On-ramp Detection Using Yaw Rate and Camera Sensors
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Solution Overview
Problem
Existing driver assistance systems face challenges in adapting lane change assistance functions when transitioning from one road to another, particularly when entering a freeway on-ramp, due to the need for additional costly information sources like electronic horizons.
Innovation Solution
A method and apparatus that utilize existing vehicle sensors to determine if a vehicle is on an on-ramp by detecting curves, road boundaries, and traffic conditions, allowing for adaptive lane change assistance without additional costs, by using sensors like yaw rate, camera, and GPS to assess the vehicle's position and traffic angles.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If an electronic horizon is used to determine on-ramp position for adapting lane change assistance, then the adaptability of the driver assistance system is improved, but the system cost increases
Solution Approach 1:
The patent extracts the essential detection functions from the expensive electronic horizon system and implements them using existing vehicle sensors. By taking out only the necessary detection capabilities (curve detection, road boundary detection, traffic detection) and implementing them with sensors already present in the vehicle, the system achieves on-ramp position determination without the additional cost of an electronic horizon.
Solution Approach 2:
The patent makes existing vehicle sensors perform multiple functions. The yaw rate sensor, originally designed for stability control, is used for curve detection to determine on-ramp position. The camera and GPS serve both their original navigation purposes and the additional function of detecting road boundaries and traffic conditions. This multi-functionality eliminates the need for dedicated expensive hardware.
2Device complexity
If existing sensors are used to determine on-ramp position, then the system cost is reduced, but the measurement precision may be insufficient
Solution Approach 1:
The patent combines multiple sensor data sources (yaw rate sensor, camera, GPS) to determine on-ramp position. By merging the information from these different sensors, the system compensates for the individual limitations of each sensor and achieves sufficient measurement precision through data fusion, eliminating the need for expensive dedicated detection hardware.
Solution Approach 2:
The system continuously monitors multiple parameters (curve detection, road boundary detection, parallel traffic detection, tangential traffic detection) and uses feedback from these detections to confirm on-ramp position. This multi-parameter feedback approach increases measurement reliability and precision by cross-validating signals from different sensors.
Data Source
AI summary
It is determined that (a) a first vehicle is driving on a curve on a first road or, (b) by detecting a second road, a road boundary of the first road. It is further determined that there is (c) parallel traffic by determining that a second vehicle is moving in a lane of the second road adjacent to the first road, or (d) tangential traffic by detecting a third vehicle is moving tangentially to the curve. Upon determining that there is simultaneously at least one of (a) the first vehicle driving on the curve on the first road or (b) the road boundary of the first road, and at least one of (c) parallel traffic or (d) tangential traffic, then it is determined that the first vehicle is driving on an on-ramp.