Personalized Adaptive Cruise Control for Cut-In Driver Preference Learning
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Solution Overview
Problem
Traditional adaptive cruise control systems do not account for individual driver preferences, leading to inconsistent vehicle responses to trigger events such as cut-ins, cut-outs, road conditions, and weather, often causing drivers to deactivate the system.
Innovation Solution
Implementing personalized adaptive cruise control (PACC) that uses machine learning to create a driver profile based on observed behavior, adjusting vehicle responses to trigger events according to the driver's preferences, incorporating sensors and metadata to refine the adaptive cruise control system.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If traditional adaptive cruise control systems are implemented, then basic cruise control functionality is provided, but driver preferences are not accounted for leading to inconsistent vehicle responses and driver deactivation
Solution Approach 1:
The ACC system dynamically adapts its behavior by continuously learning driver preferences through machine learning algorithms. The system transitions from static, pre-programmed responses to dynamic, personalized control strategies that evolve based on observed driver behavior patterns during ACC trigger events.
Solution Approach 2:
The system performs self-learning and self-adjustment by automatically analyzing driver operations and traffic events to update its own control parameters. The machine learning model continuously refines the driver profile without requiring manual intervention, enabling the system to serve itself in optimizing its performance.
2Ease of operation
If personalized adaptive cruise control with machine learning is implemented, then driver comfort is enhanced by aligning vehicle responses with personal driving styles, but data processing and system complexity increase
Solution Approach 1:
The system implements continuous feedback loops where driver operations and traffic events are captured, analyzed, and used to update the driver profile. This feedback mechanism enables the system to learn from past behavior and continuously improve its responsiveness to driver preferences, enhancing comfort through adaptive personalization.
Solution Approach 2:
The machine learning model pre-processes and analyzes driver behavior patterns during ACC trigger events to establish a comprehensive driver profile before actual personalized control is needed. This preliminary action enables the system to have driver preferences ready and waiting, reducing real-time processing complexity during critical driving moments.
3Measurement precision
If event data capture and machine learning model updates are implemented, then personalized driver profiles are created, but computational resources and processing time are consumed
Solution Approach 1:
The system focuses on capturing and processing only the most relevant event data associated with ACC trigger events rather than all possible driving data. By selectively processing partial data sets that are most indicative of driver preferences, the system achieves high measurement precision while reducing overall computational burden and processing time.
Data Source
AI summary
Systems and methods are provided for learning a driving behavior during a cut-in cut-out event or other triggering event to create a profile that adjusts operation of the adaptive cruise control (ACC) component of the vehicle to mimic the preferences of the driver. The profile may be based on data collected during a previous cut-in cut-out event or other triggering event. The data may be transmitted to an adaptive cruise control system that uses the data as input to a machine learning model. Output of the machine learning model may update the profile for the driver that operates the vehicle in ACC during the cut-in cut-out event. When the vehicle is operating in ACC and an event is within a threshold value of the cut-in cut-out event occurs at a later time, the vehicle may apply rules defined in the profile.