Autonomous Driving Route QoS Scoring for Reliable Autonomy Levels
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Solution Overview
Problem
Existing autonomous vehicles lack a comprehensive system for determining and communicating the quality of service (QoS) of autonomous driving, which affects user experience and feasibility, especially in varying environmental conditions and vehicle capabilities.
Innovation Solution
A vehicle computing device that determines multiple route segments for potential routes to a destination, calculates an autonomous driving QoS score based on factors like map data, weather, traffic, and sensor health, and updates these scores in real-time to ensure a consistent and user-preferred driving experience.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If autonomous vehicles operate without a comprehensive QoS determination system, then the system complexity is reduced, but the user experience and feasibility of autonomous driving deteriorate due to lack of reliable quality assurance in varying environmental conditions
Solution Approach 1:
The patent segments the QoS determination into multiple independent components: route segment identification, autonomous driving factor determination (map data, weather, traffic, sensor health), and scoring mechanisms. Each component operates independently and contributes to the overall QoS score, allowing the system to manage complexity through modular architecture while maintaining comprehensive reliability assessment
Solution Approach 2:
The system performs preliminary QoS determination and route selection before autonomous driving begins. By pre-calculating QoS scores for multiple potential routes and selecting the optimal route in advance, the system ensures reliable quality assurance without adding operational complexity during actual driving execution
2Adaptability or versatility
If the vehicle provides full autonomous driving capabilities without QoS scores, then the ease of operation is improved, but the adaptability to different environmental conditions and user preferences deteriorates
Solution Approach 1:
The QoS system dynamically adapts to varying environmental conditions by continuously determining autonomous driving factors such as map data availability, weather conditions, traffic conditions, and sensor health for each route segment. The system adjusts QoS scores in real-time based on these dynamic factors, allowing the vehicle to adapt its autonomous driving operation to current environmental conditions while maintaining ease of use through automated score calculation and route recommendation
3Measurement precision
If the vehicle calculates detailed QoS scores for multiple route segments, then the measurement precision of autonomous driving quality is improved, but the loss of time for calculation and processing increases
Solution Approach 1:
The system performs preliminary calculation of QoS scores for multiple potential route segments before the vehicle needs to make a routing decision. By pre-calculating scores based on map data, weather, traffic, and sensor factors, the system achieves high measurement precision without causing time loss during critical driving moments, as the calculations are completed in advance
Solution Approach 2:
The QoS calculation is segmented into discrete factors (map data quality, weather conditions, traffic conditions, sensor health) that can be independently evaluated and processed. This segmentation allows the system to calculate precise QoS scores efficiently by processing each factor separately and combining the results, reducing overall calculation time while maintaining precision
Data Source
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AI summary
Technologies for autonomous vehicle driving quality of service (QoS) determination and communication include an advanced vehicle with a vehicle computing device. The computing device determines multiple route segments of one or more routes to a destination. The computing device determines, for each route segment, one or more autonomous driving factors that are each indicative of an autonomy level achievable by the advanced vehicle for the associated route segment. Factors may include map availability, weather conditions, road conditions, or other factors. The computing device determines, for each route segment, an autonomous QoS score based on the autonomous driving factors. The computing device may rank multiple routes to the destination based on the autonomous QoS scores associated with the route segments of those routes. The computing device may display a proposed route to a user of the advanced vehicle and receive a selection from the user. Other embodiments are described and claimed.