Vehicle Lane Guidance Using Swarm Data Under Recognition Uncertainty
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Solution Overview
Problem
Modern motor vehicles equipped with automated driver assistance systems face challenges in accurately determining the road course under varying lighting conditions, fog, and poor road markings, leading to unreliable lane change decisions and potential loss of automation due to strict lateral acceleration regulations.
Innovation Solution
A method that combines optical recordings from an image capture unit with swarm data from a database to determine the uncertainty factor of lane recognition, using weighting factors to create a mixed course for vehicle guidance, incorporating meta-information such as environmental conditions and localization confidence to improve the reliability of lane detection and vehicle guidance.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If pattern recognition methods are used to recognize the road course from optical images, then the system can process the road course information, but the recognition accuracy deteriorates under varying lighting conditions, fog, and poor road markings
Solution Approach 1:
The patent introduces an intermediary system that combines multiple data sources (optical images, map data, sensor data) with pattern recognition algorithms to mediate between the raw optical input and the final lane detection output. This intermediary processing layer compensates for deficiencies in individual data sources under challenging conditions.
Solution Approach 2:
The system creates a composite information model by fusing data from multiple sources (optical images, swarm data, map data, sensor measurements) similar to how composite materials combine different properties. This composite approach ensures reliable lane detection even when individual data sources are degraded by environmental conditions.
2Reliability
If the system strictly follows current lateral acceleration regulations for lane changes, then legal compliance is ensured, but necessary lane changes may be aborted and control transferred to the driver unnecessarily
Solution Approach 1:
The system performs preliminary assessment of road geometry and curvature using the mixed course information before executing lane changes. By pre-calculating whether a lane change will violate lateral acceleration limits based on the reliable mixed course data, the system can approve necessary lane changes while ensuring regulatory compliance.
Solution Approach 2:
The system continuously monitors actual lateral acceleration during lane changes and compares it against regulatory limits, using feedback to adjust control commands in real-time. This allows the system to maintain compliance while completing necessary lane changes that might be aborted by simpler threshold-based systems.
3Device complexity
If only optical images from the image capture unit are used to determine the ego lane course, then the system is simpler, but the reliability deteriorates in challenging environmental conditions
Solution Approach 1:
The patent merges multiple data sources (optical image processing, map data, swarm data from other vehicles, sensor measurements) into a unified mixed course representation. This combination of diverse information sources significantly improves reliability in challenging environmental conditions compared to using optical images alone.
Solution Approach 2:
The mixed course determination system serves multiple functions: it provides accurate lane geometry for lane change decisions, serves as a reference for driver behavior analysis, and enables various driver assistance functions. This multi-functional approach justifies the increased system complexity.
Data Source
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AI summary
The invention relates to a method for operating a motor vehicle (1). Within this method, an optical image of a roadway ahead of the motor vehicle (1) is created using an image acquisition unit (4). Based on this image (A), an ego lane (EF) of the roadway assigned to the motor vehicle (1) is determined, and based on this image (A), a preceding path (EV) of the ego lane (EF) is determined. Furthermore, at least one piece of meta-information is acquired that can influence the recognition of the ego lane (EF) and/or the preceding path (EV) of the ego lane (EF).Furthermore, swarm data (SD) about the roadway is retrieved from a database (6). From the swarm data (SD), a comparison lane (VF) of the roadway and a comparison trajectory (VV) of the comparison lane (VF) are derived, and based on at least one piece of meta-information, the preceding trajectory (EV), and the comparison trajectory (VV), an uncertainty factor (UF) for the recording (A) of the image acquisition unit (4) is determined. Depending on the uncertainty factor (UF), weighting factors for the preceding trajectory (EV) and for the comparison trajectory (VV) are determined, and a mixed trajectory (MV) of the ego lane (EF) is provided for a vehicle guidance function of the motor vehicle (1) on the roadway, depending on the respective weighting factors.