Cluster-Based Stopping Point Determination for Autonomous Vehicles
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Current methods for determining stopping points for autonomous vehicles are environment-dependent and may not align with human driver behavior, leading to impractical or unsafe stopping positions.
Innovation Solution
Determine a swarm-based stopping point by analyzing the distribution of individual stopping points from multiple manually driven vehicles, which are then used to guide autonomous vehicles to a position that aligns with human driver behavior and legal requirements, incorporating both x-axis and y-axis distributions for accurate positioning.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If stopping points are determined based on detected stopping lines and predefined tolerances, then autonomous vehicles can stop at consistent positions, but the stopping points may not align with human driver behavior and may be impractical or unsafe
Solution Approach 1:
The patent copies human driver behavior by collecting and analyzing stopping point data from multiple manually driven vehicles. Instead of relying on theoretical calculations, the system creates a swarm-based stopping point that replicates how human drivers actually stop, making autonomous stopping behavior align with human expectations and practical needs
Solution Approach 2:
The system uses feedback from observed human driver stopping behaviors to continuously improve the determination of appropriate stopping points. By analyzing where human drivers actually stop and using this information to adjust swarm-based stopping points, the system learns from real-world outcomes to enhance both precision and practicality
2Device complexity
If stopping points are determined using camera detection of stopping lines, then the method is simple and fast, but the stopping points are highly dependent on environment and may not account for complex traffic situations
Solution Approach 1:
The patent merges multiple data sources including camera detection of stopping lines, swarm-based analysis of human driver behavior, and map data to determine optimal stopping points. This combination allows the system to maintain the simplicity of camera-based detection while adding the adaptability of multi-source data integration to handle complex traffic situations
Solution Approach 2:
The system creates a universal stopping point determination method that works across diverse environments by combining multiple detection approaches. The swarm-based stopping point serves as a universal solution that adapts to different traffic situations, road types, and environmental conditions while maintaining a relatively simple overall architecture
3Adaptability or versatility
If individual vehicles determine stopping points independently based on their environment, then each vehicle can adapt to local conditions, but there is no consistency across the vehicle swarm and missed opportunities for collective learning
Solution Approach 1:
The patent segments the stopping point determination process into two parts: individual vehicles still detect local stopping lines and environmental features independently, while simultaneously contributing their stopping point data to a centralized swarm analysis system. This segmentation allows both local adaptation and collective learning to occur in parallel without interfering with each other
Solution Approach 2:
The system introduces a backend server as an intermediary that collects stopping point data from multiple vehicles, analyzes human driver behavior patterns, and returns swarm-based stopping points to individual vehicles. This intermediary enables information exchange and collective learning while preserving the independence and local adaptability of individual vehicle operations
Data Source
AI summary
Technologies and techniques for determining a cluster-based stopping point for a motor vehicle for a predefined reason for stopping in a lane of a road. The individual stopping points of a plurality of vehicles may be determined for the reason for stopping in the lane, wherein the vehicles are controlled by individual drivers. A distribution of the individual stopping points in the lane at least in the direction of travel of the vehicles may be determined. The maximum of the distribution may be determined and stored as a cluster-based stopping point. The cluster-based stopping point determined in this way may be applied to autonomous driving technologies.