Vehicle Light Detection Confidence for Adaptive Control
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Solution Overview
Problem
Current vehicles, both manual and autonomous, do not effectively utilize information from the lights of other vehicles to anticipate road conditions or adjust their behavior, particularly failing to consider the status of other vehicles' lights and not anticipating vacated parking spots.
Innovation Solution
The system employs vision sensors, such as cameras, to determine the confidence level of other vehicles' lights being ON or OFF, using this information to activate alarms, redistribute computational resources, update trajectory costs, and adjust vehicle speed and distance based on the confidence level and light status of other vehicles.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If the vehicle uses vision sensors to detect and analyze other vehicles' lights to improve situational awareness and safety, then the reliability and warning capability are improved, but the device complexity and computational resource requirements increase
Solution Approach 1:
The system performs preliminary detection of light status (ON/OFF) of other vehicles using vision sensors before making driving decisions. By continuously monitoring and analyzing the light states of surrounding vehicles in advance, the system can anticipate potential road conditions and adjust its behavior proactively, improving reliability without requiring complex real-time reactions
Solution Approach 2:
The patent introduces an intermediary processing layer that analyzes light status information from vision sensors and translates it into actionable warnings and control signals. This intermediary system processes the raw visual data through confidence level calculations and threshold comparisons, mediating between the simple light detection and the complex vehicle control decisions, thereby managing system complexity while maintaining high reliability
2Measurement precision
If the vehicle continuously monitors and analyzes light status of other vehicles with high confidence levels, then the measurement precision and detection accuracy are improved, but the loss of time and computational resources increase
Solution Approach 1:
The system applies partial monitoring by focusing computational resources only on vehicles where light status detection is critical. Instead of uniformly analyzing all detected vehicles with equal precision, the system selectively intensifies analysis for specific targets based on relevance to the subject vehicle's path and behavior, achieving high measurement precision where needed while minimizing overall processing time and resource consumption
Solution Approach 2:
The system implements periodic monitoring of light status with variable intervals. Rather than continuously analyzing every vehicle at maximum precision, the system periodically updates light status detection at appropriate intervals, adjusting the frequency based on the vehicle's motion state and relevance. This periodic approach maintains measurement precision for critical decisions while significantly reducing average processing time and computational load
3Adaptability or versatility
If the autonomous vehicle adjusts its motion and trajectory based on detected light status and confidence levels, then the adaptability and safety are improved, but the device complexity and control system complexity increase
Solution Approach 1:
The system implements dynamic adjustment of vehicle motion and trajectory based on real-time light status detection and confidence levels. The control parameters (such as trajectory cost, maximum allowed speed, minimum allowed distance) are made dynamic and adapt to the detected environmental conditions. This allows the autonomous vehicle to exhibit adaptable driving behavior that responds to the presence and light status of other vehicles, improving safety without requiring a completely complex control architecture
Solution Approach 2:
The patent employs parameter changes in the control system by modifying key motion parameters (trajectory cost, speed limits, distance constraints) based on detected light status and confidence levels. When another vehicle's lights are detected with high confidence, the system changes control parameters to increase caution; when confidence is low or lights are OFF, parameters are adjusted for more efficient travel. This parameter-based adaptation provides versatile driving behavior while maintaining manageable control system complexity through standardized parameter adjustment mechanisms
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 enhances the ability of vehicles to provide warning cues to operators and adjust their motion dynamically, improving safety and efficiency by considering the status of other vehicles' lights, thereby enhancing situational awareness and adaptive driving behaviors.
Implementation Method 1
determining, via a controller, a confidence level that the light of the other vehicle is ON based on images captured by a camera of the operating vehicle
Data Source
AI summary
A method for controlling an operating vehicle includes: (a) determining, via a controller, a confidence level that the light of the other vehicle is ON based on images captured by a camera of the operating vehicle; and (b) controlling, via the controller, an alarm of the operating vehicle based on the confidence level that the light of the other vehicle is ON.