CN114945953A - 自动驾驶的损失评估方法及装置 - Google Patents
自动驾驶的损失评估方法及装置 Download PDFInfo
- Publication number
- CN114945953A CN114945953A CN202180004560.2A CN202180004560A CN114945953A CN 114945953 A CN114945953 A CN 114945953A CN 202180004560 A CN202180004560 A CN 202180004560A CN 114945953 A CN114945953 A CN 114945953A
- Authority
- CN
- China
- Prior art keywords
- loss value
- loss
- observed
- observation object
- correcting
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Pending
Links
- 238000011156 evaluation Methods 0.000 title claims abstract description 31
- 238000000034 method Methods 0.000 claims abstract description 16
- 230000009471 action Effects 0.000 claims description 4
- 238000012937 correction Methods 0.000 claims description 4
- 238000010586 diagram Methods 0.000 description 3
- 230000006870 function Effects 0.000 description 3
- 238000001514 detection method Methods 0.000 description 2
- 238000012986 modification Methods 0.000 description 2
- 230000004048 modification Effects 0.000 description 2
- 238000012549 training Methods 0.000 description 2
- 238000004590 computer program Methods 0.000 description 1
- 125000004122 cyclic group Chemical group 0.000 description 1
- 238000013499 data model Methods 0.000 description 1
- 230000000694 effects Effects 0.000 description 1
- 230000004807 localization Effects 0.000 description 1
- 239000011159 matrix material Substances 0.000 description 1
- 230000003287 optical effect Effects 0.000 description 1
- 230000008569 process Effects 0.000 description 1
- 238000007670 refining Methods 0.000 description 1
Images
Classifications
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06N—COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS
- G06N20/00—Machine learning
-
- B—PERFORMING OPERATIONS; TRANSPORTING
- B60—VEHICLES IN GENERAL
- B60W—CONJOINT CONTROL OF VEHICLE SUB-UNITS OF DIFFERENT TYPE OR DIFFERENT FUNCTION; CONTROL SYSTEMS SPECIALLY ADAPTED FOR HYBRID VEHICLES; ROAD VEHICLE DRIVE CONTROL SYSTEMS FOR PURPOSES NOT RELATED TO THE CONTROL OF A PARTICULAR SUB-UNIT
- B60W30/00—Purposes of road vehicle drive control systems not related to the control of a particular sub-unit, e.g. of systems using conjoint control of vehicle sub-units
- B60W30/08—Active safety systems predicting or avoiding probable or impending collision or attempting to minimise its consequences
- B60W30/095—Predicting travel path or likelihood of collision
- B60W30/0956—Predicting travel path or likelihood of collision the prediction being responsive to traffic or environmental parameters
-
- B—PERFORMING OPERATIONS; TRANSPORTING
- B60—VEHICLES IN GENERAL
- B60W—CONJOINT CONTROL OF VEHICLE SUB-UNITS OF DIFFERENT TYPE OR DIFFERENT FUNCTION; CONTROL SYSTEMS SPECIALLY ADAPTED FOR HYBRID VEHICLES; ROAD VEHICLE DRIVE CONTROL SYSTEMS FOR PURPOSES NOT RELATED TO THE CONTROL OF A PARTICULAR SUB-UNIT
- B60W50/00—Details of control systems for road vehicle drive control not related to the control of a particular sub-unit, e.g. process diagnostic or vehicle driver interfaces
- B60W50/0097—Predicting future conditions
-
- B—PERFORMING OPERATIONS; TRANSPORTING
- B60—VEHICLES IN GENERAL
- B60W—CONJOINT CONTROL OF VEHICLE SUB-UNITS OF DIFFERENT TYPE OR DIFFERENT FUNCTION; CONTROL SYSTEMS SPECIALLY ADAPTED FOR HYBRID VEHICLES; ROAD VEHICLE DRIVE CONTROL SYSTEMS FOR PURPOSES NOT RELATED TO THE CONTROL OF A PARTICULAR SUB-UNIT
