High definition map based localization optimization

A technology of positioning variables and positioning technology, applied in the field of positioning strategies, can solve the problems of autonomous vehicles that cannot navigate correctly and work poorly

Pending Publication Date: 2020-09-11
NVIDIA CORP
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  • Summary
  • Abstract
  • Description
  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

Parameters that work well in one geographic area may work poorly in another
If the localization process fails, the autonomous vehicle may not navigate correctly

Method used

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  • High definition map based localization optimization
  • High definition map based localization optimization
  • High definition map based localization optimization

Examples

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Embodiment Construction

[0031] Embodiments of the present invention use high accuracy to maintain a high definition (HD) map containing the latest information. HD maps can be used by autonomous vehicles to safely navigate to their destinations without or with limited manual input. An autonomous vehicle is a vehicle that can sense its environment and navigate without human input. Autonomous vehicles may also be referred to as "unmanned vehicles", "self-driving cars" or "robot cars" in this article. HD maps are maps that store data with very high accuracy (usually 5-10cm). The embodiment generates an HD map containing spatial geometric information about roads on which autonomous vehicles can travel. Therefore, the generated HD map includes information necessary for the autonomous vehicle to safely navigate without human intervention. The embodiment generates and maintains a high definition (HD) map that is accurate and includes the latest road conditions for safe navigation.

[0032] Autonomous vehicl...

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Abstract

A vehicle, for example, an autonomous vehicle performs localization to determine the current location of the vehicle using different localization techniques as the vehicle drives. The localization technique used by the autonomous vehicle is selected from a localization variant index that stores mapping from a driving context to localization variant, each localization variant identifying a localization technique. The driving context may comprise information including: a geographical region in which the autonomous vehicle is driving, a speed at which the autonomous vehicle is driving, an angularvelocity of the autonomous vehicle, or other information. Using an optimal localization technique in each driving context improves the accuracy of localization as well as computing efficiency of theprocess of localization.

Description

[0001] Cross references to related applications [0002] This application claims the priority rights of U.S. Provisional Application 62 / 593,334 filed on December 1, 2017 under 35USC 119(e), and the entire content of the U.S. Provisional Application is incorporated herein by reference for all purposes. Background technique [0003] The present disclosure generally relates to the positioning of autonomous vehicles, and more specifically relates to optimizing the positioning strategy used by the autonomous vehicle based on the driving context (eg, geographic area where the autonomous vehicle is driving, time of day, speed of the autonomous vehicle, etc.). [0004] Autonomous vehicles (also known as self-driving cars, driverless cars, cars, or robotic cars) drive from a source location to a destination location without the need for a human driver to control and navigate the vehicle. Autonomous vehicles need to accurately determine their location in order to be able to navigate. Autonomo...

Claims

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Application Information

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IPC IPC(8): G01C21/00
CPCG01C21/3602G01C21/165G01S19/48H04W64/006G01S5/0263G01S5/01G01S5/0244G01C21/3878G01C21/3881G01C21/3815G01C21/3867G01C21/32G05D1/0274G05D1/0278G05D1/0246G05D1/027G05D1/021
Inventor 马克·达蒙·惠乐
Owner NVIDIA CORP
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