Intersection-Aware Sensor Fusion Weighting for Collision Detection
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Solution Overview
Problem
Existing vehicle collision detection systems using sensor fusion do not effectively differentiate between the relevance and reliability of sensor information in varying intersection scenes, leading to potential false alarms or missed collisions.
Innovation Solution
A method that identifies specific intersections and adjusts the weight of information sources based on real-time conditions, such as ambient light and obstructed views, to prioritize the most relevant sensors for each collision potential scenario, incorporating driver intention and real-time data quality.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If sensor fusion uses information from every sensor equally, then all available sensor data is processed, but the accuracy of collision detection decreases due to irrelevant or unreliable information
Solution Approach 1:
The patent applies local quality by assigning different weights to different information sources based on their relevance and reliability for specific collision scenarios. Each sensor's contribution is locally optimized rather than uniformly applied, with weights adjusted according to the specific area and collision potential being evaluated.
Solution Approach 2:
The system dynamically changes the weight parameters of information sources based on real-time conditions such as ambient light intensity and view obstruction. This parameter adjustment allows the sensor fusion to adapt to varying environmental conditions and prioritize the most relevant data sources for each situation.
2Reliability
If all sensor information sources are processed with equal weight, then the system is simple to implement, but the reliability of collision detection decreases in varying environmental conditions
Solution Approach 1:
The patent implements dynamics by making the information source weights adjustable and adaptable rather than fixed. The weights are dynamically modified based on real-time environmental conditions and the specific collision scenario being evaluated, allowing the system to respond flexibly to changing circumstances.
Solution Approach 2:
The sensor fusion system performs self-service by automatically evaluating and adjusting the weights of information sources based on the specific collision scenario and environmental conditions. The system self-regulates which sensors to trust more without requiring manual intervention or complex external control.
3Measurement precision
If the system processes all collision potential scenarios for all paths through an intersection, then comprehensive coverage is achieved, but the processing time and computational load increase
Solution Approach 1:
The patent applies segmentation by dividing the intersection into multiple paths and further segmenting each path into specific collision potential scenarios. This allows the system to process and evaluate each segment independently, focusing computational resources on relevant areas rather than treating the entire intersection as a single unit.
Solution Approach 2:
The system performs partial action by evaluating only the collision potential scenarios that are relevant to the vehicle's current path and driver intention. Rather than processing all possible scenarios for all paths, the system focuses on the subset of scenarios that pose actual risk, reducing unnecessary computational overhead.
4Measurement precision
If the system adjusts weights based on real-time conditions, then the accuracy of information prioritization improves, but the complexity of weight adjustment increases
Solution Approach 1:
The patent implements feedback by continuously monitoring real-time conditions such as ambient light intensity and view obstruction, and using this information to adjust the weights of information sources. The system feeds back the environmental state to the weight adjustment mechanism, creating a closed-loop system that adapts to changing conditions.
Data Source
AI summary
Methods and systems to implement sensor fusion to determine collision potential for a vehicle include identifying a specific intersection that the vehicle is approaching, and identifying collision potential scenarios associated with one or more paths through the specific intersection. Each collision potential scenario defines a risk of a collision between the vehicle and an object in a specified area. A weight with which one or more information sources of the vehicle are considered is adjusted for each collision potential scenario such that a highest weight is given to one or more of the one or more information sources that provide most relevant and reliable information about the specified area. Sensor fusion is implemented based on the adjusting the weight of the one or more information sources and performing detection based on the sensor fusion, and an alert is provided or actions are implemented according to the detection.