US-20260126294-A1 - MAP DATA COMPRESSION METHODS IMPLEMENTING MACHINE LEARNING

US20260126294A1US 20260126294 A1US20260126294 A1US 20260126294A1US-20260126294-A1

Abstract

Aspects of the present disclosure provide techniques for training a machine-learning model to compress map data for use online by an autonomous vehicle and techniques for compressing map data using the trained machine-learning model. A system includes a computing device configured to deploy a simulation environment initiating an instance of a virtual vehicle, execute iterations of a simulation of the virtual vehicle, wherein each iteration: deploys a set of map data compressed by the machine-learning compression model and causes the virtual vehicle to execute control operations based on the deployed set of map data, evaluate performance of the executed control operations by the virtual vehicle based on the compressed map data for each iteration, and train the machine-learning compression model to compress map data such that the evaluated performance of the executed control operations by the virtual vehicle exceeds a performance threshold.

Inventors

Michael J. BENISCH
James J. Kuffner

Assignees

WOVEN BY TOYOTA, INC.

Dates

Publication Date: 20260507
Application Date: 20260105

Claims (20)

1 . A method for compressing map data, the method comprising: receiving, with a computing device, uncompressed map data; implementing, with the computing device, a machine-learning compression model, wherein the machine-learning compression model is trained in response to an evaluated performance of a virtual vehicle subjected to iterations of a simulation of the virtual vehicle, wherein each iteration deploys a set of map data compressed by the machine-learning compression model and causes the virtual vehicle to execute control operations based on the deployed set of compressed map data; compressing the uncompressed map data with the machine-learning compression model; and outputting a compressed map data for use online by a vehicle.
2 . The method of claim 1 , further comprising transmitting the compressed map data to the vehicle.
3 . The method of claim 1 , wherein the virtual vehicle deployed in the simulation comprises at least one autonomous vehicle system or a semi-autonomous vehicle system.
4 . The method of claim 1 , wherein the set of map data comprises geospatial data.
5 . The method of claim 1 , wherein the machine-learning compression model is trained in response to the evaluated performance of the virtual vehicle comprises: identifying one or more proxy signals corresponding to aspects of the compressed map data; and causing the machine-learning compression model to preserve the one or more identified proxy signals when compressing map data.
6 . The method of claim 5 , wherein the one or more identified proxy signals comprises at least one of a smoothness of lane geometry, intersection positioning, or sign positioning.
7 . The method of claim 1 , wherein the iterations of the simulation of the virtual vehicle comprises deploying an updated compressed map data based on training revisions made to the machine-learning compression model.
8 . A system for compressing map data, the system comprising: a computing device configured to: receive uncompressed map data; implement a machine-learning compression model, wherein the machine-learning compression model is trained in response to an evaluated performance of a virtual vehicle subjected to iterations of a simulation of the virtual vehicle, wherein each iteration deploys a set of map data compressed by the machine-learning compression model and causes the virtual vehicle to execute control operations based on the deployed set of compressed map data; compress the uncompressed map data with the machine-learning compression model; and output a compressed map data for use online by a vehicle.
9 . The system of claim 8 , wherein the computing device is further configured to transmit the compressed map data to the vehicle.
10 . The system of claim 8 , wherein the virtual vehicle deployed in the simulation comprises at least one autonomous vehicle system or a semi-autonomous vehicle system.
11 . The system of claim 8 , wherein the set of map data comprises geospatial data.
12 . The system of claim 8 , wherein the computing device is further configured to: identify one or more proxy signals corresponding to aspects of the compressed map data; and cause the machine-learning compression model to preserve the one or more identified proxy signals when compressing map data.
13 . The system of claim 12 , wherein the one or more identified proxy signals comprises at least one of a smoothness of lane geometry, intersection positioning, or sign positioning.
14 . The system of claim 8 , wherein the computing device is further configured to deploy, with the iterations of the simulation of the virtual vehicle, an updated compressed map data based on training revisions made to the machine-learning compression model.
15 . A vehicle comprising: a computing device configured to: receive uncompressed map data; implement a machine-learning compression model, wherein the machine-learning compression model is trained in response to an evaluated performance of a virtual vehicle subjected to iterations of a simulation of the virtual vehicle, wherein each iteration deploys a set of map data compressed by the machine-learning compression model and causes the virtual vehicle to execute control operations based on the deployed set of compressed map data; compress the uncompressed map data with the machine-learning compression model; and output a compressed map data for use online by the vehicle.
16 . The vehicle of claim 15 , wherein the computing device is further configured to transmit the compressed map data to the vehicle.
17 . The vehicle of claim 15 , wherein the virtual vehicle deployed in the simulation comprises at least one autonomous vehicle system or a semi-autonomous vehicle system.
18 . The vehicle of claim 15 , wherein the set of map data comprises geospatial data.
19 . The vehicle of claim 15 , wherein the computing device is further configured to: identify one or more proxy signals corresponding to aspects of the compressed map data; and cause the machine-learning compression model to preserve the one or more identified proxy signals when compressing map data.
20 . The vehicle of claim 19 , wherein the one or more identified proxy signals comprises at least one of a smoothness of lane geometry, intersection positioning, or sign positioning.

