeVSProductivity

Solution Approach 1:

The system pre-positions roadside assistance vehicles at strategically determined cluster locations before autonomous vehicles actually need assistance. The clustering algorithm continuously determines optimal locations based on current autonomous vehicle distributions, and assistance vehicles are dispatched to these pre-calculated positions in advance, ready to provide immediate help when needed.

Inventive Principle:

Principle #10Preliminary action

Solution Approach 2:

The cluster locations are not fixed but dynamically adjusted based on real-time data about autonomous vehicle positions and distributions. The system periodically recalculates cluster locations using clustering algorithms that consider current spatial patterns, ensuring assistance vehicles are always positioned optimally relative to where autonomous vehicles are currently concentrated.

Inventive Principle:

Principle #15Dynamics

2Area of stationary object

If more roadside assistance vehicles are deployed to cover larger service areas, then coverage is improved, but costs increase

Engineering Contradiction:

Improveservice area coverageVSAvoidnumber of roadside assistance vehicles

Core Design Contradiction:

Area of stationary objectVSQuantity of substance

Solution Approach 1:

Instead of uniformly distributing assistance vehicles across the entire service area, the system concentrates them at specific cluster locations where autonomous vehicles are currently concentrated. The clustering algorithm identifies high-density areas and positions assistance vehicles there, providing intensive coverage where needed while reducing coverage in low-density areas, thereby optimizing the ratio of vehicles to coverage area.

Inventive Principle:

Principle #3Local quality

Solution Approach 2:

Each roadside assistance vehicle is assigned to a specific cluster location and serves multiple autonomous vehicles within that cluster's service radius. One assistance vehicle can handle multiple breakdown events sequentially or simultaneously within its cluster area, making the fleet more versatile and reducing the total number of vehicles needed compared to a one-to-one assignment model.

Inventive Principle:

Principle #6Universality (Multi-functionality)

3Ease of operation

If roadside assistance vehicles are randomly distributed, then deployment is simple, but response predictability and resource utilization are poor

Engineering Contradiction:

Improvedeployment simplicityVSAvoidresponse predictability

Core Design Contradiction:

Ease of operationVSReliability

Solution Approach 1:

The system automatically performs the complex task of determining optimal cluster locations and assigning assistance vehicles without requiring manual intervention. The clustering algorithm self-adjusts based on input data about autonomous vehicle positions, continuously optimizing the distribution. This maintains operational simplicity from the user perspective while achieving high reliability through automated, data-driven decision-making.

Inventive Principle:

Principle #25Self-service

Solution Approach 2:

The system continuously monitors the positions of autonomous vehicles and uses this feedback to periodically recalculate and adjust cluster locations. This closed-loop approach ensures that assistance vehicle distributions remain optimized as conditions change, improving response predictability while the automated nature of the algorithm maintains deployment simplicity.

Inventive Principle:

Principle #23Feedback

Data Source

PatentUS20250076881A1Prepositioning and dynamic distribution of roadside assistance vehicles in an autonomous vehicle service

Publication Date: 2025.03.06 WAYMO LLC

AI summary

Aspects of the disclosure provide for enabling distribution or prepositioning of roadside assistance vehicles within a service area for a fleet of autonomous vehicles. For instance, a clustering approach may be used to determine an assignment identifying a plurality of cluster locations and respective assigned ones of the roadside assistance vehicles. The clustering approach uses a number of clusters corresponding to the number of the roadside assistance vehicles. Distribution information may be sent to computing devices associated with technicians of the respective assigned ones of the roadside assistance vehicles. The distribution information may enable the technicians of the respective assigned ones of the roadside assistance vehicles to proceed to the cluster locations of the determined assignment.