Driver Assistance Collision Path Detection at Intersections
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Solution Overview
Problem
Existing advanced driver assistance systems (ADAS) face challenges in accurately determining the risk of collision with obstacles, particularly in dynamic environments like city intersections, leading to increased errors and degraded response performance due to varying obstacle movements.
Innovation Solution
A driver assistance system that utilizes a camera and obstacle detector to recognize obstacles, determine the rate of change in obstacle size and relative bearing, and calculates time-to-collision on a two-dimensional plane, providing collision risk warnings and controlling vehicle speed and direction as necessary.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Device complexity
If existing ADAS assumes constant vehicle speed and acceleration to predict time-to-collision, then the calculation is simple, but the accuracy of collision risk determination deteriorates in dynamic environments with moving obstacles
Solution Approach 1:
The patent applies dynamics by transitioning from static assumptions (constant speed and acceleration) to dynamic modeling that accounts for obstacle movements. The system now considers varying speeds and acceleration patterns of both the vehicle and obstacles, enabling accurate collision risk assessment in dynamic environments while maintaining computational feasibility through structured calculation approaches.
2Productivity
If the system only uses distance and speed information for collision prediction, then the processing is fast, but the reliability of collision risk assessment deteriorates when obstacles perform various movements
Solution Approach 1:
The system applies preliminary action by pre-calculating and storing movement patterns, speed variations, and acceleration profiles of obstacles before collision assessment. This allows the system to quickly retrieve and apply relevant parameters during real-time operation, maintaining fast response while improving reliability through comprehensive pre-prepared data on obstacle behaviors.
Solution Approach 2:
The patent implements feedback mechanisms where the system continuously monitors actual obstacle movements and compares them with predicted patterns. This feedback loop enables the system to adjust its collision risk assessment in real-time, improving reliability by adapting to actual obstacle behaviors while maintaining responsive performance through efficient update cycles.
3Device complexity
If the system determines collision risk based on predicted TTC only, then the processing is straightforward, but the accuracy of collision detection deteriorates in complex environments like crosswalks and intersections
Solution Approach 1:
The patent applies segmentation by dividing the collision detection process into distinct stages: obstacle detection, movement pattern analysis, time-to-collision calculation, and risk assessment. This segmented approach allows the system to handle complex environments systematically, improving detection accuracy while maintaining manageable processing complexity through modular computation.
Solution Approach 2:
The system transitions from one-dimensional distance-based assessment to multi-dimensional analysis by incorporating spatial position, speed, acceleration, and temporal factors. This dimensional expansion enables accurate collision detection in complex environments like crosswalks and intersections, where obstacles move in various directions and patterns, while maintaining computational efficiency through structured mathematical modeling.
Data Source
AI summary
Disclosed herein are a driver assistance system and a vehicle including the same. The driver assistance system of the present disclosure includes a camera, an obstacle detector configured to detect an obstacle and output obstacle information about the detected obstacle, and a processor configured to recognize an image of the obstacle based on image information acquired by the camera, obtain a rate of change in size of the recognized image of the obstacle, obtain relative bearing information of the obstacle based on the obstacle information detected by the obstacle detector, determine whether the obstacle and a vehicle are present on a collision path based on the obtained rate of change in size of the image of the obstacle and the relative bearing information of the obstacle, and upon determining that the vehicle and the obstacle are present on the collision path, output a collision risk warning.