Autonomous Vehicle Queue Management in Transition Regions
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Solution Overview
Problem
Autonomous vehicles face challenges in maintaining precision and confidence in autonomous driving due to environmental conditions, sensor failures, and changes in autonomous levels, which affect navigation and vehicle control.
Innovation Solution
A method and apparatus for managing autonomous vehicles by organizing them in autonomous transition regions, using vehicle context data to assign vehicles to queues based on autonomous levels, location, and communication signal strength, facilitating navigation and communication between vehicles, and recommending media content based on expected queue duration.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If autonomous vehicles operate independently without organization in transition regions, then each vehicle maintains individual navigation freedom, but precision and confidence of autonomous driving capabilities deteriorate due to environmental conditions and sensor failures
Solution Approach 1:
A server acts as an intermediary between autonomous vehicles and the infrastructure, coordinating vehicle queues in transition regions. The server receives vehicle context data, determines appropriate queues based on autonomous levels and location, and provides guidance information back to vehicles, thereby improving reliability without requiring complex direct vehicle-to-vehicle communication systems
Solution Approach 2:
The system implements feedback loops where vehicles report their context data (autonomous level, location, sensor status) to the server, which then provides queue assignments and navigation guidance back to the vehicles. This closed-loop feedback mechanism enhances autonomous driving precision by continuously adjusting vehicle routing based on real-time conditions and vehicle capabilities
2Measurement precision
If vehicles are organized in queues with detailed coordination, then autonomous driving precision and navigation are improved, but communication requirements and system complexity increase
Solution Approach 1:
The system applies different levels of coordination and communication to different vehicles based on their specific context, such as autonomous level capability and location within the transition region. Vehicles with higher autonomous levels may receive more detailed queue positioning information, while others receive simplified guidance, optimizing communication efficiency without sacrificing localization precision
3Productivity
If comprehensive vehicle context data is collected and processed, then queue assignment accuracy and route guidance are improved, but computational resources and processing time increase
Solution Approach 1:
The system extracts and processes only the most critical vehicle context data elements needed for queue assignment, such as autonomous level, location, and basic sensor status, rather than processing all available vehicle data. This selective extraction approach maintains navigation efficiency while reducing computational energy consumption on both vehicle and server sides
Data Source
AI summary
A method, apparatus and computer program product are provided for managing autonomous vehicles. In this regard, vehicle context data associated with an indication of a change in an autonomous level for a vehicle traveling along a road segment is determined. Furthermore, the vehicle is assigned to a queue of vehicles traveling along the road segment in response to a determination that the vehicle context data corresponds to particular vehicle context data associated with the queue of vehicles. An indication of the queue of vehicles may also be provided to the vehicle to facilitate navigation of the vehicle.