Egocentric Vision Future Vehicle Localization
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Solution Overview
Problem
Automated and advanced driving assist systems (ADAS) face challenges in predicting the future actions and locations of participant vehicles due to limitations in bird's eye view imaging, which is often generated from LiDAR systems or aerial photos, and may not work consistently without the required sensors or be distorted by road irregularities.
Innovation Solution
A computer-implemented method and system for egocentric-vision based future vehicle localization, which involves receiving egocentric first-person view images, encoding past bounding box trajectories and dense optical flow, and decoding future bounding boxes to control autonomous vehicle movement based on predicted locations and trajectories of traffic participants.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If bird's eye view imaging is used for future vehicle localization, then prediction accuracy may be improved, but sensor requirements and system complexity increase
Solution Approach 1:
The patent extracts the essential function of bird's eye view imaging (future location prediction) and implements it through software-based image processing of standard camera feeds, removing the requirement for specialized LiDAR sensors or aerial photography systems while maintaining the core predictive capability
Solution Approach 2:
The patent creates a virtual bird's eye view representation by processing and transforming standard vehicle-mounted camera images, effectively copying the functional benefit of expensive specialized sensors using affordable commodity camera systems combined with computational algorithms
2Measurement precision
If LiDAR points are projected to ground plane for BEV image generation, then future location prediction capability is enhanced, but road irregularities cause distortion
Solution Approach 1:
Instead of projecting 3D LiDAR points onto a 2D ground plane (which causes distortion on irregular surfaces), the patent inverts the approach by processing 2D camera images through optical flow analysis to directly predict future 2D bounding box positions, avoiding the problematic 3D-to-2D transformation entirely
Solution Approach 2:
The patent replaces the mechanical/geometric projection system (LiDAR point cloud transformation) with an optical information processing system (dense optical flow field analysis), substituting physical measurement and geometric transformation with computational image analysis that is inherently more robust to surface variations
3Measurement precision
If dense optical flow encoding is applied to egocentric images, then motion prediction accuracy improves, but computational complexity increases
Solution Approach 1:
The patent segments the computational task by first detecting and tracking individual traffic participants using bounding boxes, then applying dense optical flow encoding only to regions containing these participants rather than processing the entire image, thereby maintaining prediction accuracy while reducing overall computational complexity
Solution Approach 2:
The patent performs preliminary bounding box detection and trajectory encoding before applying computationally intensive dense optical flow analysis, preparing the data in advance so that the heavy computational workload can be focused selectively on relevant regions and time frames, improving efficiency without sacrificing accuracy
Data Source
AI summary
A system and method for egocentric-vision based future vehicle localization that include receiving at least one egocentric first person view image of a surrounding environment of a vehicle. The system and method also include encoding at least one past bounding box trajectory associated with at least one traffic participant that is captured within the at least one egocentric first person view image and encoding a dense optical flow of the egocentric first person view image associated with the at least one traffic participant. The system and method further include decoding at least one future bounding box associated with the at least one traffic participant based on a final hidden state of the at least one past bounding box trajectory encoding and the final hidden state of the dense optical flow encoding.