Pedestrian Intent Recognition for Adversarial Crossing Detection
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Solution Overview
Problem
Current systems fail to effectively detect and classify adversarial behavior directed towards autonomous vehicles, particularly in urban environments where interactions with vulnerable road users are common.
Innovation Solution
A method and system that utilize a pedestrian detection system and an adversarial intent algorithm to identify and classify adversarial behavior. This involves tracking pedestrians, determining their intentions, and analyzing crossing behavior, non-verbal cues, and audible cues to assess potential adversarial actions.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If current pedestrian detection systems are used, then basic pedestrian identification is achieved, but adversarial behavior detection capability is insufficient
Solution Approach 1:
The system segments behavior analysis into multiple independent modules: crossing behavior analysis module, non-verbal cue analysis module, and audible cue analysis module. Each module processes specific aspects of pedestrian behavior separately, then results are integrated to determine adversarial intent. This segmentation allows complex adversarial behavior detection to be achieved through coordinated simple modules, resolving the contradiction between detection accuracy and system complexity.
2Measurement precision
If comprehensive behavior analysis is performed, then adversarial behavior detection accuracy is improved, but processing time increases
Solution Approach 1:
The system performs preliminary classification of pedestrian behavior into standard categories (crossing, waiting, standing) before conducting detailed adversarial behavior analysis. By pre-establishing behavior categories and only applying complex analysis when necessary (e.g., when crossing behavior deviates from norms or when suspicious cues are detected), the system reduces overall processing time while maintaining high detection accuracy for adversarial behaviors.
3Adaptability or versatility
If multiple analysis dimensions are used, then behavior recognition completeness is improved, but system complexity increases
Solution Approach 1:
The system divides multi-dimensional behavior analysis into three independent analysis modules: crossing behavior analysis, non-verbal cue analysis, and audible cue analysis. Each module operates with its own simplified algorithms focused on specific dimensions, avoiding the need for one complex integrated algorithm. This modular segmentation achieves comprehensive behavior recognition while keeping individual algorithm components manageable and interpretable.
Data Source
AI summary
A system for identifying adversarial behavior directed to an autonomous vehicle includes a pedestrian detection system in communication with a vehicle controller adapted to identify a pedestrian within proximity of the autonomous vehicle, track the pedestrian, determine if the pedestrian is intending to cross in front of the autonomous vehicle, and if the pedestrian tracking system determines that the pedestrian is not intending to cross in front of the autonomous vehicle, the pedestrian tracking system is further adapted to continue tracking the pedestrian, and if the pedestrian tracking system determines that the pedestrian is intending to cross in front of the autonomous vehicle, the pedestrian tracking system is further adapted to determine, with an adversarial intent algorithm, if the pedestrian is exhibiting any adversarial behavior.