Motion planning method based on driving habit learning

A technology of motion planning and driving habits, applied in measuring devices, instruments, surveying and navigation, etc., can solve problems such as difficulty in describing road structure and traffic rules, mismatching, mismatching, etc., and achieve complex road and traffic conditions , Reduce data redundancy, reduce the effect of data transmission volume

Active Publication Date: 2018-12-21
SHANGHAI INT AUTOMOBILE CITY GRP CO LTD +2
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  • Summary
  • Abstract
  • Description
  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

[0010] (3) The preview point generated by the road centerline and its lateral offset is difficult to describe the complex road structure and traffic rules
[0015] (1) The trajectory of the steering wheel angle directly mapped by the image cannot be completely consistent with the actual trajectory, and there may be mis-matching and mismatching situations; Figure 4 As shown, there is a deviation between the predicted trajectory and the actual trajectory: the blue in the figure is the recorded real trajectory, and the green is the trajectory planned by this technology
[0016] (2) The preview point generated by the road centerline and its lateral offset is difficult to describe the complex road structure and traffic rules

Method used

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  • Motion planning method based on driving habit learning
  • Motion planning method based on driving habit learning
  • Motion planning method based on driving habit learning

Examples

Experimental program
Comparison scheme
Effect test

Embodiment 1

[0106] (1) Specific steps:

[0107] Since the collected artificial driving trajectory may be very long, the trajectory is segmented first, such as Figure 11 shown.

[0108] Perform a least-squares fit on each trajectory:

[0109] y=a 0 +a 1 x+a 2 x 2 +...+a m x m

[0110]

[0111] And find the median error of each trajectory:

[0112]

[0113]

[0114] The trajectory point least squares fitting is as follows Figure 12 shown.

[0115] In the same way, the center error of the head pointing, curvature, speed and other information of each trajectory can be calculated. Then, the weight value of each point on the trajectory σ xy Indicates the median error of the track point of the driving track A; σ θ Indicates the medium error of the head pointing of the driving trajectory A; σ k Indicates the median error of the curvature of the driving trajectory A; σ v Indicates the medium error of the speed of the driving trajectory A; ∝, β, γ, is a constant.

[01...

Embodiment 2

[0127] We can also obtain intersection and other information by processing the trajectory image. Such as Figure 16 As shown, the area with the highest trajectory density thermal value is the intersection area, and we can extract the intersection area through the image operation of expansion and edge detection, as shown in the red box in the figure.

[0128] Specific steps: take the boundary line as a cross section, and extract preview points of key positions such as intersections. The subsequent process is consistent with that in Example 1.

[0129] 3. Summary of key technical points of the present invention

[0130] (1) The method and process of collecting manual driving trajectories by crowdsourcing, assigning different weight values ​​to trajectories from different sources, and projecting the trajectory points onto the pixel plane, and obtaining the position distribution of the preview points through graphic processing.

[0131] (2) The definition of the preview point i...

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Abstract

The invention relates to a motion planning method based on driving habit learning. The method comprises the following steps: step 1, a cloud processing terminal collects manual driving trajectories, and through an image processing, extracts preview points with road traffic attributes; step 2, the cloud processing terminal delivers the preview points in front of a vehicle in real time according tothe state of a self-driving vehicle; step 3, the self-driving vehicle generates an algorithm based on a set path, generates an alternate trajectory cluster between the vehicle and each preview point,and selects an optimal path according to a cost function. Compared to the prior art, the motion planning method based on driving habit learning has the following advantages: using artificial driving trajectories to generate local planning paths and getting rid of dependence on high-precision maps; new attributes are added to the preview points, and the way of generating is changed, so that the path of the local planning is more in line with the driving habits of people.

Description

technical field [0001] The invention relates to the field of unmanned driving, in particular to a motion planning method based on driving habit learning. Background technique [0002] A driverless car is a smart car that senses the road environment through an on-board sensor system, automatically plans a driving route, and controls the vehicle to reach a predetermined target. Among them, local path planning is a bridge between environment perception and vehicle control, and the performance of the planning algorithm directly affects whether the unmanned vehicle can drive autonomously in complex traffic scenes. [0003] Nowadays, the structure of urban roads is becoming more and more complex, and there are more and more vehicles on the roads, so the problem of path planning is becoming more and more important. The current planning method needs to rely on high-precision maps. When the map information is incomplete, it cannot adapt to the intricate road structure and traffic ru...

Claims

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): G01C21/34
CPCG01C21/3415
Inventor 李霖陈海林顾磊敏林瑜周柳郑虎李枭金叶蒙高琼王亦科章品
Owner SHANGHAI INT AUTOMOBILE CITY GRP CO LTD
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