Adaptive Headlamp Recognition Sensitivity for Light Distribution Switching
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Solution Overview
Problem
Existing headlamp control systems face issues with inappropriate light distribution switching due to misidentification or failure to recognize forward vehicles with lit lamps, leading to driver dissatisfaction and potential safety hazards.
Innovation Solution
A headlamp control device that adjusts recognition sensitivity based on learning results from probe vehicles, using vehicle position and camera images to optimize light distribution switching between high-beam, shielded high-beam, and low-beam states, incorporating steering angle and speed thresholds to ensure appropriate control.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Extent of automation
If learning results are used to directly control headlamp states, then automation is improved, but reliability deteriorates due to inappropriate control when misidentification occurs
Solution Approach 1:
The patent introduces a human-in-the-loop verification mechanism where the driver's manual operations serve as an intermediary check between the automated recognition system and the final control output. When the recognition unit identifies a forward vehicle, the system waits for driver confirmation through manual headlamp operation before executing control, thereby preventing inappropriate automated control while maintaining automation benefits
Solution Approach 2:
The system implements feedback by monitoring driver manual operations and using them to verify or correct automated recognition results. The driver's manual headlamp control actions provide feedback signals that confirm or refute the recognition unit's identification, creating a closed-loop control system that improves reliability while maintaining automation
2Measurement precision
If recognition sensitivity is increased to detect more forward vehicles, then measurement precision is improved, but false identification increases leading to harmful effects
Solution Approach 1:
The patent applies preliminary anti-action by requiring driver verification before executing control based on recognition results. This preliminary check prevents false identification from causing harmful effects by blocking inappropriate control actions before they occur, while still allowing accurate detections to proceed
Solution Approach 2:
The system dynamically adjusts the control execution based on real-time driver behavior. When driver manual operations confirm the recognition result, control is executed; when they contradict, control is blocked. This dynamic adaptation allows high sensitivity operation without fixed thresholds, preventing false identification harm while maintaining detection precision
3Reliability
If the system blocks control based on driver manual operations, then reliability is improved, but productivity deteriorates due to delayed response
Solution Approach 1:
The patent applies partial action by selectively blocking control only when driver manual operations contradict recognition results, rather than blocking all automated control. This partial verification approach maintains high reliability for critical decisions while minimizing delays for routine situations, balancing reliability and productivity
Data Source
AI summary
The headlamp control device acquires a result of learning of the recognition sensitivity of the forward vehicle during lighting of the lamp included in the forward camera image of the probe vehicle suitable for each traveling position of the probe vehicle performed using the traveling position of the probe vehicle and the forward camera image of the probe vehicle when the driver of the probe vehicle performs an operation to turn OFF the function of switching the light distribution of the headlamp of the probe vehicle from the lighting state of the high beam to the lighting state of the light shielding high beam, and adjusts the recognition sensitivity of the forward vehicle during lighting of the lamp included in the forward camera image of the own vehicle at each traveling position of the own vehicle based on the obtained result of the learning.