Automated Driving Tuning Using Scenario-Based User Feedback
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Solution Overview
Problem
Current Automated Driving Systems (ADS) lack personalized tuning parameters, leading to suboptimal user experience and reduced autonomy, as they do not effectively adapt to individual driver preferences and varying environmental scenarios.
Innovation Solution
A method utilizing a self-learning model that receives environmental data and user feedback to dynamically set tuning parameters for ADS, such as Adaptive Cruise Control, by determining environmental scenarios and updating the model based on user satisfaction, thereby providing customized settings.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If traditional fixed tuning parameters are used in ADS, then system simplicity is maintained, but user satisfaction and adaptability deteriorate
Solution Approach 1:
The patent implements dynamic tuning parameters that automatically adapt to different driving scenarios and user preferences. The system transitions from fixed parameters to dynamically adjustable parameters based on environmental conditions, vehicle state, and learned user preferences, resolving the contradiction between adaptability and complexity through automated dynamic adjustment
Solution Approach 2:
The system employs self-learning models that automatically improve tuning parameters without requiring manual user configuration. The ADS learns from user feedback and environmental data to autonomously optimize parameters, eliminating the need for complex manual tuning interfaces while enhancing adaptability to individual user preferences
2Ease of operation
If more physical buttons and interactions are provided for parameter adjustment, then user control is improved, but driver workload and complexity increase
Solution Approach 1:
The patent replaces physical buttons and manual adjustment mechanisms with automated electronic systems. The tuning parameter adjustment is performed automatically by the control device based on sensor data and learning models, eliminating the need for physical controls while maintaining ease of operation through invisible automation
Solution Approach 2:
The system implements feedback loops where user responses to ADS actions are continuously monitored and used to automatically adjust tuning parameters. This feedback mechanism enables the system to learn and adapt to user preferences without requiring explicit manual input, improving ease of operation while minimizing physical interactions
3Adaptability or versatility
If generic tuning parameters are used for all scenarios, then system simplicity is maintained, but user satisfaction and personalized experience deteriorate
Solution Approach 1:
The patent implements dynamic parameter changes based on detected driving scenarios and user preferences. The system automatically modifies tuning parameters such as acceleration rates, deceleration profiles, and response thresholds to match different environmental conditions and individual user preferences, achieving personalization through automated parameter adaptation
Solution Approach 2:
The system performs preliminary learning during normal operation to build user preference profiles before specific driving scenarios occur. By continuously gathering and analyzing user feedback data in advance, the system is prepared to provide personalized tuning parameters when needed, enhancing both personalization capability and automation level
Data Source
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AI summary
A method for setting a tuning parameter for an Automated Driving System (ADS) of a vehicle is disclosed. A corresponding non-transitory computer-readable storage medium, vehicle control device and a vehicle comprising such a control device are also disclosed. The method comprises receiving environmental data from a perception system of the vehicle, said environmental data comprising a plurality of environmental parameters, determining, by means of a self-learning model, an environmental scenario based on the received environmental data;, setting the tuning parameter for the ADS based on the self-learning model and the determined environmental scenario, the tuning parameter defining a dynamic parameter of the ADS, receiving at least one signal representative of a vehicle user feedback on the set tuning parameter, and updating the self-learning model for the set tuning parameter for the identified environmental scenario based on the received vehicle user feedback.