World Model Keyframe Compression for Autonomous Vehicle Navigation
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Solution Overview
Problem
Autonomous vehicles face challenges in efficient data processing and storage due to the large size of world model frames, which consume significant resources and are affected by sensor limitations such as occlusions and latency in processing real-time data for navigation.
Innovation Solution
Implementing keyframe-based compression methods that compute and transform world model frames at different frequencies, using a double buffer system to alternate keyframe computation and transformation, reducing data transmission and storage needs by generating multiple frames from each keyframe.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If world model frames are computed at a rate of 30 frames per second based on map data, then navigation accuracy and real-time decision making are improved, but communication, processing, and storage resources are excessively consumed
Solution Approach 1:
The patent segments the continuous world model computation into discrete keyframes captured at specific timestamps. Instead of processing all intermediate states, the system divides the temporal sequence into representative keyframes that capture essential environmental changes, reducing the overall data volume while preserving navigation-critical information.
Solution Approach 2:
The patent creates multiple transformed copies of each keyframe at different coordinate frames (global, local, ego-vehicle) rather than computing independent frames for each coordinate system. These transformed copies are generated through mathematical transformations of the same underlying keyframe data, significantly reducing computation and storage requirements.
2Loss of information
If all world model frames are stored and transmitted in real-time, then complete environmental information is available for decision making, but communication bandwidth and storage capacity are overwhelmed
Solution Approach 1:
The patent performs preliminary coordinate transformations on keyframes to generate all necessary coordinate frame representations (global, local, ego-vehicle) in advance. By pre-computing these transformations when the keyframe is first captured, the system avoids redundant real-time transformation computations and reduces the data transmission burden, as all transformed versions are already available when needed.
3Speed
If real-time sensor data is processed at high frequency, then responsive driving decisions are enabled, but processing latency increases and computational resources are exhausted
Solution Approach 1:
The patent implements periodic keyframe capture at specific timestamps rather than continuous processing of all sensor data streams. This periodic sampling approach reduces the computational load by processing only representative moments in time, while still capturing essential environmental changes needed for safe navigation decisions.
Solution Approach 2:
The patent extracts only the essential keyframe data from the continuous sensor stream at critical timestamps, separating the most important environmental information from the redundant data. By taking out only the necessary keyframes and their transformations, the system reduces processing requirements while maintaining the information needed for responsive decision-making.
Data Source
AI summary
In various examples, a method includes computing a current keyframe, the current keyframe being representative of an area around an autonomous vehicle at a current time based on map data. The method includes transforming a preceding keyframe to a coordinate frame of the autonomous vehicle at a first time prior to completing computation of the current keyframe to generate a first world model frame. The method includes transforming the preceding keyframe to the coordinate frame of the autonomous vehicle at a second time after the first time and prior to completing computation of the current keyframe to generate a second world model frame.