Personalized Test Drive Routes from Consumer Driving Patterns
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Solution Overview
Problem
Existing vehicle test drives often lack personalization and efficiency, with consumers unfamiliar with the area and dealership agents providing generic routes that may not accurately represent the consumer's intended vehicle operation, leading to inefficiencies and increased wear on the test drive vehicle.
Innovation Solution
A system that generates tailored test drive routes based on a consumer's driving pattern, identifying waypoints and route segments that satisfy conditions associated with their driving style and preferences, using driving data to create a personalized route that starts and ends at a third-party entity location.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If a generic test drive route is provided by dealership agents, then the route can be easily established without complex analysis, but the route does not accurately represent the consumer's intended vehicle operation and increases wear on the test drive vehicle
Solution Approach 1:
The system collects and analyzes driving data from the consumer's existing driving behavior before generating the test drive route. This preliminary analysis of driving patterns, preferences, and typical routes enables the system to create a personalized test drive route that accurately reflects how the consumer intends to operate the vehicle, rather than using generic pre-established routes
Solution Approach 2:
The system automatically generates the personalized test drive route by processing the consumer's driving data through algorithms that identify patterns and preferences. This self-service approach eliminates the need for dealership agents to manually create custom routes for each consumer, reducing labor complexity while improving accuracy through data-driven personalization
2Measurement precision
If a personalized test drive route is generated based on driving data, then the route accurately reflects the consumer's driving pattern, but more computing resources are required to process and analyze the driving data
Solution Approach 1:
The system extracts only the essential features and patterns from the consumer's driving data that are relevant to route generation, such as typical routes, driving times, and preferred road types. By focusing on extracting only the necessary information rather than processing all raw data, the system reduces computing resource requirements while maintaining high accuracy in personalization
Solution Approach 2:
The system performs preliminary processing and filtering of driving data to identify key patterns and preferences before generating the test drive route. This pre-processing step organizes the data into meaningful categories and extracts actionable insights, reducing the computational burden during the actual route generation phase while preserving personalization accuracy
3Object-generated harmful factors
If the test drive route is optimized to reduce vehicle wear, then the vehicle experiences less wear and tear, but the route planning becomes more complex requiring multiple condition checks
Solution Approach 1:
The system incorporates multiple parameters and conditions into the route planning algorithm, such as road surface quality, traffic patterns, route length, and terrain characteristics. By evaluating and optimizing across multiple parameters simultaneously, the system identifies routes that minimize vehicle wear while maintaining personalization, with the added complexity handled automatically by the computational system rather than manual planning
4Ease of operation
If generic routes are used for test drives, then the route establishment is simple and quick, but the consumer experience lacks personalization and does not match their intended vehicle operation
Solution Approach 1:
The system automatically generates personalized test drive routes by processing the consumer's driving data through algorithms that identify patterns and preferences. This self-service approach eliminates the need for dealership agents to manually create custom routes for each consumer, maintaining ease of operation while significantly improving personalization and adaptability to individual driving styles
Solution Approach 2:
The system collects and analyzes driving data from the consumer's existing driving behavior before generating the test drive route. This preliminary analysis enables automatic personalization without requiring manual intervention during the route establishment process, maintaining simplicity for the consumer while delivering highly customized experiences
Data Source
AI summary
In some implementations, a system may receive driving data representative of a driving pattern of a user. The system may identify a third party entity to facilitate a test drive by the user. The system may identify a geographic area within a threshold proximity of a location of the third party entity. The system may identify a plurality of waypoints within the geographic area, wherein one or more of the waypoints or one or more route segments defined by the waypoints satisfy one or more conditions based on the driving data. The system may obtain the test drive route starting and ending at the location of the third party entity and including the waypoints. The system may transmit data indicating the test drive route to at least one of a user device, an on-board computer of a test drive vehicle, or a third party device of the third party entity.