Vehicle 3D Surround View Using Adaptive Projection Models
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Solution Overview
Problem
Existing vehicle blind spot detection systems fail to provide comprehensive coverage of a vehicle's surroundings, particularly for large vehicles, leading to potential accidents due to unseen obstacles and limited visibility during parking and maneuvering.
Innovation Solution
A vehicle three-dimensional image system that synthesizes images from multiple cameras using various three-dimensional projection models, adjusting based on input information such as driving direction, speed, and environment, to generate a comprehensive 3D image for improved visibility.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If multiple cameras and projection models are used to reduce blind spots, then visibility and safety are improved, but device complexity increases
Solution Approach 1:
The system dynamically selects and switches between different three-dimensional projection models based on real-time driving conditions, vehicle speed, and steering angle. This allows the blind spot detection system to adapt its complexity to the actual driving situation, using simpler models when appropriate and more complex models only when needed, thereby improving safety while managing device complexity
Solution Approach 2:
The monitoring area around the vehicle is divided into multiple zones with different detection requirements. Different projection models are applied to different zones based on their specific needs, allowing the system to optimize detection coverage without uniformly applying maximum complexity across all areas
2Loss of information
If multiple cameras are installed to provide comprehensive coverage, then blind spot detection is improved, but cost and device complexity increase
Solution Approach 1:
Multiple camera feeds are merged and integrated into a unified three-dimensional view using projection models. This combining approach allows the system to achieve comprehensive coverage and reduce information loss by synthesizing data from multiple sources into a cohesive visualization, rather than requiring separate display systems for each camera
3Loss of information
If three-dimensional projection models are used to reduce blind spots, then visibility is improved, but computational requirements and energy consumption increase
Solution Approach 1:
The system dynamically adjusts computational resources and projection model complexity based on driving conditions. During normal driving, less computationally intensive models are used, while more demanding models are activated only when blind spot risks are detected or during critical maneuvers, thereby reducing overall energy consumption while maintaining visibility
Solution Approach 2:
The system changes parameters such as projection model resolution, update frequency, and computational detail based on driving speed, steering angle, and detected risk levels. This allows the visibility quality to be optimized for each situation while minimizing unnecessary computational energy consumption during low-risk periods
Data Source
AI summary
A vehicle three-dimensional image system includes a computing device configured to generate a synthesized three-dimensional image by acquiring a plurality of images from a plurality of cameras installed on a vehicle and arranging the plurality of images on a surface of a three-dimensional projection model, wherein the computing device is configured to generate the synthesized three-dimensional image by storing a plurality of three-dimensional projection models, selecting one of the plurality of three-dimensional projection models according to input information, and arranging the plurality of images on a surface of the selected three-dimensional projection model.