Adaptive Vehicle Feature Recommendation System
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Solution Overview
Problem
Vehicle owners often fail to utilize the full range of features provided by their vehicles due to limited opportunities to learn about and interact with them, leading to a suboptimal driving experience and potential dissatisfaction with the vehicle and manufacturer.
Innovation Solution
A system that uses a processor to determine underutilized vehicle features and recommends their engagement based on measured conditions and crowd behavior data, allowing for real-time reminders and configuration options via a mobile device, enabling drivers to access and configure features effectively without diverting attention from driving.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If customers are provided with extensive vehicle features and controls, then vehicle functionality and customer control over driving experience are improved, but customer ability to learn and utilize all features is worsened
Solution Approach 1:
The system continuously monitors vehicle usage data and provides feedback to customers about features they are not utilizing. The processor determines underutilized features by comparing actual usage against expected usage patterns, then delivers targeted information about these features through the user interface, helping customers discover and learn about available functionality without overwhelming them with all features at once.
Solution Approach 2:
The system enables customers to self-educate about vehicle features by providing on-demand information about underutilized features. Rather than requiring formal training or manual reading, the system automatically identifies what features the customer should know about and delivers concise guidance through the mobile device or vehicle display, allowing customers to learn at their own pace and convenience.
2Loss of information
If customers are required to sit through vehicle demonstrations or read substantial vehicle manuals to learn features, then feature knowledge is improved, but customer time and convenience are worsened
Solution Approach 1:
The system performs preliminary analysis of vehicle usage data before the customer needs information. By continuously monitoring feature usage in the background and pre-identifying underutilized features, the system has the information ready when the customer is next interacting with the vehicle or receives a notification on their mobile device, eliminating the need for time-consuming demonstrations or manual reading sessions.
Solution Approach 2:
Rather than providing all possible feature information at once (excessive action), the system provides only the specific subset of information that is most relevant to each customer's needs (partial action). By focusing only on underutilized features that match the customer's usage patterns and preferences, the system delivers sufficient information for effective feature use without the time cost of comprehensive training materials.
3Productivity
If the system provides detailed feature recommendations and configuration options, then feature utilization is improved, but system complexity and processing requirements are worsened
Solution Approach 1:
The system segments the complex task of feature recommendation into distinct processing stages: data collection from vehicle sensors and usage tracking, analysis to identify underutilized features, matching features against customer preferences and usage patterns, and delivery of tailored recommendations through appropriate interfaces. This segmentation allows each component to handle a specific aspect of the complexity rather than requiring a monolithic complex system.
Solution Approach 2:
The system introduces an intermediary processing layer between the vehicle's complex feature set and the customer. This intermediary (the recommendation system) translates raw usage data into meaningful insights, filters out irrelevant features, and presents only the most relevant configuration options to the customer, thereby reducing the effective complexity the customer must process while maintaining high feature utilization.
Data Source
AI summary
A system includes a processor configured to determine that measured vehicle variable values match a predefined set of vehicle variable values associated with recommending vehicle feature engagement. The processor is further configured to determine that the feature has not been used with a threshold frequency in a vehicle in which the variable values were measured. Also, the processor is configured to responsive to the match and use below the threshold, present a recommendation to engage the feature.