Autonomous Vehicle Control System Using User Feedback
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Solution Overview
Problem
The quality and reliability of automated driving and driving assistance systems in vehicles are challenging to determine due to the diverse subjective evaluations of users, leading to increased resource requirements for quality control across various scenarios.
Innovation Solution
Implementing a vehicle control system that collects both quantitative and subjective data from users to identify correlations between vehicle parameters and user feedback, allowing for adjustments to autonomous vehicle operations to improve system quality and reliability, utilizing learning algorithms like neural networks for enhanced analysis and efficiency.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If automated driving systems operate across diverse scenarios with multiple user preferences, then system adaptability and user satisfaction improve, but quality control resources and system complexity increase significantly
Solution Approach 1:
The system implements feedback loops where user subjective evaluations of autonomous maneuvers are collected, analyzed, and used to adjust system parameters. This allows the system to adapt to individual user preferences automatically, improving adaptability without requiring manual reconfiguration or increasing operational complexity for quality control
Solution Approach 2:
The system performs self-adjustment by automatically modifying autonomous vehicle operation parameters based on analyzed user feedback patterns. This self-service capability enables the system to adapt to user preferences autonomously, eliminating the need for external quality control intervention and reducing system complexity from an operational perspective
2Reliability
If traditional quality control methods are used without user feedback integration, then system operation remains stable and predictable, but system reliability and user satisfaction deteriorate due to inability to adapt to individual preferences
Solution Approach 1:
The system transitions from static, pre-programmed autonomous driving parameters to dynamic parameters that automatically adjust based on real-time user feedback. This allows the system to maintain reliability through controlled adaptation while improving ease of operation by tailoring behavior to individual user preferences and expectations
3Adaptability or versatility
If user feedback is collected and analyzed for every autonomous maneuver, then system adaptability and user satisfaction improve, but data processing requirements and system complexity increase
Solution Approach 1:
The system extracts only the most relevant features and patterns from user feedback data rather than processing complete raw datasets. By focusing on key evaluation metrics and correlation patterns, the system achieves effective adaptability while significantly reducing data processing requirements and computational resource consumption
Data Source
AI summary
Examples of the disclosure are directed to using vehicle users' subjective evaluation of autonomous vehicle performance to adjust operation of one or more autonomous vehicle systems, such as an adaptive cruise control system. In some examples, a vehicle can perform an autonomous maneuver, and can use automotive sensors to collect data describing one or more quantitative aspects of that vehicle during the maneuver, such as make, model, GPS coordinates, mileage, speed, engine activity, or LIDAR data, among other possibilities. In some examples, a control system can receive subjective feedback inputted by the vehicle's user that indicates one or more qualitative evaluations of the completed autonomous maneuver. In some examples, a control system may perform statistical analysis of collected vehicle data and of subjective feedback input by vehicle users to identify correlations between quantitative vehicle parameters, specified types of user feedback and/or autonomous vehicle maneuvers.