Swarm Trajectory Plausibility Check Using Nearby Vehicle Paths
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Solution Overview
Problem
Existing methods for checking the plausibility of trajectories generated based on swarm data for partially assisted motor vehicles are not sufficiently reliable, especially in situations where lane markers are not detected, and there is a need for a simple and robust method to validate this data for safe operation.
Innovation Solution
A method that uses a detection device, such as a camera or radar sensor, to compare the trajectory generated from swarm data with the actual driving trajectory of nearby vehicles, allowing for deviations within preset threshold values to determine the plausibility of the swarm data, and interpolating between the two trajectories to generate a drivable path.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Extent of automation
If swarm data is used to generate trajectories for partially assisted motor vehicles, then automated operation capability is improved, but reliability of trajectory validation deteriorates when lane markers are not detected
Solution Approach 1:
The patent introduces a detection device as an intermediary to observe actual vehicle trajectories and use these observations as a mediator to validate swarm data. Instead of directly trusting swarm data or lane markers, the system uses detected vehicle positions and trajectories as an intermediate reference to indirectly verify the plausibility of swarm data, resolving the reliability issue in environments without lane markers.
Solution Approach 2:
The system implements feedback by continuously detecting actual vehicle trajectories using detection devices and comparing them against swarm data trajectories. This feedback loop allows the system to validate swarm data in real-time, adjusting trust in the swarm data based on whether observed vehicle behavior matches the predicted swarm trajectories, thereby maintaining reliability without lane markers.
2Reliability
If detection devices are used to validate swarm data by comparing with actual vehicle trajectories, then reliability is improved, but device complexity increases
Solution Approach 1:
The detection device serves multiple functions: it detects lane markers when present, tracks vehicle positions, determines vehicle trajectories, and validates swarm data. By making the detection device multi-functional, the system avoids adding separate dedicated validation hardware, thus improving reliability without proportionally increasing device complexity.
Solution Approach 2:
The system uses the motor vehicles themselves as validation resources. By detecting and tracking existing vehicles on the road, the system leverages the vehicles' own motion patterns to validate swarm data, rather than requiring external validation infrastructure. This self-service approach improves reliability while minimizing additional system complexity.
3Adaptability or versatility
If swarm data is used without lane marker detection, then operation capability in complex environments is improved, but measurement precision of trajectory validation deteriorates
Solution Approach 1:
The patent replaces the mechanical/visual system of lane marker detection with a sensor-based detection system that tracks vehicle positions and infers trajectories. Instead of relying on visual lane markers, the system uses detection devices to measure vehicle positions and calculate trajectories mathematically, maintaining measurement precision in environments where lane markers are absent or indistinct.
Data Source
AI summary
The disclosure relates to a method for checking plausibility of a trajectory generated based on swarm data for a first motor vehicle, which is operated in an at least partially assisted manner. The method includes: receiving the swarm data, detecting a second motor vehicle in a surrounding area of the first motor vehicle by a detection device, determining a driving trajectory of the second motor vehicle, comparing the trajectory generated based on the swarm data with the driving trajectory of the second motor vehicle, and checking the plausibility of the swarm data based on the comparing. The disclosure further relates to a computer-readable medium and to an assistance system.