Automated Vehicle Turning Calibration Using Generated Driving Patterns
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Solution Overview
Problem
Existing automated vehicle driving systems face inefficiencies in calibrating turning characteristics during the automated steering control process, requiring manual parameter setting which is time-consuming and not optimized for varying vehicle types and driving environments.
Innovation Solution
An automated method where driving patterns are generated and data on turning characteristics are acquired through automated driving, using a steering robot, self-position detecting device, and controller to set parameters such as turning curvature, yaw rate, and stability factors based on acquired data.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If manual parameter setting is used in calibration process, then flexibility in adjusting turning characteristics is maintained, but calibration time and complexity increase significantly
Solution Approach 1:
The system automatically generates driving patterns and acquires turning characteristic data through automated driving, eliminating the need for manual parameter setting. The automated vehicle driving system performs calibration independently by controlling the steering robot to execute generated patterns and collect data, thereby reducing calibration time while maintaining accuracy.
Solution Approach 2:
Driving patterns are automatically generated before the calibration process begins, preparing the test scenarios in advance. This preliminary generation of driving patterns enables the subsequent automated data collection to proceed efficiently without delays, resolving the time loss issue while preserving operational flexibility.
2Productivity
If automated driving patterns are generated and used, then calibration efficiency is improved, but system complexity increases due to additional components
Solution Approach 1:
The automated vehicle driving system integrates multiple functions into a unified platform: the controller generates driving patterns, controls the steering robot, manages the self-position detecting device, and processes calibration data. This multi-functional integration improves calibration efficiency while managing system complexity through consolidation rather than proliferation of separate components.
Solution Approach 2:
The controller serves as an intermediary that coordinates between the driving pattern generator, steering robot, self-position detecting device, and data processing systems. This centralized mediation simplifies the overall system architecture by providing a single point of control, thereby improving efficiency without proportionally increasing complexity.
3Loss of time
If driving patterns are automatically generated without driver input, then time consumption is reduced, but adaptability to specific driving areas may be compromised
Solution Approach 1:
The system performs preliminary acquisition of the driving area by having the driver operate the vehicle along the perimeter to define the operational boundaries. This preliminary action captures the specific characteristics of the driving area, which are then stored and used to automatically generate adaptive driving patterns without requiring repeated manual input, thus reducing time consumption while maintaining adaptability.
Solution Approach 2:
The driving area is copied or recorded during the initial driver-operated perimeter traversal, creating a digital representation that can be reused for automatic pattern generation. This copying approach preserves the adaptability to specific driving areas while eliminating the need for repeated manual setup, thereby reducing time consumption in subsequent calibrations.
Data Source
AI summary
In a setting method of turning characteristics of an automated vehicle driving system, driving patterns for acquisition of a parameter of turning characteristics are generated automatically. Data concerning the turning characteristics of a vehicle is acquired through automated driving of the vehicle with the generated driving patterns. The parameter is set based on the acquired data.