Multi-Participant Traffic Safety Prediction System
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Solution Overview
Problem
Existing traffic safety systems are inadequate in predicting and mitigating risks involving three or more traffic participants in a chained manner, as they primarily focus on two-party interactions and do not effectively account for the complexities of multiple participants' behaviors and driving capabilities.
Innovation Solution
A traffic safety support system that recognizes and acquires information on multiple traffic participants, predicts their future behaviors based on recognition and state information, and notifies them of potential risks through a coordinated communication network, utilizing a virtual space simulation and pattern behavior analysis to identify high-risk scenarios and alert participants to take preventive actions.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If a traffic safety system focuses on two-party interaction prediction, then the system complexity remains manageable, but the system cannot predict chain risks involving three or more traffic participants
Solution Approach 1:
The patent segments the complex multi-party risk prediction problem into hierarchical levels: individual traffic participant behavior prediction, two-party interaction prediction, and multi-party chain risk prediction. Each level processes information independently and feeds results to the next level, making the overall system manageable while achieving comprehensive chain risk detection
Solution Approach 2:
The patent implements a nested prediction structure where individual participant behavior models are nested within two-party interaction models, which are in turn nested within multi-party chain risk models. This nested architecture allows the system to build complex predictions from simpler components, improving reliability without proportionally increasing system complexity
2Measurement precision
If the system predicts futures of multiple traffic participants based on comprehensive recognition information, then the prediction accuracy improves, but the information processing time and computational load increase
Solution Approach 1:
The patent performs preliminary classification and filtering of recognition information before detailed prediction processing. Traffic participants are pre-categorized by type and risk level, and only relevant information for each participant category is processed in detail, reducing overall processing time while maintaining prediction accuracy for critical participants
Solution Approach 2:
The patent applies different processing qualities to different traffic participants based on their relevance to potential chain risks. High-priority participants receive comprehensive analysis with multiple data sources, while lower-priority participants receive streamlined processing, optimizing the balance between accuracy and processing speed
3Reliability
If the system notifies all traffic participants of potential chain risks, then the safety improvement is maximized, but the notification load and communication overhead increase
Solution Approach 1:
The patent provides differentiated notification content to different traffic participants based on their specific role and risk exposure in the predicted chain scenario. Each participant receives tailored warnings relevant to their situation rather than generic notifications, improving safety effectiveness while reducing redundant communication
Solution Approach 2:
The patent implements selective notification where only traffic participants involved in the specific chain risk scenario are notified, rather than all participants in the system. This partial action approach focuses communication resources on those who need the information most, reducing overall notification volume while maintaining safety
Data Source
AI summary
This traffic safety support system comprises: a target traffic area recognition unit for acquiring recognition information concerning each of traffic participants; a driving subject information acquisition unit for acquiring driving subject state information correlated to the driving ability of a driving subject of a movement body recognized as a traffic participant; a prediction unit for predicting the future of a plurality of the traffic participants on the basis of the recognition information and the driving subject state information; and a cooperative support information notification unit for making notification of cooperative support information according to a prediction result. The prediction unit is characterized by: recognizing a motorcycle 3a, a four-wheeled vehicle 2b, and a pedestrian group 4a as first to third traffic participants, respectively; and predicting, on the basis of the recognition information and the driving subject state information, a future behavior of the motorcycle 3a, a future behavior of the four-wheeled vehicle 2b according to the future behavior of the motorcycle 3a, and a chain risk of the pedestrian group 4a in the future according to at least one of the future behaviors of the motorcycle 3a and the four-wheeled vehicle 2b.