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Active driving method based on multi-information fusion

A multi-information fusion, active driving technology, applied in the field of active driving, can solve problems such as detection and identification errors, and achieve the effect of improving efficiency, reducing range and ensuring safety

Active Publication Date: 2019-08-27
DALIAN ROILAND SCI & TECH CO LTD
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  • Summary
  • Abstract
  • Description
  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

However, there are often certain errors in the detection and identification of these basic road condition information, because autonomous driving vehicles are still in the stage of experimentation and exploration, so in order to better help driving control decisions, it is necessary to provide more abundant, reliable and highly credible information

Method used

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  • Active driving method based on multi-information fusion
  • Active driving method based on multi-information fusion
  • Active driving method based on multi-information fusion

Examples

Experimental program
Comparison scheme
Effect test

Embodiment 1

[0045] The active driving method based on multi-information fusion is characterized in that it is realized through the following steps:

[0046] S1: During the driving process of the car, use visual sensors to detect pedestrians in the surrounding environment, and at the same time detect traffic lights and surrounding vehicles;

[0047] S2: When a pedestrian is detected, judge whether the pedestrian is a traffic control person, if it is a traffic control person, recognize its action, judge how to drive according to the traffic control person's action, and send the corresponding driving strategy to the driving control Module; Here, the Hidden Markov Model combined with Viterbi Algorithm is used to detect traffic police actions, and other methods can also be used to judge how to drive to stop, slow down, or pass normally based on traffic police actions.

[0048] S3: The visual sensor detects the ground traffic signs, obtains the sign indication strategy and sends it to the drivi...

Embodiment 2

[0053] The difference between this embodiment and embodiment 1 is that in step S3, the detection method of traffic lights is specifically realized through the following steps:

[0054] The method for identifying traffic control personnel applied in active driving technology is realized through the following steps:

[0055] Step 1: Collect a large number of fluorescent vest image samples and negative samples of pedestrians and traffic control personnel;

[0056] Step 2: Perform feature statistics through adboost, train offline to obtain the classifier for pedestrian detection and fluorescent vest detection, and obtain the color histogram template of fluorescent vest through rbf neural network training;

[0057] Step 3: When the active driving system detects the presence of a pedestrian target through the pedestrian detection module, the upper, lower, left, and right sides of the pedestrian's torso deviate from the 1 / 2 interval, and the color histogram template matching of the f...

Embodiment 3

[0063] This embodiment is different from Embodiments 1 and 2 in that the detection method for traffic lights in step S3 is specifically implemented through the following steps:

[0064] D1: Use the on-board GPS to perform "coarse" positioning of the vehicle body position to obtain the vehicle body position information; the position obtained this time often has a certain error, which may be several meters at most; therefore, the following two corrections are required.

[0065] D2: While the GPS obtains the position information of the vehicle body, the on-board visual sensor checks the lane line on the road surface, and determines the lane where the vehicle body is located through the coordinate position relationship of the lane line in the visual scene; uses the information of the vehicle body lane to search and compare through the map Correct the result of GPS positioning for the first time by means of the method;

[0066] D3: The vehicle-mounted radar system detects the road ...

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Abstract

The active driving method based on multi-information fusion, when the car is moving, uses the visual sensor to detect the pedestrians in the surrounding environment, and at the same time detects the traffic lights and surrounding vehicles; when the pedestrian is detected, whether the pedestrian is traffic The control personnel make judgments, and if they are traffic control personnel, recognize their actions; the visual sensor detects the ground traffic signs, obtains the sign instruction strategy and sends it to the driving control module; the vehicle-mounted radar system and the vehicle-mounted vision sensor jointly detect the vehicles in front and on both sides and its behavior, judge the judgment of the surrounding vehicles on the current road conditions through its behavior, and send the driving state of the surrounding vehicles to the driving control module; the present invention not only considers the relatively fixed detection target information of traffic lights and road marker lights, but also considers External dynamic information such as traffic police command actions and the behavior of surrounding vehicles make active driving more flexible and adaptable to the environment.

Description

technical field [0001] The invention belongs to the technical field of active driving, in particular to an active driving method based on multi-information fusion. Background technique [0002] At present, from the field of assisted driving of automobiles to the related technologies of active driving of automobiles, it is basically used to identify road condition information, including traffic lights, zebra crossings, stop lines and other information, to judge whether it is necessary to slow down, stop and other operations. There are often certain errors in the detection and identification of these basic road condition information, because autonomous driving vehicles are still in the stage of experimentation and exploration, so in order to better help driving control decisions, it is necessary to provide more abundant, reliable and highly credible information . In particular, traffic safety is related to the safety of drivers themselves and others, which is even more ambigu...

Claims

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Application Information

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Patent Type & Authority Patents(China)
IPC IPC(8): G06K9/62
CPCG06F18/25G06F18/254G06F18/214
Inventor 田雨农吴子章周秀田陆振波于维双
Owner DALIAN ROILAND SCI & TECH CO LTD