High Beam Control Using Object Detection Confidence
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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 may not necessarily indicate the presence of other vehicles.
Innovation Solution
The system determines the occurrence of predetermined situations using sensor data and detection confidence levels, automatically transitioning from high beam to low beam headlights based on detection of moving vehicles, pedestrians, cyclists, and other objects within the vehicle's range and field of view, utilizing a perception subsystem and machine learning models to reduce false activations and deactivations.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If conventional techniques detect bright light sources to control high beam headlights, then the system can identify potential hazards, but it produces false positives and false negatives leading to incorrect beam transitions
Solution Approach 1:
The system segments the detection task into multiple specialized detectors (pedestrian detector, cyclist detector, vehicle detector, animal detector, light detector) that each focus on specific types of road users. This segmentation allows each detector to be optimized for its specific target, improving overall detection accuracy and reducing false positives compared to a single general-purpose detector.
Solution Approach 2:
The system introduces an intermediary processing layer that combines detections from multiple specialized detectors and applies confidence threshold logic. This intermediary layer synthesizes information from different detection sources, cross-validates findings, and determines the final high beam control action, thereby improving reliability by reducing false positives and false negatives.
2Reliability
If the system uses multiple detection thresholds and confidence levels, then it reduces false activations, but it increases system complexity
Solution Approach 1:
The system divides the detection logic into separate, specialized detector modules (pedestrian detector, cyclist detector, vehicle detector, animal detector, light detector), each with its own confidence threshold. This segmentation manages complexity by creating modular, independent detection units that can be developed and tuned separately, rather than one monolithic complex system.
Solution Approach 2:
The system performs preliminary detections using multiple specialized detectors before making the final high beam control decision. Each detector independently evaluates its specific target type with its own confidence threshold, and only after all preliminary detections are complete does the system synthesize the results. This preliminary action approach reduces false positives by requiring multiple independent confirmations before triggering beam transitions.
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.