Collision Indication Using Yaw Rate and Lateral Velocity Thresholds
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Conventional techniques for determining imminent collisions in vehicles are often inaccurate, leading to false positives and decreased safety in advanced driver assistance systems (ADAS), automatic emergency braking (AEB), and enhanced steering-assist (ESA) systems due to reliance on single sensor inputs or unreliable yaw rate measurements.
Innovation Solution
The method employs an in-path band and lateral movement estimations based on yaw rate and lateral velocity thresholds to generate a collision indication, ensuring the host vehicle and target are likely to be within the in-path band at a future time, thereby improving collision detection accuracy.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If conventional collision detection techniques are used, then the system is simple to operate, but the measurement precision of collision detection is poor leading to false positives
Solution Approach 1:
The patent combines multiple detection parameters (yaw rate, lateral velocity, target position, in-path band) into a unified collision detection framework. By merging these different data sources and analysis methods, the system achieves higher measurement precision in determining imminent collisions while maintaining systematic organization that manages complexity.
Solution Approach 2:
The patent introduces the in-path band concept as an additional dimensional framework for collision assessment. Instead of relying solely on traditional distance and velocity measurements, the system adds a spatial dimension (the in-path band region) to evaluate whether a target is in a collision course, thereby improving detection accuracy through multi-dimensional analysis.
2Reliability
If single sensor inputs are used for collision detection, then the device complexity is low, but the reliability of collision detection deteriorates due to false positives
Solution Approach 1:
The patent merges multiple sensor inputs and data sources including yaw rate sensors, lateral velocity measurements, target position data, and in-path band calculations. By combining these diverse inputs through a unified detection algorithm, the system achieves higher reliability in collision detection while managing the complexity through integrated processing.
Solution Approach 2:
The system employs feedback mechanisms by continuously monitoring multiple parameters (yaw rate, lateral velocity, target position relative to in-path band) and using this feedback to dynamically assess collision risk. This multi-parameter feedback loop enhances reliability by cross-validating detection signals and reducing false positives through corroborating evidence from multiple sources.
Data Source
Figure 1
Figure 2
Figure 3
AI summary
The techniques and systems herein enable collision indication based on yaw rate and lateral velocity thresholds. Specifically, an in-path band is determined for a predicted path of a host vehicle. Responsive to determining that a target is within the in-path band, a lateral movement for the host vehicle at a time is determined based on whether a yaw rate of the host vehicle meets a yaw rate threshold. A lateral movement for the target at the time is also determined based on whether a lateral velocity of the target meets a lateral velocity threshold. A collision indication is generated responsive to determining, based on the lateral movements, that the host vehicle and the target are likely to be within the in-path band at the time. In this way, the collision indication more accurately reflects an imminent collision, thereby increasing safety while also mitigating false-positive events.