Platooning Formation Pattern Proposal for Diverse Vehicles
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Solution Overview
Problem
Conventional platooning systems face challenges in forming efficient formations when a wide variety of vehicles participate, leading to decreased incentives and usability, as they typically prioritize a single index and are limited to commercial vehicles.
Innovation Solution
A platooning formation pattern proposal method and system that collects information from multiple vehicles, extracts index factors from predetermined indices, calculates scores for multiple indices, and proposes a formation pattern that satisfies predetermined conditions, ensuring a balanced recommendation for diverse vehicle participation.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If conventional platooning systems prioritize a single index for formation, then the formation process is simple and fast, but the adaptability to diverse vehicle types deteriorates and incentives for participation decrease
Solution Approach 1:
The formation pattern is evaluated by segmenting the performance criteria into multiple independent indices (fuel efficiency index, time index, cost index, safety index). Each index is calculated separately based on specific vehicle characteristics, allowing comprehensive evaluation of diverse vehicle types without overwhelming complexity in a single evaluation framework.
Solution Approach 2:
The system implements a universal evaluation framework that can assess multiple formation patterns against multiple indices simultaneously. This multi-functional approach allows the same system to evaluate different vehicle combinations (trucks, buses, mixed fleets) using consistent criteria, enhancing adaptability while maintaining a structured evaluation process.
2Ease of operation
If multiple indices are evaluated for formation patterns, then the usability and incentive for participation improve, but the calculation time and processing complexity increase
Solution Approach 1:
The system performs preliminary calculations by pre-establishing the evaluation indices and their corresponding vehicle characteristic requirements. Formation patterns are generated and evaluated in advance using these pre-defined criteria, allowing rapid assessment without time-consuming on-the-fly calculations during actual platooning operations.
Solution Approach 2:
The system dynamically adjusts the weightings and thresholds of different indices based on specific platoon compositions and operating conditions. By changing parameter settings rather than recalculating entire formation evaluations from scratch, the system maintains comprehensive multi-index assessment while reducing processing time for subsequent evaluations.
3Adaptability or versatility
If the system is limited to commercial vehicles, then the formation pattern generation is straightforward, but the versatility and real-world applicability deteriorate
Solution Approach 1:
The evaluation system applies local quality assessment by tailoring specific index criteria to the characteristics of different vehicle types. Each vehicle type (trucks, buses, motorcycles, cars) has specific characteristic parameters assigned to relevant indices, allowing nuanced evaluation that accommodates diversity while maintaining a unified overall framework.
Solution Approach 2:
The system achieves universality by designing a multi-functional evaluation framework that can handle various vehicle types through a common set of indices. The same fundamental evaluation structure works for commercial vehicles, passenger vehicles, and mixed fleets, with only the specific parameter values needing adjustment rather than requiring separate evaluation systems.
Data Source
AI summary
By a platooning formation pattern proposal method, a platooning formation pattern proposal device, or a platooning formation pattern proposal system, a formation pattern of platooning is proposed when vehicles perform the platooning, information related to the formation pattern is collected, information in a predetermined index is extracted from the collected information, a score of the index is calculated, a recommendation formation pattern is proposed.


