Adaptive Off-Road Feature Control Using Crowd-Sourced Terrain Data
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Solution Overview
Problem
Off-road driving presents challenges due to varying terrain and lack of terrain and course knowledge, which can lead to vehicles being inadequately equipped or driven unsuitably for specific courses, resulting in potential damage and skill-related detriments.
Innovation Solution
A system utilizing processors to determine vehicle capabilities and recommend suitable off-road courses and feature settings based on crowd-sourced data, providing real-time recommendations and automatic adjustments to ensure safe and successful navigation.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If drivers navigate off-road courses without pre-knowledge of terrain and course characteristics, then driver autonomy and exploration are maintained, but vehicle damage risk and traversal failure increase due to inadequate feature engagement
Solution Approach 1:
The system collects crowd-sourced data from multiple vehicles traversing the same course, aggregates feature engagement patterns, and feeds this information back to individual vehicles in real-time. This allows drivers to maintain autonomy while receiving data-driven recommendations about which features to engage based on actual performance data from other drivers on the same course.
Solution Approach 2:
The system enables vehicles to independently determine their own optimal feature settings by processing crowd-sourced data and generating personalized recommendations. Each vehicle serves itself by making intelligent decisions about feature engagement based on aggregated knowledge from the fleet, without requiring manual intervention or pre-programming.
2Reliability
If vehicles are pre-configured with all possible off-road features engaged, then vehicle capability and traversal success increase, but device complexity and fuel consumption increase
Solution Approach 1:
Instead of static preconfiguration, the system dynamically adjusts feature engagement based on real-time conditions and crowd-sourced performance data. Features are engaged or disengaged adaptively as the vehicle progresses through different sections of the course, optimizing capability while minimizing complexity.
Solution Approach 2:
The system changes operational parameters (feature engagement states) based on aggregated data from crowd-sourced traversals. By analyzing which features were successfully used by other vehicles on the same course, the system optimizes the current vehicle's feature configuration to match proven successful settings without requiring exhaustive preconfiguration.
3Reliability
If drivers manually research and configure vehicle features for each off-road course, then feature appropriateness improves, but time consumption and operational complexity increase
Solution Approach 1:
The system performs preliminary analysis of course characteristics and crowd-sourced traversal data before the driver begins the course. By pre-processing information about terrain types, obstacles, and successful feature engagements from other vehicles, the system prepares optimized feature recommendations in advance, eliminating the need for manual research during course preparation.
Solution Approach 2:
The system acts as an intermediary between the driver and the complex task of feature configuration. Instead of requiring drivers to manually research and configure features, the system automatically aggregates crowd-sourced data, analyzes course requirements, and presents simplified recommendations, mediating the complex information processing task.
Data Source
AI summary
A system receives a request for course identification and determines specific vehicle off-road capabilities for a vehicle from which the request was received. The system determines, for presentation, one or more courses within a geographic area having terrain over which the vehicle can travel based on the determined capabilities and presents the one or more courses as selectable course options and receive selection of a course from the course options. Also, the system determines, for the selected course, geographic change regions where an optional vehicle feature should be engaged based on crowd-sourced data gathered for the selected course indicating that the vehicle feature is used by more than a threshold number of vehicles while traversing a travel region of the course following the change region and sends the vehicle the geographic change regions and the corresponding vehicle features.