- B60W50/00—Details of control systems for road vehicle drive control not related to the control of a particular sub-unit, e.g. process diagnostic or vehicle driver interfaces
- B60W50/06—Improving the dynamic response of the control system, e.g. improving the speed of regulation or avoiding hunting or overshoot
-
- B—PERFORMING OPERATIONS; TRANSPORTING
- B60—VEHICLES IN GENERAL
- B60W—CONJOINT CONTROL OF VEHICLE SUB-UNITS OF DIFFERENT TYPE OR DIFFERENT FUNCTION; CONTROL SYSTEMS SPECIALLY ADAPTED FOR HYBRID VEHICLES; ROAD VEHICLE DRIVE CONTROL SYSTEMS FOR PURPOSES NOT RELATED TO THE CONTROL OF A PARTICULAR SUB-UNIT
- B60W60/00—Drive control systems specially adapted for autonomous road vehicles
- B60W60/001—Planning or execution of driving tasks
- B60W60/0015—Planning or execution of driving tasks specially adapted for safety
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06N—COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS
- G06N3/00—Computing arrangements based on biological models
- G06N3/02—Neural networks
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06N—COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS
- G06N3/00—Computing arrangements based on biological models
- G06N3/02—Neural networks
- G06N3/08—Learning methods
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06N—COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS
- G06N7/00—Computing arrangements based on specific mathematical models
- G06N7/01—Probabilistic graphical models, e.g. probabilistic networks
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06V—IMAGE OR VIDEO RECOGNITION OR UNDERSTANDING
- G06V20/00—Scenes; Scene-specific elements
- G06V20/50—Context or environment of the image
- G06V20/56—Context or environment of the image exterior to a vehicle by using sensors mounted on the vehicle
- G06V20/58—Recognition of moving objects or obstacles, e.g. vehicles or pedestrians; Recognition of traffic objects, e.g. traffic signs, traffic lights or roads
-
- B—PERFORMING OPERATIONS; TRANSPORTING
- B60—VEHICLES IN GENERAL
- B60W—CONJOINT CONTROL OF VEHICLE SUB-UNITS OF DIFFERENT TYPE OR DIFFERENT FUNCTION; CONTROL SYSTEMS SPECIALLY ADAPTED FOR HYBRID VEHICLES; ROAD VEHICLE DRIVE CONTROL SYSTEMS FOR PURPOSES NOT RELATED TO THE CONTROL OF A PARTICULAR SUB-UNIT
- B60W2552/00—Input parameters relating to infrastructure
- B60W2552/45—Pedestrian sidewalk
-
- B—PERFORMING OPERATIONS; TRANSPORTING
- B60—VEHICLES IN GENERAL
- B60W—CONJOINT CONTROL OF VEHICLE SUB-UNITS OF DIFFERENT TYPE OR DIFFERENT FUNCTION; CONTROL SYSTEMS SPECIALLY ADAPTED FOR HYBRID VEHICLES; ROAD VEHICLE DRIVE CONTROL SYSTEMS FOR PURPOSES NOT RELATED TO THE CONTROL OF A PARTICULAR SUB-UNIT
- B60W2554/00—Input parameters relating to objects
- B60W2554/40—Dynamic objects, e.g. animals, windblown objects
- B60W2554/402—Type
- B60W2554/4029—Pedestrians
-
- B—PERFORMING OPERATIONS; TRANSPORTING
- B60—VEHICLES IN GENERAL
- B60W—CONJOINT CONTROL OF VEHICLE SUB-UNITS OF DIFFERENT TYPE OR DIFFERENT FUNCTION; CONTROL SYSTEMS SPECIALLY ADAPTED FOR HYBRID VEHICLES; ROAD VEHICLE DRIVE CONTROL SYSTEMS FOR PURPOSES NOT RELATED TO THE CONTROL OF A PARTICULAR SUB-UNIT
- B60W2554/00—Input parameters relating to objects
- B60W2554/80—Spatial relation or speed relative to objects
Landscapes
- Engineering & Computer Science (AREA)
- Automation & Control Theory (AREA)
- Theoretical Computer Science (AREA)
- Physics & Mathematics (AREA)
- Mechanical Engineering (AREA)
- Transportation (AREA)
- Software Systems (AREA)
