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Intelligent network connection automobile public road dangerous scene extraction method and device, dangerous scene construction method and device and computing equipment

An extraction method and intelligent network technology, applied in the field of driving scene construction, can solve the difficulties of driving assistance functions, development of automatic driving functions, unmanned driving, user property and personal safety injuries, dangerous scene recognition capabilities, planning capabilities, and decision-making capabilities , Insufficient executive ability and other issues, to reduce misjudgment

Pending Publication Date: 2022-05-24
CHINA FIRST AUTOMOBILE
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  • Summary
  • Abstract
  • Description
  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

[0002] With the popularization of intelligent networked vehicles, more and more people use, trust, and rely on intelligent networked systems. What follows is that in some dangerous scenarios, intelligent networked vehicles cannot make timely and accurate judgments and solutions , causing harm to the user's property and personal safety
[0003] When the intelligent network connection system (such as super cruise system, high-speed automatic driving system, low-speed automatic driving system, and valet parking system) controls the car to drive on public roads, due to the intelligent network connection system's ability to identify dangerous scenes, planning capabilities, and decision-making Insufficient ability and executive ability will lead to traffic accidents
However, the existing simulation and test cases are usually relatively simple, which brings the following disadvantages:
[0006] c) Regardless of dangerous scenarios, it is difficult to develop from existing driving assistance functions to automatic driving functions and unmanned driving functions;
[0007] d) Existing public road scene collection systems, methods and processes cannot automatically extract dangerous scenes;
[0008] e) Existing public road scene collection systems, methods and processes cannot convert dangerous scenes into test cases

Method used

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  • Intelligent network connection automobile public road dangerous scene extraction method and device, dangerous scene construction method and device and computing equipment
  • Intelligent network connection automobile public road dangerous scene extraction method and device, dangerous scene construction method and device and computing equipment
  • Intelligent network connection automobile public road dangerous scene extraction method and device, dangerous scene construction method and device and computing equipment

Examples

Experimental program
Comparison scheme
Effect test

Embodiment approach 1

[0091] Embodiment 1. See figure 1 Illustrating this embodiment, the method for extracting a dangerous scene on a public road for an intelligent networked vehicle described in this embodiment includes the following steps:

[0092] Data collection step: collect the video information obtained by the vehicle loaded with the data collection device during the driving process, and the driving information and status information of the vehicle as scene data to be processed;

[0093] The data co-frequency processing step is to adjust the data of different frequencies in the scene data to be processed to the same frequency;

[0094] Dangerous scene trigger condition judgment step: Read the information in the scene data in chronological order to determine whether the dangerous scene trigger conditions are met. If so, start the dangerous scene extraction step; if not, close the dangerous scene extraction step and start the dangerous scene. Scene storage steps;

[0095] Dangerous scene ex...

Embodiment approach 2

[0107] Embodiment 2. This embodiment is a further limitation of the method for extracting dangerous scenes on public roads of intelligent networked vehicles described in Embodiment 1. In this embodiment, the data collection device includes a video collection device, a target object recognition equipment, CAN data acquisition equipment and GPS positioning equipment, including:

[0108] The video collection device is used to collect video information in front of the vehicle;

[0109] The target object identification device is used to identify the target object type in the video information collected by the video capture device, and to mark the target object;

[0110]The CAN data acquisition device is used to collect and store the marked video information of the target; it is also used to collect the information obtained by the vehicle lidar system; it is also used to collect the information obtained by the vehicle millimeter wave system; it is also used to collect the vehicle T...

Embodiment approach 3

[0122] Embodiment 3. This embodiment is a further limitation of the method for extracting a dangerous scene on a public road of an intelligent networked vehicle described in Embodiment 1 or 2. The triggering condition of the dangerous scene described in this embodiment is based on the driving state of the vehicle. Different conditions are set, specifically:

[0123] The dangerous scene trigger condition refers to meeting any one of the following conditions:

[0124] Condition 1: c≤d 0 And v≥20km / h, where D is the distance between the vehicle and the preceding vehicle; where d 0 In order to judge whether it is necessary to detect the critical distance of the target vehicle, v is the speed of the test vehicle, and c is the distance between the test vehicle and the target vehicle in front;

[0125] Condition 2: d 0 1 and v≥36km / h; d 1 Indicates the critical distance for judging whether it is necessary to measure distance or relative speed;

[0126] Condition 3: d 1 max time...

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Abstract

The invention discloses an intelligent network connection automobile public road dangerous scene extraction method and device, a dangerous scene construction method and device and computing equipment, and relates to the construction technology of driving scenes. The objective of the invention is to provide an extraction technology suitable for automobiles of various models and dangerous scenes of various types. The dangerous scene extraction method comprises the following steps: acquiring scene data through data acquisition equipment of a vehicle, performing same-frequency processing on the data, and triggering acquisition according to a dangerous scene triggering condition to obtain dangerous scene data to form a dangerous scene file so as to realize dangerous scene extraction. The dangerous scene construction method implemented on the basis of the method comprises the steps of data marking, lane model construction, target vehicle model construction, vehicle model construction, weather model construction and final completion of dangerous scene case construction. The dangerous scene use case obtained by the method is suitable for the technical fields of intelligent network connection system design scheme reference, real vehicle-in-the-loop simulation, intelligent network connection automobile field performance test and the like.

Description

technical field [0001] The invention relates to the technical field of intelligent networked vehicles, and in particular, to the construction technology of driving scenes. Background technique [0002] With the popularization of ICVs, more and more people use, trust, and rely on ICVs. What follows is that in some dangerous scenarios, ICVs cannot make timely and accurate judgments and actions. , causing damage to the user's property and personal safety. [0003] When the intelligent networked system (such as super cruise system, high-speed automatic driving system, low-speed automatic driving system, valet parking system) controls the car on public roads, due to the intelligent networked system's ability to identify, plan, and make decisions on dangerous scenes The lack of ability and execution ability will lead to the occurrence of traffic accidents. The existing simulation and test cases are usually relatively simple, which brings the following disadvantages: [0004] a)...

Claims

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Application Information

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IPC IPC(8): G06F16/14G06F16/9537G06V20/58G06V20/40G01S19/39G01S19/42G01S17/86
CPCG06F16/148G06F16/156G06F16/9537G01S19/393G01S19/42G01S17/86
Inventor 骆实郑建明覃斌金鉴吴南洋张宇飞张建军刘迪
Owner CHINA FIRST AUTOMOBILE
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