Vehicle Speed Control via Yaw Angle Prediction
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Solution Overview
Problem
Existing driving support systems require highly accurate map data to determine safe vehicle speeds for navigating curves, which can be impractical and unreliable.
Innovation Solution
A driving support apparatus that uses a combination of GPS, speed, and direction sensors to predict vehicle position and direction changes, deriving a target speed based on yaw angles without relying on curve curvature data from map databases.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If highly accurate map data is used to obtain curve curvature, then the precision of target speed derivation is improved, but the complexity and cost of the system increases
Solution Approach 1:
The patent introduces an intermediary approach by using predicted position and direction data as intermediate steps to derive curvature information without directly relying on high-precision map data. The system calculates predicted position based on current position and speed, then derives predicted direction from the predicted position, ultimately obtaining yaw angle as a mediator to determine target speed.
Solution Approach 2:
The patent replaces the mechanical/system-dependent approach of using high-accuracy map data with a computational method based on sensor data processing. Instead of relying on external map databases, the system substitutes a calculation-based approach using GPS position data, speed data, and direction data to derive the necessary curvature information through mathematical predictions.
2Reliability
If map data is used to determine curve curvature, then the reliability of speed control is improved, but the system becomes dependent on external data sources
Solution Approach 1:
The patent implements self-service by enabling the vehicle system to generate its own curvature information internally using its sensors and computational units. The system serves itself by deriving predicted position, predicted direction, and yaw angle from its own operational data (position sensor, speed sensor, direction sensor) without external assistance from map databases, thereby achieving independence while maintaining reliability.
3Measurement precision
If curve curvature from map data is used, then the accuracy of target speed derivation is improved, but the ease of operation decreases due to data processing complexity
Solution Approach 1:
The patent segments the complex task of curvature determination into distinct modular steps: acquiring position data, calculating predicted position, deriving predicted direction from predicted position, obtaining direction data, and finally calculating yaw angle. This segmentation allows each step to be processed independently with clear input-output relationships, simplifying the overall operation while maintaining accuracy.
Data Source
AI summary
A driving support apparatus derives position prediction data when T=Tn, which is when a predicted time Tn has elapsed from the present time, based on acquired map data, position data and speed data. Direction prediction data indicating a traveling direction of a vehicle 1 at the time T=Tn is derived based on acquired map data and derived position prediction data. A yaw angle which is an angle formed by a traveling direction D0 represented by e direction data and the traveling direction Dn represented by direction prediction data is derived. A target speed Vn of the vehicle is then derived.