Posture-Based Action Prediction for Pedestrian Collision Warning
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Solution Overview
Problem
Current accident avoidance technologies for drivers lack effective methods to predict and prevent interactions with pedestrians and two-wheeled vehicles, as they rely on limited data sources and do not accurately anticipate the actions of these targets.
Innovation Solution
An information processing apparatus with a detection unit, estimation unit, and prediction unit that analyzes input images to detect and estimate the posture of targets, such as pedestrians and two-wheeled vehicles, and predicts their actions based on these postures, using features like the barycenter point to accurately forecast movements and potential collisions.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Device complexity
If accident avoidance technologies use limited data sources (weather information, road information, database readings), then the system complexity is reduced, but the prediction accuracy of target object actions deteriorates
Solution Approach 1:
The system segments the target object detection into multiple independent modules: detection unit for identifying target objects, estimation unit for determining posture, and prediction unit for forecasting actions. Each module processes specific aspects separately, allowing complex predictions to be built from simpler, more accurate component analyses.
Solution Approach 2:
The system transitions from analyzing only static target object presence to analyzing multi-dimensional attributes including posture estimation and action prediction. By adding the posture dimension as an intermediate analysis layer between detection and prediction, the system achieves more accurate action forecasts without proportionally increasing overall system complexity.
2Measurement precision
If the system detects and estimates posture of target objects (pedestrians, two-wheeled vehicles), then the action prediction accuracy improves, but the device complexity increases
Solution Approach 1:
The system performs preliminary posture estimation before action prediction. By pre-processing the target object data to extract posture information first, the prediction unit receives enriched input data that enables more accurate action forecasting without requiring a complete system redesign.
Solution Approach 2:
The estimation unit acts as an intermediary between the detection unit and prediction unit. It transforms raw detection data into meaningful posture information that bridges the gap between simple object detection and complex action prediction, reducing the complexity burden on both adjacent modules.
3Reliability
If the system provides comprehensive cautionary information to drivers about pedestrians and two-wheeled vehicles, then the safety effectiveness improves, but the information processing time increases
Solution Approach 1:
The system focuses on predicting and warning about the most critical actions of target objects (such as sudden movements, direction changes) rather than analyzing all possible actions equally. This partial focus on high-risk scenarios provides sufficient safety effectiveness while reducing overall processing time.
Solution Approach 2:
The system establishes a feedback loop where detection results inform posture estimation, which in turn refines action predictions. This iterative feedback mechanism allows the system to progressively improve prediction accuracy without requiring all processing to complete simultaneously, reducing overall time loss.
Data Source
AI summary
An information processing apparatus according to an embodiment of the present technology includes a detection unit, an estimation unit, and a prediction unit. The detection unit detects a target object from an input image. The estimation unit estimates a posture of the detected target object. The prediction unit predicts an action of the target object on a basis of the estimated posture.