Driver Assistance System Curved Path Collision Risk
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Solution Overview
Problem
Existing driver assistance systems struggle to accurately estimate collision risks when a vehicle is driving on a curved path, as they do not consider the expected driving path, leading to anomalous factors in collision risk estimation.
Innovation Solution
A driver assistance system that includes a radar and a controller to detect other vehicles and calculate collision risks by generating regions of interest along the expected driving path, using a first region partitioned according to the vehicle's steering angle and a second region transformed after a pre-selected time, with a camera providing additional position information.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If a driver assistance system detects obstacles without considering the expected driving path, then the detection process is simple, but the collision risk estimation becomes inaccurate on curved paths
Solution Approach 1:
The detection space is segmented into multiple regions of interest (first ROI and second ROI) along the expected driving path. Each region is processed separately with the first ROI being partitioned according to steering angle and the second ROI being generated after vehicle movement transformation. This segmentation allows the system to focus computational resources on relevant areas while maintaining accurate collision risk estimation on curved paths.
2Measurement precision
If the system generates regions of interest along the expected driving path considering steering angle, then collision risk estimation improves, but computation processing time increases
Solution Approach 1:
The system extracts only the necessary regions of interest along the expected driving path rather than processing the entire detection space. By taking out only the relevant first ROI and second ROI that align with the vehicle's expected path, the computation is significantly reduced while maintaining accurate collision risk estimation. This extraction approach processes only critical areas where collisions are most likely to occur.
3Reliability
If the system uses a first region of interest partitioned by steering angle and transforms it to a second region after vehicle movement, then collision detection accuracy on curved paths improves, but the system complexity increases
Solution Approach 1:
The system dynamically adjusts the regions of interest based on the vehicle's expected driving path and movement. The first ROI is partitioned according to the current steering angle, and the second ROI is generated by applying movement transformation after the vehicle moves along the expected path. This dynamic adaptation ensures the detection regions always align with the vehicle's trajectory, improving collision detection reliability on curved paths while managing system complexity through algorithmic rather than hardware complexity.
Applied Scientific Principles
This section explains which scientific principles are used to turn an abstract innovation direction into a practical engineering solution.
Function Achieved in This Case
This approach allows for precise collision risk estimation and reduced computation processing, enhancing the reliability of collision detection and warning systems, especially on curved paths.
Implementation Method 1
a radar installed in a vehicle to detect other vehicle driving outside of the vehicle, and configured to acquire radar data comprising position information of the other vehicle
Data Source
AI summary
Disclosed herein is a driver assistance system and a control method thereof. The driver assistance system includes a radar installed in a vehicle to detect other vehicle driving outside of the vehicle, and configured to acquire radar data comprising position information of the other vehicle, and a controller configured to calculate a risk of collision based on a relative distance of the other vehicle with respect to the vehicle. The controller generates a first region of interest partitioned along an expected driving path of the vehicle, generates a second region of interest during the vehicle moves along the expected driving path of the vehicle, when other vehicle is detected in the first region of interest, and calculates a relative distance of the other vehicle detected in the second region of interest.