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A system for autonomous vehicles or assisted driving systems to plan routes

A technology for assisted driving and system planning, applied in vehicle position/route/height control, control/adjustment system, motor vehicles, etc., to achieve the effect of clear route selection

Active Publication Date: 2021-01-26
DALIAN NATIONALITIES UNIVERSITY
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  • Summary
  • Abstract
  • Description
  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

[0009] In order to solve the problem that non-dangerous pedestrians are considered in route planning by autonomous vehicles or assisted driving systems, the following solution is proposed: a system for planning routes by autonomous vehicles or assisted driving systems stores a plurality of instructions, and the instructions are suitable for being loaded by a processor And execute: Classify the pedestrians according to the magnetic relationship of the pedestrians in front of the car, and plan the route according to the classification of the pedestrians in front of the car

Method used

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  • A system for autonomous vehicles or assisted driving systems to plan routes
  • A system for autonomous vehicles or assisted driving systems to plan routes
  • A system for autonomous vehicles or assisted driving systems to plan routes

Examples

Experimental program
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Effect test

Embodiment 1

[0085] Classification of Repulsive Pedestrians

[0086] This example is aimed at the classification of repulsive pedestrians, and the simulation results are as follows figure 2 shown. figure 2Three frames of images in consecutive video frames and the pedestrian classification results of this frame are listed, and only the repulsive force probability of the pedestrian's magnetic force meets the determination requirements of the magnetic force pedestrian. In the video, three pedestrian targets are moving at a speed of about 1.2m / s, two of them are moving in the positive direction, and one is moving in the negative direction, and they all keep moving in a straight line without changing the moving speed. From frame 8 to frame 33, pedestrians B and C keep approaching. Until the 33rd frame, the repulsion probability of pedestrians B and C exceeds δ, and they are judged as repulsive pedestrians. Similarly, at the 72nd frame, the repulsion probability of pedestrians A and C excee...

Embodiment 2

[0088] Suction Pedestrian Classification Case

[0089] This example is aimed at the classification of repulsive pedestrians, and the simulation results are as follows image 3 shown. image 3 Three frames of images in consecutive video frames and the pedestrian classification results of this frame are listed, among which the magnetic probability of pedestrians and only the attractive probability meet the determination requirements of magnetic pedestrians. In the video, the three pedestrian targets move in the forward direction at a speed of about 1.2m / s, and they all keep moving in a straight line without changing the moving speed. From frame 11 to frame 39, pedestrians B and C keep walking together. Until the 39th frame, the suction probability of pedestrians B and C exceeds δ, and they are judged as suction pedestrians. In the subsequent 75th frame, pedestrians B and C maintain the determination result of the suction pedestrian.

Embodiment 3

[0091] Classification of non-magnetic pedestrians

[0092] This example is aimed at the classification of non-magnetic pedestrians. The simulation results are as follows Figure 4 shown. Figure 4 Three frames of images in consecutive video frames and the pedestrian classification results of this frame are listed, among which the magnetic probability of pedestrians and only the non-magnetic probability meet the determination requirements of magnetic pedestrians. In the video, the three pedestrian targets move in the negative direction at different speeds, and they all keep moving in a straight line without changing the moving speed. The speed of pedestrian A is about 0.5m / s, and the speed of pedestrians B and C is about 1.3m / s . At frame 9, pedestrian A's non-magnetic probability is calculated as 1, and he is judged as a non-magnetic pedestrian. In the subsequent 42nd and 103th frames, pedestrian A maintains the determination result of non-magnetic pedestrian.

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Abstract

A system for planning a route by an autonomous vehicle or an assisted driving system belongs to the field of moving target tracking processing. In order to solve the problem that an autonomous vehicle or an assisted driving system considers non-dangerous pedestrians in route planning, a plurality of instructions are stored, and the instructions are suitable for being processed by a processor. Load and execute: classify pedestrians according to their magnetic relationship in front of the vehicle, and plan routes based on the classification of pedestrians in front of the vehicle. The driving route is safer and more comprehensive, and the route is determined by classification, which can make the route selection clearer.

Description

technical field [0001] The invention belongs to the field of tracking and processing of moving objects, in particular to a classification method for distinguishing potential danger levels of road pedestrians by using a magnetic force model. Background technique [0002] Moving target tracking processing technology is an important research topic in the field of machine vision. With the application of autonomous vehicles and assisted driving systems, how to reasonably use target tracking processing technology to protect the safety of pedestrians and vehicles is also a popular research direction. . [0003] At present, in the case of only using vehicle cameras, classifying pedestrians by analyzing information such as their historical moving trajectories and moving speeds is a main way to use target tracking processing technology to protect pedestrians and vehicles. Firstly, the pedestrian's moving trajectory and moving speed are analyzed to calculate the probability that pedes...

Claims

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Application Information

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Patent Type & Authority Patents(China)
IPC IPC(8): G05D1/02
CPCG05D1/0214G05D1/0221G05D1/0223G05D1/0253G05D1/0285
Inventor 杨大伟毛琳许烨豪
Owner DALIAN NATIONALITIES UNIVERSITY