Traffic Light Selection Control on Curved Roads
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Solution Overview
Problem
Existing vehicle control systems struggle to accurately recognize the target traffic light to obey while considering the form of the road and handle multiple traffic lights in an image effectively.
Innovation Solution
A vehicle control apparatus that sets a traffic light detection area based on the road form, using road shape information and turn radius, and selects the target traffic light by considering road curvature and lane directions, with additional criteria for traffic light selection.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If a fixed detection area is used for traffic light recognition, then the system structure is simple, but the system cannot adapt to varying road forms and accurately identify target traffic lights
Solution Approach 1:
The detection area is dynamically adjusted based on road form characteristics. The system calculates turn radius from vehicle motion data and uses it to adaptively define the detection area boundaries, allowing the detection region to expand or contract according to the curvature of the road ahead.
Solution Approach 2:
The system changes the detection area parameters (position, size, shape) based on detected road form parameters. By calculating turn radius and road curvature, the system modifies the detection area geometry to match the road configuration, improving traffic light detection accuracy on curved roads.
2Measurement precision
If multiple traffic lights are detected in the image, then more traffic light information is available, but it becomes difficult to identify the correct target traffic light to obey
Solution Approach 1:
The system applies different selection criteria to different regions within the detection area. Traffic lights are evaluated based on their spatial relationship to the vehicle and road geometry, with priority given to lights in specific zones (e.g., lights aligned with the vehicle's path, lights at appropriate distances from the intersection).
Solution Approach 2:
The system performs preliminary filtering of detected traffic lights based on geometric criteria before final selection. By pre-processing the list of detected lights using road form and vehicle state information, the system reduces the candidate set and makes target identification more reliable.
3Adaptability or versatility
If the detection area is expanded to cover more road forms, then more traffic lights can be detected, but the processing complexity and computational load increase
Solution Approach 1:
The detection area is segmented into multiple regions based on road form characteristics. Instead of processing a single large area uniformly, the system divides the detection space into zones (e.g., near field, far field, left curve zone, right curve zone) and applies region-specific detection and selection criteria, improving efficiency.
Data Source
AI summary
A vehicle control apparatus recognizes traffic conditions ahead of an own vehicle based on an image captured by a camera that captures an area ahead of the own vehicle. The vehicle control apparatus includes a detection area setting unit and a selecting unit. The detection area setting unit sets a traffic light detection area in which a traffic light present ahead of the own vehicle is detected in the image, based on a form of a road. The selecting unit that selects a target traffic light to be obeyed by the own vehicle from traffic lights detected in the traffic light detection area.