Autonomous Vehicle Yellow-Light Timing for Traffic Signal Response
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Solution Overview
Problem
Autonomous vehicles face challenges in consistently recognizing and responding to varying yellow light durations at traffic lights, leading to potential red light running or abrupt braking due to inconsistencies in how yellow light durations are set across different locations and the lack of practical methods for real-time communication with traffic lights.
Innovation Solution
The method involves storing multiple default yellow light durations based on different possible transitions for each traffic light, allowing autonomous vehicles to detect the current state and select the appropriate duration from a pre-stored table, and sharing this information with remote computing devices for further analysis and fleet communication.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If autonomous vehicles use a single fixed yellow light duration for all traffic lights, then the control logic is simple, but the vehicle may run red lights or brake abruptly when encountering varying yellow light durations at different locations
Solution Approach 1:
The system dynamically selects yellow light duration based on the detected traffic light state (solid green circle, green arrow, flashing yellow arrow, etc.) rather than using a fixed duration. This allows the control logic to adapt to different traffic light configurations and locations, improving response accuracy while maintaining manageable complexity through state-based selection
Solution Approach 2:
The system changes the yellow light duration parameter based on the observed traffic light state. Different states (solid green, green arrow, flashing yellow arrow) are associated with different yellow light durations from a pre-stored table, allowing the vehicle to adjust to varying durations at different locations without complex real-time communication infrastructure
2Reliability
If autonomous vehicles store multiple yellow light durations for different traffic light states, then the vehicle can respond accurately to varying durations, but the device complexity increases due to storing and selecting from multiple durations
Solution Approach 1:
The system performs preliminary action by pre-storing multiple yellow light durations in a table before operation. Each traffic light state (solid green circle, green arrow, flashing yellow arrow, etc.) is pre-associated with its corresponding yellow light duration. When the vehicle encounters a traffic light, it simply detects the state and retrieves the pre-stored duration, avoiding complex real-time calculations while maintaining high accuracy
Solution Approach 2:
The patent introduces an intermediary element: a pre-stored table that maps traffic light states to yellow light durations. This table acts as a lookup mechanism between the detected traffic light state and the required duration parameter, simplifying the selection process and reducing computational complexity while maintaining reliability
3Ease of manufacture
If autonomous vehicles rely on pre-stored default yellow light durations, then the system is easy to implement, but the vehicle cannot adapt to actual variations in yellow light durations at different locations
Solution Approach 1:
The system transitions from static pre-stored durations to dynamic selection based on observed traffic light states. By detecting the specific state (solid green, green arrow, flashing yellow arrow, etc.) and selecting the corresponding duration from a pre-stored table, the system adapts to location-specific variations while maintaining the simplicity of pre-stored data structures
Solution Approach 2:
The system incorporates feedback by observing the actual traffic light state and using that observation to select the appropriate yellow light duration. The perception system detects the traffic light state, which feeds back into the control system to determine the correct duration from the pre-stored table, enabling adaptation to actual conditions at each location
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.