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Method for planning route for autonomous vehicle or auxiliary driving system

A technology for assisted driving and system planning, which is applied in the traffic control system, collision avoidance system, traffic control system and other directions of road vehicles to achieve the effect of clear route selection

Active Publication Date: 2019-01-04
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 scheme is proposed: a method for autonomous vehicles or assisted driving systems to plan routes, classify pedestrians according to the magnetic relationship of pedestrians in front of the vehicle, and Classification of pedestrians in front of vehicles for route planning

Method used

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  • Method for planning route for autonomous vehicle or auxiliary driving system
  • Method for planning route for autonomous vehicle or auxiliary driving system
  • Method for planning route for autonomous vehicle or auxiliary driving system

Examples

Experimental program
Comparison scheme
Effect test

Embodiment 1

[0084] Classification of repulsive pedestrians

[0085] This example is aimed at the classification of repulsive pedestrians, and the simulation results are as follows figure 2 shown. figure 2 Three 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 exce...

Embodiment 2

[0087]Suction Pedestrian Classification Case

[0088] 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

[0090] Classification of non-magnetic pedestrians

[0091] 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

The invention discloses a method for planning a route for an autonomous vehicle or an auxiliary driving system. The method belongs to the field of moving object tracking processing. In order to solvea problem that the autonomous vehicle or the auxiliary driving system considers non-dangerous pedestrians in route planning, the method is characterized by classifying pedestrians according to the magnetic force relations of the pedestrians in front of the vehicle, and planning a route based on the classification of the pedestrians in front of the vehicle. According to the method disclosed by theinvention, the classification is performed according to the magnetic force relations of the pedestrians, and the route is planned according to the classification, so that an automatic driving route has the advantages of safety and comprehensiveness; and the route is determined through classification so that route selection is clearer.

Description

technical field [0001] The invention belongs to the field of moving target tracking processing, and specifically relates 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, and 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 hot research direction now . [0003] At present, when only on-board cameras are used, classifying pedestrians by analyzing information such as their historical trajectory and moving speed is a major way to use target tracking processing technology to protect the safety of pedestrians and vehicles. Firstly, the pedestrian's trajectory and speed are analyzed to calculate the probability that the pedestrian...

Claims

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Application Information

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IPC IPC(8): G08G1/16
CPCG08G1/166
Inventor 毛琳杨大伟许烨豪
Owner DALIAN NATIONALITIES UNIVERSITY