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Autonomous vehicle or auxiliary driving system route planning system

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: 2018-11-13
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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  • Autonomous vehicle or auxiliary driving system route planning system
  • Autonomous vehicle or auxiliary driving system route planning system
  • Autonomous vehicle or auxiliary driving system route planning system

Examples

Experimental program
Comparison scheme
Effect test

Embodiment 1

[0085] Repulsion pedestrian classification situation

[0086] This example is aimed at the classification of repulsive pedestrians, and the simulation results are as follows figure 2 Shown. figure 2 Lists the three images in the continuous video frame and the pedestrian classification result of this frame. Among them, the magnetic probability of pedestrians only meets the requirement of magnetic pedestrians. In the video, three pedestrian targets move at a speed of about 1.2m / s, of which two pedestrians move in the positive direction and one pedestrian moves 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, when the repulsion probability of pedestrians A and C exceeds δ, they are judged to be repulsive p...

Embodiment 2

[0088] Classification of suction pedestrians

[0089] This example is aimed at the classification of repulsive pedestrians, and the simulation results are as follows image 3 Shown. image 3 The three images in the continuous video frame and the pedestrian classification result of this frame are listed. Among them, the magnetic probability of pedestrians only meets the requirements of magnetic pedestrians. In the video, the three pedestrian targets move in the positive direction at a speed of about 1.2m / s, and 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. At the next 75th frame, pedestrians B and C maintain the judgment 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 The three images in the continuous video frame and the pedestrian classification results of this frame are listed. Among them, the magnetic probability of the pedestrian only has the probability of no magnetic force to meet the determination requirements of the magnetic pedestrian. In the video, the three pedestrian targets move in the negative direction at different speeds, and 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. . In the ninth frame, the non-magnetic probability of pedestrian A is calculated to be 1, and it is determined as a non-magnetic pedestrian. In the subsequent 42nd and 103rd frames, pedestrian A maintains the determination result of non-magnetic pe...

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Abstract

An autonomous vehicle or auxiliary driving system route planning system belongs to the moving target tracking processing field. In order to solve a problem that an autonomous vehicle or an auxiliary driving system considers non-dangerous pedestrians in route planning, multiple instructions are stored. The instructions are loaded and are executed by a processor. The instructions are characterized by classifying the pedestrians according to the magnetic force relations of the pedestrians in front of the vehicle; and based on the classification of the pedestrians in front of the vehicle, planninga route. In the scheme of the invention, the classification is performed according to the magnetic force relations of the pedestrians, and the route is planned according to the classification so thatan automatic driving route has safety and comprehensiveness; and the route is determined through classification so that route selection is clear.

Description

Technical field [0001] The invention belongs to the field of moving target tracking and processing, and specifically is a classification method that uses a magnetic model to distinguish the potential danger of road pedestrians. 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 popular research direction. . [0003] At present, when only the vehicle-mounted camera is used, classifying pedestrians by analyzing the historical movement trajectory and speed of the pedestrians is a major way to protect the safety of pedestrians and vehicles by using target tracking processing technology. First, analyze the pedestrian's moving trajectory and speed to calculate the probability of pedestrian collision with th...

Claims

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Application Information

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