Pedestrian Behavior Prediction Using Visual Recognition Assessment
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Solution Overview
Problem
Current systems for predicting pedestrian behavior on roads lack accuracy, which is crucial for safe driving, especially in scenarios where pedestrians visually recognize objects that may indicate their intention to cross.
Innovation Solution
A prediction apparatus comprising an acquisition unit for peripheral information, a determination unit to assess if a pedestrian visually recognizes a predetermined object, and a prediction unit that predicts the pedestrian's movement across the road based on this recognition, using a combination of cameras, radar, and LiDAR for accurate behavior prediction.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional behavior prediction methods are used, then the system complexity is low, but the prediction accuracy of pedestrian behavior is insufficient
Solution Approach 1:
The prediction apparatus segments the pedestrian behavior prediction into multiple independent modules: acquisition unit for collecting data, determination unit for assessing visual recognition, and prediction unit for forecasting behavior. This segmentation allows each module to specialize in specific tasks, improving overall prediction accuracy while maintaining manageable system complexity through modular design.
Solution Approach 2:
The system transitions from traditional single-dimension prediction to multi-dimensional analysis by incorporating visual recognition assessment as an additional dimension. The determination unit evaluates whether pedestrians visually recognize specific objects (crossings, vehicles, obstacles), adding this cognitive state dimension to the prediction model, thereby significantly improving prediction accuracy beyond conventional methods.
2Measurement precision
If visual recognition determination is added to predict pedestrian behavior, then the prediction accuracy improves, but the processing time increases
Solution Approach 1:
The system performs preliminary assessment of visual recognition status before final behavior prediction. The determination unit pre-evaluates whether pedestrians visually recognize relevant objects, and this preliminary information is then used by the prediction unit to make more accurate forecasts. This preliminary action reduces the need for complex real-time processing during critical decision moments.
Solution Approach 2:
The determination unit acts as an intermediary between raw data acquisition and final behavior prediction. It processes visual recognition information and transforms it into meaningful indicators that the prediction unit can utilize. This intermediary layer simplifies the overall processing by pre-processing complex visual recognition data into actionable insights, reducing the computational burden on the final prediction stage.
Data Source
AI summary
A prediction apparatus, comprising an acquisition unit configured to acquire peripheral information of an own vehicle, a determination unit configured to determine, based on the peripheral information, whether a behavior prediction target on a road visually recognizes a predetermined object, and a prediction unit configured to, if it is determined by the determination unit that the behavior prediction target visually recognizes the predetermined object, predict that the behavior prediction target moves across the road in a crossing direction.