Autonomous Vehicle Yellow-Light Timing for Red-Light Prediction
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Solution Overview
Problem
Autonomous vehicles face challenges in consistently determining yellow light durations at traffic lights due to inconsistencies in how these durations are set across different locations, leading to potential red light violations or abrupt braking.
Innovation Solution
Store a plurality of default yellow light durations based on different possible transitions for each traffic light, using human labeling or onboard detection systems to determine these durations, and utilize a pre-stored table to select the appropriate duration for controlling vehicle behavior.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If autonomous vehicles use a single fixed yellow light duration for all traffic lights, then the system is simple to implement, but it leads to red light violations or abrupt braking due to inconsistencies in actual yellow light durations across different locations
Solution Approach 1:
The patent segments the yellow light duration into multiple discrete options (e.g., 3 seconds, 4 seconds, 5 seconds) stored in a table, rather than using a single fixed duration. This segmentation allows the system to select the most appropriate duration based on specific traffic light conditions, improving prediction accuracy while maintaining manageable system complexity through structured data organization
Solution Approach 2:
The patent performs preliminary action by pre-storing multiple yellow light durations in a table during system setup or previous operations. This pre-computation and storage of duration values eliminates the need for real-time calculation during critical driving moments, allowing the system to quickly retrieve and select the appropriate duration when needed, thus improving response reliability
2Reliability
If autonomous vehicles store multiple default yellow light durations based on different transitions, then the prediction accuracy improves, but the data storage and selection process becomes more complex
Solution Approach 1:
The patent applies local quality by associating different yellow light durations with specific traffic light transition types (e.g., green to yellow, yellow to red). Each duration value is tailored to its specific transition context, ensuring that the most appropriate duration is selected for each situation. This targeted approach improves consistency in navigation while keeping the data structure organized and manageable through context-specific categorization
3Adaptability or versatility
If the system uses human labeling or onboard detection to determine yellow light durations, then the adaptability to different locations improves, but the time and resources required for data collection increase
Solution Approach 1:
The patent performs preliminary action by collecting and storing yellow light duration data through human labeling or onboard detection during non-critical periods, building a comprehensive duration table in advance. This pre-collection and storage of location-specific duration information enables the system to quickly retrieve pre-determined values during actual driving operations, achieving high adaptability to different locations while minimizing time loss during critical decision-making moments
Data Source
AI summary
Aspects of the disclosure relate to controlling a vehicle having an autonomous driving mode. For instance, a current state of a traffic light may be determined. One of a plurality of yellow light durations may be selected based on the current state of the traffic light. When the traffic light will turn red may be predicted based on the selected one. The prediction may be used to control the vehicle in the autonomous driving mode.