Personalized Adaptive Cruise Control Using Driver Behavior Learning
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Solution Overview
Problem
Existing driver assistance systems lack the ability to fully adjust to individual driver preferences, providing limited customization options for personalized driving experiences.
Innovation Solution
A driver assistance system that incorporates a personalization algorithm capable of learning a driver's habits and preferences, adjusting parameters such as following gap, acceleration, and deceleration based on observed driving behaviors, and adapting to different driving conditions.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If a driver assistance system uses fixed standard parameters for cruise control, then the system is simple to operate and manufacture, but it cannot adapt to individual driver preferences and provides limited customization
Solution Approach 1:
The system performs preliminary actions by collecting driving data during a learning phase before full personalization is implemented. The processor gathers information about the driver's acceleration patterns, following distances, and response to various driving conditions, then uses this pre-collected data to automatically adjust cruise control parameters tailored to that specific driver's preferences.
Solution Approach 2:
The driver assistance system serves itself by automatically learning and adapting to the driver's preferences without requiring manual configuration. The processor continuously monitors driving behavior and self-adjusts parameters such as acceleration rates, deceleration profiles, and following gaps based on observed patterns, eliminating the need for the driver to manually program or adjust these settings.
2Measurement precision
If the system collects and analyzes extensive driving behavior data, then it can accurately learn driver habits and provide personalized control, but it increases the complexity of data processing and system response time
Solution Approach 1:
The system applies partial action by initially focusing on collecting and analyzing only the most critical driving parameters such as acceleration patterns and following distances, rather than attempting to process all possible driving behaviors simultaneously. This allows the system to achieve meaningful personalization faster, with the capability to gradually incorporate additional behavioral nuances as the learning phase progresses.
3Ease of operation
If the system provides multiple customization options for driving parameters, then it enhances driver comfort and familiarity, but it complicates the ease of operation and initial setup
Solution Approach 1:
The system eliminates the need for manual configuration of multiple parameters by implementing self-service learning. The processor automatically monitors the driver's natural driving behavior and adjusts parameters such as acceleration rates, deceleration profiles, and following distances based on observed patterns, providing personalized comfort without requiring the driver to navigate complex setup menus or understand technical parameters.
Data Source
AI summary
A vehicular personalized adaptive cruise control system includes a forward-viewing camera viewing forward through a windshield of a vehicle, and an electronic control unit disposed at the vehicle. When the vehicle is operating in an adaptive cruise control mode, the system controls driving of the vehicle. When the equipped vehicle is not operating in the adaptive cruise control mode, a driver present in the vehicle drives the vehicle. The system identifies the driver via processing of image data captured by a cabin monitoring camera. When the identified driver drives the vehicle with the vehicle not operating in the adaptive cruise control mode, the system determines and stores personalized parameters for the identified driver. With the identified driver present in the vehicle, and when the vehicle is operating in the adaptive cruise control mode, the system uses the determined personalized parameters.