Vehicle Mission Control for Load and Traction Adaptation
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Solution Overview
Problem
Autonomous vehicles face challenges in maintaining energy efficiency, productivity, and vehicle longevity when operating in confined geographical areas with varying and unpredictable environments, particularly in hilly and muddy terrains, which affect traction control and energy consumption.
Innovation Solution
A computer system that processes travel mission data and real-time road conditions to adjust driving modes and load capacities, enhancing vehicle control by adapting traction force distribution and load levels to improve efficiency and reduce wear.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If autonomous vehicles operate in hilly and muddy terrains with heavy loads, then productivity and transport capacity are improved, but energy consumption increases and vehicle wear increases
Solution Approach 1:
The system dynamically adjusts driving modes and load capacity based on real-time road condition data and drivability impact assessments. The computer system modifies vehicle operation parameters adaptively rather than using fixed settings, allowing the vehicle to optimize energy consumption while maintaining productivity across varying terrain conditions.
Solution Approach 2:
The system changes operational parameters including driving mode, load capacity, and traction control levels based on determined drivability impact. By modifying these parameters in response to road conditions, the system resolves the contradiction between maintaining high transport capacity and reducing energy consumption in challenging terrains.
2Productivity
If autonomous vehicles operate in hilly and muddy terrains with heavy loads, then productivity and transport capacity are improved, but vehicle wear increases
Solution Approach 1:
The system dynamically adjusts driving modes and load capacity based on real-time road condition data and drivability impact assessments. The computer system modifies vehicle operation parameters adaptively rather than using fixed settings, allowing the vehicle to optimize energy consumption while maintaining productivity across varying terrain conditions.
Solution Approach 2:
The system performs beforehand cushioning by assessing drivability impact in advance and adjusting load capacity and driving modes proactively. This prevents excessive vehicle wear before it occurs by moderating operational intensity in challenging terrains, rather than reacting to wear after damage has occurred.
3Use of energy by moving object
If real-time adjustments to driving modes and load capacity are implemented, then energy efficiency is improved, but device complexity increases
Solution Approach 1:
The computer system performs multiple functions including receiving road condition data, determining drivability impact, selecting driving modes, and adjusting load capacity within a single integrated system. This multi-functionality reduces the need for separate specialized systems, thereby limiting the increase in device complexity while achieving real-time energy efficiency optimization.
Solution Approach 2:
The system uses feedback from real-time road condition data to continuously adjust driving modes and load capacity. This closed-loop control enables energy efficiency improvements through adaptive adjustments while keeping the control architecture relatively simple by using the existing sensor and communication infrastructure for feedback.
Data Source
Figure 1A~1B
Figure 1C~2
Figure 3
AI summary
The present disclosure relates to a computer system (100) for controlling one or more vehicles (10, 10a to 10n) operating in a confined geographical area (200), the computer system comprising processing circuitry (102) configured to receive travel mission data for at least one vehicle (10a) of a plurality of vehicles within the confined geographical area, the travel mission data comprising at least data about an intended route (220) for completing a transport mission (212); obtain real-time road condition data for the intended route; based on the obtained real-time road condition data, determine a drivability impact for the at least one vehicle intended to perform the travel mission along the intended route, the drivability impact being indicative of an estimated decrease in any one of a vehicle traction control level and an energy efficiency level; in response to the determined drivability impact, adapt any one of a driving mode and load capacity for the travel mission for the at least one vehicle; and control the at least one vehicle based on any one of the adapted driving mode and adapted load capacity for the travel mission.