Adaptive Path Estimation for ADAS Object Detection
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Solution Overview
Problem
Advanced driver assistance systems (ADAS) face challenges in accurately determining if a detected object is in a vehicle's path, especially when the driver alters the path unexpectedly or objects move unexpectedly, requiring improved object detection strategies to prevent collisions.
Innovation Solution
A method for ADAS that selects a path estimation methodology based on the distance between the vehicle and the object, using either a yaw-rate only prediction or a combination of yaw-rate and additional sensor outputs like lane detection, radar/LIDAR, and GPS, to determine the vehicle's path and trigger appropriate responses.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If a single path estimation methodology is used, then the system complexity is reduced, but the accuracy and adaptability of in-path detection deteriorates when driving conditions change
Solution Approach 1:
The system dynamically switches between different path estimation methodologies (first methodology using yaw rate only, second methodology using additional sensors) based on real-time driving conditions and object distances, making the system adaptable without permanently complex architecture
Solution Approach 2:
The system changes operational parameters by selecting different estimation methodologies based on thresholds for object distance and rate of change of path angle, allowing adaptation to varying driving conditions while maintaining manageable system complexity
2Measurement precision
If multiple path estimation methodologies are maintained ready, then the accuracy and adaptability of in-path detection improves, but the computational load and system complexity increases
Solution Approach 1:
The path estimation problem is segmented into different operational regimes (close objects vs. distant objects, high rate of change vs. low rate of change), with each regime handled by a specialized methodology, improving precision without requiring all methodologies to operate simultaneously
Solution Approach 2:
The system dynamically selects which methodology to apply based on real-time conditions, maintaining multiple methodologies in the system architecture but activating only one at a time, thus improving measurement precision while controlling computational load
3Speed
If path estimation uses only yaw rate data, then the system response speed increases, but the accuracy of path estimation deteriorates for distant objects
Solution Approach 1:
The system dynamically adjusts the data fusion strategy based on object distance and rate of change of path angle, using yaw rate only for immediate responses and incorporating additional sensors for more accurate long-range estimation
Data Source
AI summary
A method for operating an advanced driver assistance systems (ADAS) includes determining a distance between an object and a vehicle, selecting a path estimation methodology from multiple path estimation methodologies based at least in part on a distance between the vehicle and the object, and activating at least one ADAS action in response to determining that the object intersects the estimated path.