Autonomous Vehicle Control for Constructive Crosswalk Detection
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Solution Overview
Problem
Autonomous vehicles face challenges in navigating pedestrian activity outside designated crosswalks, leading to a higher risk of collisions, as they are not equipped to handle unpredictable pedestrian crossing patterns at locations without marked crosswalks.
Innovation Solution
A system and method that utilize data analysis and machine learning, specifically through a trained neural network, to identify 'constructive crosswalks' – locations where pedestrians are likely to cross outside of designated crosswalks, allowing autonomous vehicles to adjust their driving behavior and operation accordingly.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If autonomous vehicles rely only on marked crosswalks for pedestrian detection, then the device complexity is reduced, but the reliability of pedestrian safety is worsened due to unpredictable pedestrian crossing patterns outside crosswalks
Solution Approach 1:
The system performs preliminary analysis of historical pedestrian data to identify constructive crosswalk locations before the autonomous vehicle encounters them. This advance preparation allows the vehicle to have predictive information about likely pedestrian crossing areas, improving safety without requiring complex real-time detection systems.
Solution Approach 2:
The patent introduces an intermediary layer between the autonomous vehicle and pedestrians by identifying constructive crosswalks as intermediate zones. These zones serve as predictive indicators of pedestrian presence, allowing the vehicle to adjust behavior in advance without directly detecting pedestrians or implementing complex real-time monitoring.
2Reliability
If autonomous vehicles monitor all road locations for pedestrian activity, then the reliability of collision avoidance is improved, but the loss of computational resources and time increases
Solution Approach 1:
The system applies local quality by focusing computational resources only on specific locations identified as constructive crosswalks, rather than uniformly monitoring all road areas. This selective approach concentrates processing power where pedestrian activity is most likely, reducing overall computational burden while maintaining high collision avoidance reliability.
Solution Approach 2:
Historical pedestrian data is analyzed in advance to pre-identify constructive crosswalk locations, so that when the autonomous vehicle approaches these areas, the system already has targeted information. This eliminates the need for exhaustive real-time scanning of all road locations, significantly reducing processing time while maintaining reliable collision avoidance.
3Adaptability or versatility
If autonomous vehicles use traditional map data with only marked crosswalks, then the ease of operation is maintained, but the adaptability to real-world pedestrian behavior is reduced
Solution Approach 1:
The patent introduces constructive crosswalks as an intermediary concept that bridges traditional map data and actual pedestrian behavior. These constructive crosswalks are derived from historical data and serve as predictive markers, allowing the system to adapt to real-world pedestrian patterns without requiring complex real-time analysis or complete restructuring of the mapping system.
Solution Approach 2:
The system performs preliminary processing of historical pedestrian data to generate constructive crosswalk information that can be integrated with traditional map data. This advance preparation creates an enhanced data structure that maintains compatibility with existing systems while adding adaptive capabilities for predicting pedestrian behavior at unmarked locations.
Data Source
AI summary
A method includes receiving data relating to pedestrian activity at one or more locations outside of a crosswalk, analyzing the data, based on the data, identifying at least one location of the one or more locations as a constructive crosswalk, and controlling operation of an autonomous vehicle based on the at least one location of the constructive crosswalk.