Vehicle Lateral Control Using Plausibility-Checked Swarm Data
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Solution Overview
Problem
Existing vehicle lateral control systems rely solely on sensor data, which can be unreliable due to malfunctions or environmental conditions, leading to potential errors in maintaining the vehicle within a designated movement corridor.
Innovation Solution
The system incorporates stored environment data from multiple vehicles that have previously traveled the same path, allowing for a plausibility check against current sensor data, prioritizing accurate data for lateral control while switching to current sensor data for short-term changes.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Extent of automation
If sensor data is used for lateral control, then the system can operate autonomously, but the reliability deteriorates due to sensor failures or environmental conditions
Solution Approach 1:
The patent introduces an intermediary verification mechanism where detected environment data serves as a reference to validate swarm data from other vehicles. This intermediary check allows the system to leverage multiple data sources (current sensor data and historical swarm data) without directly combining them, thereby improving reliability while maintaining autonomous operation.
Solution Approach 2:
The system performs preliminary plausibility checks on swarm data using currently detected environment data before utilizing the swarm data for lateral control. This preliminary verification ensures that only reliable historical data is used, preventing propagation of errors while maintaining system autonomy.
2Reliability
If swarm data from other vehicles is used for lateral control, then the reliability improves through verified environment data, but the device complexity increases due to data management requirements
Solution Approach 1:
The patent extracts only the necessary environment data (roadway boundary positions and courses) from the swarm data received from other vehicles, rather than processing all available data. This selective extraction reduces computational complexity while maintaining the reliability benefits of verified environment data from multiple sources.
Solution Approach 2:
The system creates a simplified reference model of the environment based on swarm data from other vehicles, using this copied information as a verification reference against current sensor data. This copying approach allows complex multi-vehicle data to be reduced to essential environmental features for validation purposes.
3Adaptability or versatility
If current sensor data is used for lateral control, then the system can respond to short-term environmental changes, but the measurement precision deteriorates in adverse conditions
Solution Approach 1:
The system uses currently detected environment data as feedback to verify the accuracy of swarm data from other vehicles. This feedback mechanism allows the system to maintain adaptability to short-term changes while improving measurement precision by cross-validating against reliable historical data from multiple sources.
Solution Approach 2:
The patent implements a dynamic data selection strategy where the system adaptively switches between using swarm data and current sensor data based on plausibility verification results. This dynamic approach allows the system to respond to environmental changes while maintaining high measurement precision by selecting the most reliable data source in each situation.
Data Source
AI summary
Technologies and techniques for the lateral control of a vehicle. Environment data of a vehicle is detected when travelling a route, and stored environment data, detected when travelling the route by a plurality of other vehicles not currently travelling the route, is received. The plausibility of the stored environment data is checked on the basis of the environment data detected. Lateral control of the vehicle is executed on the basis of the environment data checked for plausibility.