Pedestrian Countdown Signal Classification for Crosswalk Invitation Control
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Solution Overview
Problem
Current autonomous vehicles do not process pedestrian countdown signals, limiting their ability to predict and plan around pedestrian actions and safely interact with pedestrians using crosswalks.
Innovation Solution
A computer-implemented method and system that classify states of pedestrian countdown signals using a classifier model, allowing autonomous vehicles to predict pedestrian actions and perform invitation actions to safely interact with pedestrians.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If autonomous vehicles do not process pedestrian countdown signals, then the vehicle system complexity is reduced, but the ability to predict and respond to pedestrian actions is limited
Solution Approach 1:
The system segments the complex task of pedestrian interaction into distinct components: detecting pedestrian countdown signals, classifying signal states (walk/do not walk/countdown), predicting pedestrian actions based on signal state, and executing appropriate invitation actions. This segmentation allows the vehicle to process pedestrian information systematically without overwhelming system complexity.
Solution Approach 2:
The system performs preliminary classification of pedestrian countdown signal states before making driving decisions. By pre-classifying the signal state (walk, do not walk, or countdown with specific time remaining), the vehicle prepares prediction models in advance to anticipate pedestrian actions, enabling more responsive and safe interaction without adding significant complexity to the decision-making process.
2Measurement precision
If autonomous vehicles classify pedestrian countdown signal states, then pedestrian behavior prediction accuracy is improved, but the processing time and computational load increase
Solution Approach 1:
The system applies different processing levels to different signal states: for walk and do not walk states, it uses straightforward classification; for countdown states, it performs more detailed analysis of the remaining time to predict pedestrian urgency. This localized quality adjustment optimizes processing time while maintaining high prediction accuracy for each specific scenario.
Solution Approach 2:
The system changes the parameter of time remaining in the countdown signal to adjust prediction accuracy. By monitoring the countdown time parameter and adjusting the invitation action timing accordingly (e.g., initiating invitation actions earlier when countdown time is low), the system achieves high prediction accuracy without excessive processing delays.
3Reliability
If autonomous vehicles perform invitation actions based on pedestrian countdown signals, then safety of pedestrian interaction is improved, but the complexity of vehicle control systems increases
Solution Approach 1:
The system implements feedback loops where the classified pedestrian countdown signal state continuously informs the prediction model, which in turn guides the invitation action execution. The system monitors the outcome of invitation actions and adjusts subsequent actions based on pedestrian response and changing signal states, thereby improving safety through adaptive control without requiring overly complex systems.
Solution Approach 2:
The invitation control system is designed to be dynamic rather than static. The vehicle adjusts invitation actions (such as honking, lighting, or slowing down) based on the real-time classified signal state and predicted pedestrian behavior. This dynamic approach allows the system to maintain high safety standards by adapting to changing conditions without requiring a permanently complex control architecture.
Data Source
AI summary
Example embodiments relate to pedestrian countdown signal classification to increase pedestrian behavior legibility. An example embodiment includes a method that includes obtaining, by a computing system of a vehicle, a camera image patch. The method further includes determining, by the computing system, using the camera image patch and a pedestrian countdown signal classifier model, a state of a pedestrian countdown signal. The method also includes determining, by the computing system based on the state of the pedestrian countdown signal, a prediction of whether a pedestrian will enter a crosswalk governed by the pedestrian countdown signal. And the method includes, based on the prediction, causing, by the computing system, the vehicle to perform an invitation action that invites a pedestrian to enter the crosswalk.