- General Physics & Mathematics (AREA)
- Human Computer Interaction (AREA)
- Mathematical Physics (AREA)
- Artificial Intelligence (AREA)
- Data Mining & Analysis (AREA)
- Computing Systems (AREA)
- General Engineering & Computer Science (AREA)
- Evolutionary Computation (AREA)
- Computer Vision & Pattern Recognition (AREA)
- Medical Informatics (AREA)
- General Health & Medical Sciences (AREA)
- Molecular Biology (AREA)
- Biophysics (AREA)
- Computational Linguistics (AREA)
- Health & Medical Sciences (AREA)
- Biomedical Technology (AREA)
- Life Sciences & Earth Sciences (AREA)
- Computational Mathematics (AREA)
- Mathematical Optimization (AREA)
- Mathematical Analysis (AREA)
- Algebra (AREA)
- Probability & Statistics with Applications (AREA)
- Multimedia (AREA)
- Traffic Control Systems (AREA)
- Pure & Applied Mathematics (AREA)
Abstract
一种自动驾驶的损失评估方法及装置,方法包括:将观测对象进行分类或定位,该观测对象是指自动驾驶模型的任务(S101);基于实际驾驶中的真实场景,校正每个观测对象的损失值(S102)。该方法可在自动驾驶算法的评估中使用真实场景的真实值;并纠正使用通用评估方法评估自动驾驶场景中使用的算法的偏差。
Description
PCT国内申请,说明书已公开。
Claims (11)
- PCT国内申请,权利要求书已公开。
Applications Claiming Priority (3)
Application Number | Priority Date | Filing Date | Title |
---|---|---|---|
US17/114,528 | 2020-12-08 | ||
US17/114,528 US20220176998A1 (en) | 2020-12-08 | 2020-12-08 | Method and Device for Loss Evaluation to Automated Driving |
PCT/CN2021/090181 WO2022121214A1 (zh) | 2020-12-08 | 2021-04-27 | 自动驾驶的损失评估方法及装置 |
Publications (1)
Publication Number | Publication Date |
---|---|
CN114945953A true CN114945953A (zh) | 2022-08-26 |
Family
ID=81849863
Family Applications (1)
Application Number | Title | Priority Date | Filing Date |
---|---|---|---|
CN202180004560.2A Pending CN114945953A (zh) | 2020-12-08 | 2021-04-27 | 自动驾驶的损失评估方法及装置 |
Country Status (3)
Country | Link |
---|---|
US (1) | US20220176998A1 (zh) |
CN (1) | CN114945953A (zh) |
WO (1) | WO2022121214A1 (zh) |
Citations (7)
Publication number | Priority date | Publication date | Assignee | Title |
---|---|---|---|---|
CN108376235A (zh) * | 2018-01-15 | 2018-08-07 | 深圳市易成自动驾驶技术有限公司 | 图像检测方法、装置及计算机可读存储介质 |
CN108423006A (zh) * | 2018-02-02 | 2018-08-21 | 辽宁友邦网络科技有限公司 | 一种辅助驾驶预警方法及*** |
US20190073560A1 (en) * | 2017-09-01 | 2019-03-07 | Sri International | Machine learning system for generating classification data and part localization data for objects depicted in images |
CN109447018A (zh) * | 2018-11-08 | 2019-03-08 | 天津理工大学 | 一种基于改进Faster R-CNN的道路环境视觉感知方法 |
CN109740451A (zh) * | 2018-12-17 | 2019-05-10 | 南京理工大学 | 基于重要性加权的道路场景图像语义分割方法 |
US20190354782A1 (en) * | 2018-05-17 | 2019-11-21 | Uber Technologies, Inc. | Object Detection and Property Determination for Autonomous Vehicles |
US20200210721A1 (en) * | 2019-01-02 | 2020-07-02 | Zoox, Inc. | Hierarchical machine-learning network architecture |
Family Cites Families (6)
Publication number | Priority date | Publication date | Assignee | Title |
---|---|---|---|---|
CN110580482B (zh) * | 2017-11-30 | 2022-04-08 | 腾讯科技(深圳)有限公司 | 图像分类模型训练、图像分类、个性化推荐方法及装置 |
CN109447169B (zh) * | 2018-11-02 | 2020-10-27 | 北京旷视科技有限公司 | 图像处理方法及其模型的训练方法、装置和电子*** |
JP6946255B2 (ja) * | 2018-11-13 | 2021-10-06 | 株式会社東芝 | 学習装置、推定装置、学習方法およびプログラム |
JP7521535B2 (ja) * | 2019-10-10 | 2024-07-24 | 日本電気株式会社 | 学習装置、学習方法、物体検出装置、及び、プログラム |
CN110852425A (zh) * | 2019-11-15 | 2020-02-28 | 北京迈格威科技有限公司 | 基于优化的神经网络的处理方法、装置和电子*** |
US20220101066A1 (en) * | 2020-09-29 | 2022-03-31 | Objectvideo Labs, Llc | Reducing false detections for night vision cameras |
-
2020
