Vehicle Collision Threat Estimation Using Adaptive Distance Offsets
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Solution Overview
Problem
Vehicle collisions at intersections pose challenges due to the complexity of determining threat assessments, which often require data from multiple sensors and differ between rear-end and crossing-path collisions, making collision mitigation difficult and expensive.
Innovation Solution
A vehicle computer system that collects data, determines distance offsets and thresholds, and actsuates vehicle components based on threat estimations, using a combination of sensors and adaptive threshold functions to quickly assess collision risks in both lateral and longitudinal directions, reducing the number of calculations and enabling faster threat estimation.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If data from multiple sensors are used to determine threat assessment, then measurement precision is improved, but device complexity increases
Solution Approach 1:
The patent segments the threat assessment process into distinct collision scenarios (rear-end collisions and crossing-path collisions), each with specialized calculation methods. This allows the system to use simpler sensor configurations for each specific scenario rather than requiring all sensors to be active simultaneously for all scenarios, thereby reducing overall system complexity while maintaining assessment accuracy.
Solution Approach 2:
The patent creates a universal threat assessment system that can handle multiple collision types (rear-end and crossing-path) using a unified framework. The system uses adaptive threshold functions and distance offset calculations that work across different scenario types, allowing a single sensor system to perform multiple assessment functions without requiring separate specialized systems for each collision type.
2Measurement precision
If different techniques are used for rear-end and crossing-path collisions, then threat assessment accuracy is improved, but device complexity increases
Solution Approach 1:
The patent implements dynamic adaptation of assessment techniques based on the detected collision scenario. The system dynamically switches between rear-end collision algorithms and crossing-path collision algorithms based on real-time sensor data analysis. This dynamic approach allows the system to use the most appropriate simplified method for each scenario rather than maintaining complex capabilities for all scenarios simultaneously.
Solution Approach 2:
The patent changes key parameters (distance offsets and thresholds) based on the collision scenario type. For rear-end collisions, it uses longitudinal distance offsets, while for crossing-path collisions, it uses lateral distance offsets. This parameter adaptation allows the system to simplify calculations by only considering relevant spatial dimensions for each scenario, reducing computational complexity while maintaining accuracy.
3Reliability
If comprehensive threat assessment is performed, then reliability is improved, but loss of time increases
Solution Approach 1:
The patent performs preliminary classification of collision scenarios using adaptive threshold functions before conducting detailed threat assessment. By quickly determining whether a situation represents a rear-end or crossing-path collision risk, the system can then apply the appropriate simplified calculation method, avoiding unnecessary comprehensive calculations and reducing overall computational time while maintaining reliability.
Solution Approach 2:
The patent applies partial assessment action by focusing calculations only on the relevant spatial and temporal parameters for each collision type. For example, it selectively determines distance offsets and thresholds only for the dimensions pertinent to the detected scenario (longitudinal for rear-end, lateral for crossing-path), rather than performing exhaustive analysis of all possible parameters, thus reducing computational burden while maintaining sufficient reliability.
Data Source
AI summary
A distance offset is determined based on a determined time to collision, a relative lateral distance, and a relative longitudinal distance between the target and a host vehicle. A threat estimation is determined based on the distance offset and a distance threshold. A component of the host vehicle are actuated based on the threat estimation.