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Real-time detection of lanes and boundaries by autonomous vehicles

A lane and vehicle technology, applied in vehicle position/route/height control, character and pattern recognition, non-electric variable control, etc., can solve problems such as inability to run effectively in real time, high computational cost, and accuracy limited to ideals

Pending Publication Date: 2020-05-01
NVIDIA CORP
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  • Summary
  • Abstract
  • Description
  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

However, performing lane and road boundary detection in this way has proven to be too computationally expensive to run efficiently in real-time, and / or be inaccurate due to shortcuts implemented to reduce computational requirements
In other words, these traditional systems either give up the accuracy of real-time operation, or give up real-time operation to produce acceptable accuracy
Furthermore, even in conventional systems that achieve the level of accuracy required to safely and efficiently operate autonomous vehicles, the accuracy is limited to ideal road and weather conditions
As a result, autonomous vehicles operating using these traditional methods may not operate accurately in real time and / or with precision in all road and weather conditions

Method used

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  • Real-time detection of lanes and boundaries by autonomous vehicles
  • Real-time detection of lanes and boundaries by autonomous vehicles
  • Real-time detection of lanes and boundaries by autonomous vehicles

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Embodiment Construction

[0033] Systems and methods related to real-time detection of lane and road boundaries by autonomous vehicles and / or advanced driver assistance systems (ADAS) using one or more machine learning models are disclosed. The present disclosure may be described with reference to an exemplary autonomous vehicle 800 (alternatively referred to herein as "vehicle 800" or "autonomous vehicle 800"), examples of which are referenced herein Figures 8A-8D describe. However, this is not meant to be limiting. For example, the systems and methods described herein may be used in augmented reality, virtual reality, robotics, and / or other technical fields, such as for positioning, calibration, and / or other processes. Furthermore, although the detections described herein primarily relate to lanes, road boundaries, lane splits, lane merges, intersections, crosswalks, etc., the present disclosure is not intended to be limited to these detections. For example, the processes described herein can be u...

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Abstract

In various examples, sensor data representative of an image of a field of view of a vehicle sensor may be received and the sensor data may be applied to a machine learning model. The machine learningmodel may compute a segmentation mask representative of portions of the image corresponding to lane markings of the driving surface of the vehicle. Analysis of the segmentation mask may be performed to determine lane marking types, and lane boundaries may be generated by performing curve fitting on the lane markings corresponding to each of the lane marking types. The data representative of the lane boundaries may then be sent to a component of the vehicle for use in navigating the vehicle through the driving surface.

Description

Background technique [0001] For autonomous vehicles to operate safely in all environments, autonomous vehicles must be able to efficiently perform vehicle maneuvers - such as lane keeping, lane changing, lane splitting, turning, stopping and starting at intersections, crosswalks, etc., and / or other vehicles manipulate. For example, for an autonomous vehicle to navigate surface streets (e.g., city streets, sidewalks, neighborhoods, etc.) lanes, intersections, crosswalks, boundaries, etc.) where one or more subdivisions of the road are usually minimally delineated and appear to be difficult even for the most attentive and experienced driver at certain difficult to identify under certain conditions. In other words, an autonomous vehicle must be the functional equivalent of an attentive human driver, utilizing a perception and action system with an incredible ability to recognize and respond to moving and stationary obstacles in complex environments, just is to avoid collisions...

Claims

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): G06K9/00G06K9/46G06K9/48G06K9/62G06V10/44G06V10/46G06V10/764
CPCG06V20/588G06V10/457G06V10/471G06V10/44G06V10/46G06V10/82G06V10/764G05D1/0088G05D1/0221G06T7/10G06N3/084G06V20/41G06F18/24143
Inventor 许怡芳刘昕陈家智C·帕拉达D·奥诺弗里奥M·帕克M·S·穆罕默德阿巴迪V·辛塔拉普蒂O·敦克尔J·泽德勒维斯基P·贾妮斯J·N·弗里奇G·格里戈尔王佐冠I-K·陈M·塞恩斯
Owner NVIDIA CORP
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