Multi-View Collision Hazard Estimation Beyond Driver Field of View
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Solution Overview
Problem
Existing driver assistance systems rely solely on ego-centric data, which provides limited information and struggles to detect hazards outside the driver's field of vision, leading to ineffective hazard detection in diverse and complex traffic environments.
Innovation Solution
Integrate infrastructure-mounted cameras to construct a first representation of the traffic environment, generating a bird's eye view and scene graph, and transform this data into egocentric scene graphs to provide a comprehensive understanding of traffic hazards, using models trained on ego-centric data to estimate collision risks.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If ego-centric data is used for hazard detection, then the system can process information from the driver's perspective, but it fails to detect hazards outside the driver's field of vision
Solution Approach 1:
The patent introduces an intermediary representation system that transforms infrastructure camera views into ego-centric representations. This intermediary process uses bird's eye view construction and scene graph transformation to bridge the gap between infrastructure-mounted camera perspectives and the driver's viewpoint, enabling hazards outside the direct field of vision to be detected and presented in a driver-relevant format
Solution Approach 2:
The patent employs dimensionality transformation by converting 2D infrastructure camera images into 3D bird's eye view representations, and then transforming these into ego-centric scene graphs. This multi-dimensional transformation allows the system to capture hazards from infrastructure perspectives while presenting them in a format meaningful to the driver's ego-centric viewpoint
2Reliability
If multiple data sources and viewpoints are integrated, then comprehensive hazard assessment is improved, but the complexity of processing diverse perspectives increases
Solution Approach 1:
The patent segments the complex task of multi-view hazard detection into distinct processing stages: bird's eye view construction from infrastructure images, scene graph generation from the bird's eye view, and transformation to ego-centric representations. This segmentation allows each component to be optimized independently while maintaining overall system reliability
Solution Approach 2:
The patent creates a universal representation framework that can process multiple data sources (infrastructure cameras, ego-centric sensors) and transform them into a common ego-centric scene graph format. This multi-functional approach allows the same processing pipeline to handle diverse input types, reducing overall system complexity
Data Source
Figure 1~2
AI summary
A method (20) for estimating a collision hazard level in a traffic environment captured from a first point of view, comprising: - constructing (21) a first representation of the traffic environment comprising at least one predetermined feature of each detected road user in the traffic environment to generate a second representation of the traffic environment from a second point of view of each detected road user; and - processing (23) all the second representations of the traffic environment to estimate a collision hazard level between each detected road user and every other detected road user.