Autonomous Vehicle Driving Profiles for Personalized Control
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Solution Overview
Problem
Current driver assistance systems in autonomous vehicles lack personalized control, failing to adapt to individual driver preferences and styles, which can lead to suboptimal driving experiences and safety concerns.
Innovation Solution
An autonomous vehicle control system that receives a driver profile with customizable settings, learns the driver's style through manual mode data, and adjusts vehicle control systems in autonomous mode to mimic the driver's preferences, incorporating real-time feedback for optimal performance.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Extent of automation
If driver assistance systems are made fully automated, then driver interaction is reduced and driver can perform other tasks, but the system lacks personalized control and fails to adapt to individual driver preferences
Solution Approach 1:
The system performs preliminary actions by collecting driving data during manual mode operation before full automation is engaged. This preliminary data collection phase allows the system to build personalized driver profiles in advance, so that when automated mode is activated, the system already has customized parameters ready to provide personalized control without requiring real-time adaptation during autonomous operation.
Solution Approach 2:
The system implements feedback mechanisms where driver responses to automated maneuvers are continuously monitored and used to refine personalized profiles. Driver feedback during and after automated operation informs adjustments to control parameters, enabling the system to adapt to individual preferences while maintaining high automation levels. This closed-loop feedback resolves the contradiction by making the automated system responsive to individual driver characteristics.
2Adaptability or versatility
If the system collects and processes driver data to learn driving style, then personalized control is achieved, but system complexity increases
Solution Approach 1:
The system applies universality by using a multi-functional electronic processor that handles both standard vehicle control operations and complex data analysis for personalization. The same processor that manages basic automation also performs machine learning and profile management, eliminating the need for separate dedicated hardware for each function. This consolidates complexity into existing components rather than adding separate systems.
Solution Approach 2:
The system implements self-service through automated data collection and analysis processes that occur without additional manual intervention. The electronic processor automatically monitors driver behavior, analyzes patterns, and updates personalized profiles without requiring separate calibration sessions or manual programming. This self-learning capability reduces operational complexity despite the advanced personalization features.
3Ease of operation
If the system adjusts control parameters dynamically based on driver profile, then driving experience is optimized, but response time and processing requirements increase
Solution Approach 1:
The system performs preliminary computation by pre-calculating and storing optimized control parameters in driver profiles during manual mode operation. When automated mode is activated, the system retrieves pre-computed parameters rather than calculating them in real-time, significantly reducing processing time during critical driving moments while maintaining optimized driving experience.
Solution Approach 2:
The system applies dynamics by implementing a hierarchical control architecture where static baseline parameters from profiles provide quick response for routine operations, while dynamic adjustments are made only when sensor data indicates situations requiring personalized adaptation. This dynamic switching between pre-computed and real-time adjusted parameters optimizes both response time and personalization effectiveness.
Data Source
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AI summary
Systems and methods for controlling an autonomous vehicle. One method includes receiving, with an electronic processor of the autonomous vehicle, a profile selection. The method further includes receiving, with the electronic processor, a driver profile including a plurality of settings based on the profile selection. The method further includes, when the autonomous vehicle is operating in an autonomous driving mode, controlling, with the electronic processor, at least one vehicle control system of the autonomous vehicle based on at least one of the plurality of settings. The method further includes operating, with the electronic processor, the autonomous vehicle in a manual driving mode. The method further includes receiving, with the electronic processor, data from at least one sensor while the autonomous vehicle operates in the manual driving mode. The method further includes determining, with the electronic processor, a driving style based on the data and adjusting at least one of the plurality of settings based on the driving style.