Vehicle-to-Vehicle Reward System for Drafting Incentives
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Solution Overview
Problem
Current vehicle communication systems lack effective methods to incentivize and reward beneficial social interactions such as drafting and autonomous droning, which can improve fuel efficiency and safety, while also affecting insurance policies based on driving behaviors.
Innovation Solution
A system that analyzes vehicle operational data to determine drafting and autonomous droning relationships, calculating rewards such as fuel savings and insurance factors, and adjusts insurance policies accordingly, using vehicle-to-vehicle communication and data analysis.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of energy
If vehicle-to-vehicle communication systems are implemented to enable social interactions like drafting and autonomous droning, then fuel efficiency and safety are improved, but the system complexity and data analysis requirements increase
Solution Approach 1:
The system segments the complex V2V communication functionality into distinct modules: data collection modules in individual vehicles, centralized analysis server, and reward allocation system. This segmentation allows each component to handle specific tasks independently, reducing overall system complexity while enabling sophisticated analysis of drafting and autonomous droning relationships to identify fuel-efficient driving behaviors.
Solution Approach 2:
A centralized server acts as an intermediary between vehicles and the reward allocation system. The server receives operational data from multiple vehicles, analyzes drafting characteristics and autonomous droning relationships, determines fuel savings, and allocates rewards. This intermediary approach simplifies the architecture by centralizing complex analysis functions rather than requiring each vehicle to perform comprehensive analysis independently.
2Loss of energy
If comprehensive analysis of vehicle operational data is performed to determine drafting relationships and rewards, then fuel savings and insurance factors are improved, but data processing time and computational resources increase
Solution Approach 1:
The system performs preliminary data collection and preliminary analysis of drafting characteristics continuously during vehicle operation. By maintaining running calculations of vehicle spacing, speed differentials, and autonomous droning status, the system prepares processed data structures that can be quickly queried for reward determination without requiring full re-analysis when reward allocation is needed.
Solution Approach 2:
The system extracts and focuses analysis only on specific critical parameters relevant to drafting and autonomous droning relationships, such as vehicle spacing, relative speed, and autonomous driving mode status. Rather than processing all possible vehicle operational data uniformly, the system selectively extracts and analyzes only the parameters necessary for identifying rewarding social interaction behaviors, reducing computational time and resources.
3Reliability
If rewards are allocated based on drafted fuel savings and autonomous droning characteristics, then driver behavior modification and insurance premiums are improved, but the difficulty of detecting and measuring drafting relationships increases
Solution Approach 1:
The system uses universal V2V communication data standards and standardized sensor protocols that work across multiple vehicle makes and models. The same analysis algorithms detect drafting relationships whether the vehicles are from different manufacturers or have different sensor configurations. This universality simplifies detection by using consistent measurement criteria across all vehicles rather than requiring vehicle-specific calibration and detection methods.
Solution Approach 2:
The system continuously monitors vehicle operational data and provides real-time feedback on drafting status and autonomous droning relationships. By continuously comparing current vehicle parameters against established drafting characteristics and providing immediate feedback, the system accurately detects and measures social interaction behaviors, ensuring reliable identification of rewarding driving patterns for reward allocation.
Data Source
AI summary
System, apparatus, and methods are disclosed for determining, through vehicle-to-vehicle communication, a drafting characteristic of a drafting relationship using vehicle operational data, where the drafting characteristic may include one or more of a vehicle spacing between a first vehicle and a second vehicle, vehicle speed, and vehicle type. Vehicle driving data and other information may be used to calculate an autonomous droning reward amount. In addition, vehicle involved in a drafting relationship in addition to, or apart from, an autonomous droning relationship may be financially rewarded. Moreover, aspects of the disclosure related to determining ruminative rewards and/or aspects of vehicle insurance procurement/underwriting.