Automatic High Beam Control Using Tiered Situation Detection
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Solution Overview
Problem
Conventional techniques for controlling high beam headlights in vehicles fail to effectively manage situations, leading to instances of blinding other road users and increased accident risk due to false negatives and false positives, as they rely on detecting bright light sources which can be caused by reflections or not account for vehicles not yet in direct view.
Innovation Solution
A system that analyzes sensor data from a perception subsystem to detect predetermined situations, such as moving vehicles or pedestrians, and adjusts the lighting operation by deactivating high beam headlights and activating low beam headlights based on detection confidence level thresholds, using a tiered approach to prioritize situations and prevent oscillating behavior.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If conventional techniques detect bright light sources to control high beam headlights, then the system can respond to visible threats, but it produces false positives from reflections and false negatives from vehicles not yet in direct view
Solution Approach 1:
The system performs preliminary detection using multiple sensors (cameras, LIDAR, radar) to identify potential situations before they become critical threats. By detecting vehicles, pedestrians, and cyclists earlier in their approach, the system can prepare for high beam adjustment before direct visual contact is established, reducing false negatives while maintaining detection precision.
Solution Approach 2:
The system introduces an intermediary processing layer that analyzes sensor data from multiple sources and correlates it with map information and detected objects. This intermediary analysis distinguishes between actual threats (vehicles, pedestrians) and false positive sources (reflections, stationary objects), improving both detection accuracy and control reliability by filtering spurious signals before triggering high beam adjustments.
2Reliability
If the system automatically adjusts high beam headlights based on detected situations, then safety is enhanced by preventing blinding of other road users, but the system may oscillate between high and low beam states
Solution Approach 1:
The system applies preliminary anti-action by implementing hysteresis logic that prevents immediate reversal of high beam state changes. When a situation triggers a switch from high to low beam, the system requires a different or sustained condition threshold before allowing reversal, preventing oscillation while maintaining safety. This anticipates potential state fluctuations and counteracts them before they manifest as harmful oscillations.
Solution Approach 2:
The system dynamically adjusts the thresholds and response criteria for high beam control based on the specific situation detected. Different object types (pedestrians vs. vehicles), distances, and environmental conditions modify the activation and deactivation thresholds, allowing the system to maintain stability in normal conditions while remaining responsive to critical safety situations. This dynamic adaptation prevents unnecessary oscillations while preserving safety responses.
3Measurement precision
If the system uses a tiered approach with detection confidence level thresholds, then false positives and false negatives are reduced, but the device complexity increases
Solution Approach 1:
The system segments the detection and decision-making process into distinct tiers with different confidence thresholds for different situation types. Critical situations (pedestrians, cyclists) use higher confidence thresholds while less critical situations (distant vehicles) use lower thresholds. This segmentation improves detection precision by matching threshold stringency to situation severity while organizing system complexity into manageable, modular decision layers rather than a monolithic complex system.
Data Source
AI summary
Methods, systems, and non-transitory computer readable media are configured to perform operations comprising determining a plurality of predetermined situations in which lighting operation of an ego vehicle should automatically transition; determining occurrence of a predetermined situation of the plurality of predetermined situations; and causing an automatic transition in lighting operation of the ego vehicle.