Latent-Space V2V Data Compression for Accurate Vehicle Prediction
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Solution Overview
Problem
Existing vehicle-to-vehicle (V2V) communication systems face challenges in accurately sharing and decompressing raw sensor data, leading to incomplete scene information and prediction errors, as raw data from sensors like LIDAR lacks context for vehicles to coordinate traffic and ensure safety.
Innovation Solution
An assistance system processes packets from encoded data in a latent space, generating an attention vector to optimize data decoding and reduce data representations, thereby improving prediction models by selectively sharing encoded data with context about target vehicles, such as trajectories and hazardous objects.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of energy
If raw sensor data is compressed to reduce network load, then communication efficiency is improved, but lost details and errors occur during decompression
Solution Approach 1:
The patent introduces an intermediary representation (latent space encoding) between raw sensor data and the final processed data. Instead of directly compressing raw sensor data, the system first transforms it into a latent space representation that captures essential scene information. This intermediary form enables efficient compression while preserving critical details needed for accurate decompression and prediction, thus resolving the contradiction between network load reduction and data accuracy maintenance.
2Device complexity
If raw data from sensors is shared without context, then data transmission is simplified, but scene understanding becomes incomplete
Solution Approach 1:
The patent merges multiple sources of information into a unified latent space representation. It combines raw sensor data with contextual information about the scene (such as object relationships, environmental conditions, and semantic meaning) into a single encoded form. This merged representation is then transmitted through the network, preserving both the simplicity of data transmission and the completeness of scene understanding, as the context is integrated rather than separate.
3Measurement precision
If encoded data with context is shared selectively, then prediction accuracy is improved, but communication complexity increases
Solution Approach 1:
The patent changes the parameter space in which data is represented and transmitted. Instead of transmitting raw sensor readings or fully processed scene graphs, the system transforms data into a latent space with optimized parameters that balance information content and transmission efficiency. By adjusting the encoding parameters and dimensionality of the latent representation, the system achieves high prediction accuracy while managing communication complexity through parameter optimization rather than structural complexity.
Data Source
AI summary
System, methods, and other embodiments described herein relate to improving prediction models by compressing and sharing encoded data for partial scene representations about target vehicles. In one embodiment, a method includes receiving, by a subject vehicle, packets with compressed partial representations of a latent space associated with different views of target vehicles. The method also includes generating an attention vector about the different views by aggregating the packets for the target vehicles. The method also includes computing, by a prediction model, an addition vector that optimizes data decoding by the prediction model using acquired data from the attention vector. The method also includes training the prediction model using the addition vector to reduce data representations and adapt data compression associated with the latent space.