Description

CROSS-REFERENCE TO RELATED APPLICATIONS This application is a continuation of U.S. Patent Application No. 17/965,329, filed October 13, 2022 which is incorporated herein by reference in its entirety. TECHNICAL FIELD The present disclosure relates to systems, methods, and computer implemented programs for training a machine-learning compression model to compress map data for use online by an autonomous vehicle and systems, methods, and computer implemented programs for compressing map data using the trained machine-learning compression model. BACKGROUND Data compression of a data file is the reduction in the number of bits needed to represent data. A compressed data file requires less volume of memory for storage than an uncompressed data file and can be transferred between locations in less time than the uncompressed data file version. Once a compressed data file is received by a destination system and/or extracted for use by a system, the compressed data file is decompressed. However, the process of compressing and decompressing data files in an inherently lossy process. Moreover, available compression techniques indiscriminately compress data files. That is, compression techniques do not take into account whether portions of the data file are more critical than others when carrying out compression of the data file. Accordingly, a need exists for data compression techniques that do not indiscriminately compress data and more specifically intelligently compress data files to preserve performance of such data when utilized for predefined tasks. SUMMARY In an embodiment, a system for training a machine-learning model to compress map data for use online by a vehicle includes a computing device. The computing device is configured to deploy a simulation environment initiating an instance of a virtual vehicle and execute iterations of a simulation of the virtual vehicle, where each iteration: deploys a set of map data compressed by the machine-learning compression model and causes the virtual vehicle to execute control operations based on the deployed set of map data. The computing device is further configured to evaluate performance of the executed control operations by the virtual vehicle based on the compressed map data for each iteration and train the machine-learning compression model to compress map data such that the evaluated performance of the executed control operations by the virtual vehicle exceeds a performance threshold. In some embodiments, a method for training a machine-learning model to compress map data for use online by a vehicle is disclosed. The method includes deploying, with a computing device, a simulation environment initiating an instance of a virtual vehicle; executing, with the computing device, iterations of a simulation of the virtual vehicle, where each iteration: deploys a set of map data compressed by the machine-learning compression model and causes the virtual vehicle to execute control operations based on the deployed set of map data; evaluating, with the computing device, performance of the executed control operations by the virtual vehicle based on the compressed map data for each iteration; and training, with the computing device, the machine-learning compression model to compress map data such that the evaluated performance of the executed control operations by the virtual vehicle exceeds a performance threshold. In some embodiments, a method for compressing map data includes receiving, with a computing device, uncompressed map data, implementing, with the computing device, a machine-learning compression model, where the machine-learning compression model is trained in response to an evaluated performance of a virtual vehicle subjected to iterations of a simulation of the virtual vehicle, wherein each iteration deploys a set of map data compressed by the machine-learning compression model and causes the virtual vehicle to execute control operations based on the deployed set of map data, compressing the uncompressed map data with the machine-learning compression model, and outputting a compressed map data for use online by a vehicle. These and additional features provided by the embodiments described herein will be more fully understood in view of the following detailed description, in conjunction with the drawings. BRIEF DESCRIPTION OF THE DRAWINGS The embodiments set forth in the drawings are illustrative and exemplary in nature and not intended to limit the subject matter defined by the claims. The following detailed description of the illustrative embodiments can be understood when read in conjunction with the following drawings, where like structure is indicated with like reference numerals and in which: FIG. 1 schematically depicts an illustrative system, according to one or more embodiments shown and described herein; FIG. 2 schematically depicts an illustrative computing device, according to one or more embodiments shown and described herein; FIG. 3 depicts an illus