Automated Vehicle Trajectory Selection with Environmental Filtering
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Solution Overview
Problem
Existing methods for automatic vehicle navigation lack flexibility and precision in selecting stored trajectories for parking, particularly in dynamic environments where additional information such as orientation, occupancy status, and obstacle proximity are not adequately considered.
Innovation Solution
A method and device that utilize additional information to improve trajectory selection by modifying or filtering stored paths, incorporating environmental data, user interactions, and ranking criteria to ensure optimal vehicle positioning, including the ability to adapt trajectories based on user preferences and environmental conditions.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If traditional automatic vehicle navigation methods are used, then the system can provide basic trajectory following, but the system lacks flexibility and precision in selecting stored trajectories for parking in dynamic environments
Solution Approach 1:
The system pre-stores multiple trajectories for different parking scenarios and environmental conditions. Before execution, the system selects the appropriate pre-stored trajectory based on current environmental data and additional information, avoiding the need to compute trajectories in real-time and enabling flexible adaptation to dynamic conditions.
Solution Approach 2:
The system dynamically adjusts trajectory selection by continuously monitoring environmental data and additional information (such as occupancy status and orientation requirements). The selection process adapts to changing conditions by filtering and ranking stored trajectories based on current environmental context, enabling real-time flexibility without increasing computational complexity during execution.
2Adaptability or versatility
If multiple trajectories are stored for different parking scenarios, then the system can handle diverse parking situations, but the difficulty of selecting the optimal trajectory increases
Solution Approach 1:
The system implements a feedback-based selection mechanism where environmental data and additional information are continuously fed back into the trajectory selection process. The system filters stored trajectories based on this feedback, comparing current environmental conditions with stored trajectory requirements, and automatically selects the best match, reducing selection difficulty despite having multiple trajectories available.
Solution Approach 2:
The system changes the selection parameters dynamically based on environmental context. Instead of using fixed selection criteria, the system adjusts which parameters are most important for selection based on additional information (such as prioritizing orientation when garage orientation is specified, or prioritizing occupancy status when parking space availability is uncertain), simplifying the selection process while maintaining comprehensive scenario coverage.
3Measurement precision
If the system incorporates additional information such as orientation and occupancy status, then the precision of vehicle positioning is improved, but the amount of data processing required increases
Solution Approach 1:
The system pre-processes and stores trajectories with associated metadata including orientation requirements and occupancy status preferences. Before execution, the system quickly filters trajectories based on additional information without requiring complex real-time analysis, reducing energy consumption while maintaining high positioning precision through pre-computed trajectory characteristics.
Solution Approach 2:
The system extracts only the relevant additional information needed for trajectory selection (such as key orientation angles or occupancy status) rather than processing all available environmental data. This selective extraction approach maintains high positioning precision by focusing on critical parameters while minimizing data processing energy requirements by ignoring redundant information.
Data Source
AI summary
A method for carrying out an automatic drive of a vehicle along a provided trajectory which provides at least one stored trajectory for a current position of the vehicle, selects one of the provided trajectories, and carries out an automatic drive of the vehicle. A control of the vehicle provides signals for a transverse control and a longitudinal control to control the vehicle along the selected trajectory based on detected surroundings data. At least one piece of additional information is detected or received, and the selection process is additionally carried out based on the at least one piece of additional information. Also disclosed is a corresponding device.