- 2020-12-08 US US17/114,528 patent/US20220176998A1/en not_active Abandoned
-
2021
- 2021-04-27 CN CN202180004560.2A patent/CN114945953A/zh active Pending
- 2021-04-27 WO PCT/CN2021/090181 patent/WO2022121214A1/zh active Application Filing
Patent Citations (7)
Publication number | Priority date | Publication date | Assignee | Title |
---|---|---|---|---|
US20190073560A1 (en) * | 2017-09-01 | 2019-03-07 | Sri International | Machine learning system for generating classification data and part localization data for objects depicted in images |
CN108376235A (zh) * | 2018-01-15 | 2018-08-07 | 深圳市易成自动驾驶技术有限公司 | 图像检测方法、装置及计算机可读存储介质 |
CN108423006A (zh) * | 2018-02-02 | 2018-08-21 | 辽宁友邦网络科技有限公司 | 一种辅助驾驶预警方法及*** |
US20190354782A1 (en) * | 2018-05-17 | 2019-11-21 | Uber Technologies, Inc. | Object Detection and Property Determination for Autonomous Vehicles |
CN109447018A (zh) * | 2018-11-08 | 2019-03-08 | 天津理工大学 | 一种基于改进Faster R-CNN的道路环境视觉感知方法 |
CN109740451A (zh) * | 2018-12-17 | 2019-05-10 | 南京理工大学 | 基于重要性加权的道路场景图像语义分割方法 |
US20200210721A1 (en) * | 2019-01-02 | 2020-07-02 | Zoox, Inc. | Hierarchical machine-learning network architecture |
Also Published As
Publication number | Publication date |
---|---|
WO2022121214A1 (zh) | 2022-06-16 |
US20220176998A1 (en) | 2022-06-09 |
Similar Documents
Publication | Publication Date | Title |
---|---|---|
US9501703B2 (en) | Apparatus and method for recognizing traffic sign board | |
US10691952B2 (en) | Adapting to appearance variations when tracking a target object in video sequence | |
Satzoda et al. | Multipart vehicle detection using symmetry-derived analysis and active learning | |
CA3068258C (en) | Rare instance classifiers | |
EP3289528B1 (en) | Filter specificity as training criterion for neural networks | |
US8773535B2 (en) | Adaptation for clear path detection using reliable local model updating | |
US20190122059A1 (en) | Signal light detection | |
US10964033B2 (en) | Decoupled motion models for object tracking | |
KR20210013216A (ko) | 멀티 레벨 타겟 분류 및 교통 표지 검출 방법 및 장치, 기기, 매체 | |
US8681222B2 (en) | Adaptation for clear path detection with additional classifiers | |
US11487295B2 (en) | Method and device for abstracting a data record | |
Niknejad et al. | Vision-based vehicle detection for nighttime with discriminately trained mixture of weighted deformable part models | |
US20180349744A1 (en) | Method and device for classifying an object for a vehicle | |
US11106214B2 (en) | Artificial neural network-based projection information recognition apparatus and method thereof | |
Tehrani et al. | Car detection at night using latent filters | |
CN112163521A (zh) | 一种车辆行驶行为的识别方法、装置及设备 | |
US9633283B1 (en) | Adaptive device and adaptive method for classifying objects with parallel architecture | |
CN114945953A (zh) | 自动驾驶的损失评估方法及装置 | |
CN114092818B (zh) | 语义分割方法、装置、电子设备及存储介质 | |
CN114972725B (zh) | 模型训练方法、可读介质和电子设备 | |
CN115457274A (zh) | 一种基于深度学习的车载视角遮挡目标检测方法及装置 | |
CN114973205A (zh) | 一种红绿灯跟踪方法、装置、及无人驾驶汽车 | |
EP4050510A1 (en) | Object information calculation method and system | |
Santoshi et al. | Pedestrian with direction detection using the combination of decision tree learning and SVM | |
KR102109841B1 (ko) | 주행 영상으로부터 차량을 검출하는 방법 및 장치 |
Legal Events
Date | Code | Title | Description |
---|---|---|---|
PB01 | Publication | ||
PB01 | Publication | ||
SE01 | Entry into force of request for substantive examination | ||
SE01 | Entry into force of request for substantive examination |