Autonomous Fleet Recovery Risk Profile Prioritization
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Solution Overview
Problem
Autonomous vehicle fleets face challenges in efficiently prioritizing and managing vehicle recovery when multiple vehicles require assistance, especially in scenarios where manual prioritization becomes ineffective as fleet sizes grow.
Innovation Solution
The implementation of a system that assigns a risk profile to stranded vehicles based on factors like location, passenger presence, and environmental conditions, allowing for prioritization and automated dispatch of recovery vehicles.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If manual prioritization is used for vehicle recovery, then simplicity of operation is maintained, but effectiveness deteriorates as fleet sizes grow
Solution Approach 1:
The system enables autonomous vehicles to self-report their stranded status and environmental conditions, automatically generating risk profiles without manual intervention. The vehicles use their own sensors and onboard computers to assess and communicate their situation, allowing the fleet management system to prioritize recoveries based on objective risk data rather than manual assessment
Solution Approach 2:
The patent replaces manual prioritization processes with an automated computational system that uses sensor data, risk factor analysis, and algorithmic decision-making. The mechanical/manual process of assessing and prioritizing vehicle recoveries is substituted with an electronic system that processes environmental data, calculates risk profiles, and generates prioritization rankings automatically
2Reliability
If automated risk profiling is implemented, then recovery prioritization effectiveness is improved, but system complexity increases
Solution Approach 1:
The risk profiling system is segmented into distinct functional modules: sensor data collection, risk factor identification, risk calculation, and prioritization ranking. Each module performs a specific function, making the overall complex system manageable through modular design. The risk factors themselves are segmented into categories such as passenger safety, vehicle safety, and traffic impact
Solution Approach 2:
The system uses existing autonomous vehicle components (sensors, onboard computers, communication systems) for multiple purposes - both for normal autonomous operation and for risk assessment during stranded situations. This multi-functionality reduces the need for dedicated specialized equipment, thereby limiting the increase in system complexity
3Measurement precision
If comprehensive environmental data collection is performed, then risk profile accuracy is improved, but data processing requirements increase
Solution Approach 1:
The system continuously collects and pre-processes environmental data even before vehicles become stranded, so that when a stranded event occurs, the risk assessment can quickly utilize pre-organized data. This preliminary data collection and organization reduces the real-time processing burden while maintaining comprehensive risk profile accuracy
Solution Approach 2:
The system collects more environmental data than strictly necessary for basic risk assessment, including extraneous factors that may not immediately impact risk but provide context for future analysis. This excessive data collection approach ensures comprehensive accuracy while allowing the system to filter and prioritize only the most relevant factors for immediate decision-making
Data Source
AI summary
Systems and methods are provided for autonomous vehicle recovery. In particular, systems and methods are provided for vehicle recovery prioritization in a fleet of vehicles when multiple vehicles request recovery. Stranded vehicles in need of recovery assigned a risk profile and prioritized based on the risk profile. The risk profile can be based on a number of factors, such as a risk level for the vehicle due to the vehicle's situation, the presence of passengers in the vehicle, and congestion caused by the vehicle. Recovery response can be based on the recovery prioritization. Vehicle sensors and computing power can be used to inform onboard processors and/or central computers of a risk profile for the vehicle, and dispatch can trigger a response plan according to a recovery response framework.