Patent Description:
In general, platooning refers to driving a group of multiple vehicles on roads while sharing traveling information with each other and considering external environments.

In order to conduct stable platooning, it is crucial to maintain appropriate distances between platooning vehicles and to control rear vehicles to follow the traveling trajectory of front vehicles.

An autonomous driving system may perform reinforcement learning regarding platooning such that autonomous driving vehicles take optimal actions during platooning.

The reinforcement learning is one of machine learning methods for learning which action, if taken, would give an optimal result in the current state through trial and error. A reward is given each time an action is taken, and the learning proceeds so as to maximize such rewards.

The above descriptions regarding background technologies have been made only to help understanding of the background of the present disclosure, and are not to be deemed by those skilled in the art to correspond to already-known prior arts.

<CIT> discloses a method and a device for automatic car following. In the document the method is described to comprise detecting driving parameters of an automobile, wherein the driving parameters comprise a car following distance between the automobile and a front automobile and an included angle between driving directions of the automobile and the front automobile, determining, according to the driving parameters, first action control parameters by a reinforcement learning method, wherein the first action control parameters comprise a reference force value of an accelerator or a brake, a reference direction of steering wheel rotation and a reference angle, and the reinforcement learning method indicates that the automobile obtains reference actions used for the automobile car following through learning selection, and controlling, according to the first action control parameters, the automobile to complete the automatic car following actions.

<CIT> discloses that a target motor vehicle is established for a platoon of motor vehicles running on a road, and motion information of the target motor vehicle is transmitted to the motor vehicles which follow the target motor vehicle through inter-vehicular communications.

Accordingly, it is an aspect of the present disclosure to perform reinforcement learning by using a control point regarding the traveling trajectory of a front vehicle and image information during platooning such that the pertinent vehicle follows the traveling trajectory of the front vehicle stably and efficiently.

The present disclosure is advantageous in that reinforcement learning is performed by using a control point regarding the traveling trajectory of a front vehicle and image information during platooning such that the pertinent vehicle follows the traveling trajectory of the front vehicle stably and efficiently.

The above and other aspects, features, and advantages of the present disclosure will be more apparent from the following detailed description taken in conjunction with the accompanying drawings, in which:.

Hereinafter, embodiments disclosed in the present specification will be described in detail with reference to the accompanying drawings, and the same or similar elements are given the same and similar reference numerals, so duplicate descriptions thereof will be omitted.

Terms including an ordinal number such as "first", "second", or the like may be used to describe various elements, but the elements are not limited to the terms. The above terms are used only for the purpose of distinguishing one element from another element.

A singular expression may include a plural expression unless they are definitely different in a context.

As used herein, the expression "include" or "have" are intended to specify the existence of mentioned features, numbers, steps, operations, elements, components, or combinations thereof, and should be construed as not precluding the possible existence or addition of one or more other features, numbers, steps, operations, elements, components, or combinations thereof.

<FIG> is a block diagram illustrating an exemplary configuration of a platooning control device according to an embodiment of the present disclosure.

As illustrated in <FIG>, the platooning control device may include a learning device <NUM>, a compensation determination unit <NUM>, and an inferring neural network device <NUM>.

The platooning control device according to the present invention performs reinforcement learning by using the traveling trajectory of a front vehicle and image information during platooning such that the pertinent vehicle is controlled to follow the traveling trajectory of the front vehicle stably and efficiently.

Respective components of the platooning control device will now be described.

The learning device <NUM> may correspond to an agent which is the target of reinforcement learning regarding platooning.

The learning device <NUM> may perform reinforcement learning through a neural network on the basis of image information and a feedback signal, and may output a steering control signal, a braking control signal, and an acceleration control signal such that the pertinent vehicle is controlled to follow the traveling trajectory of the front vehicle according to the result of reinforcement learning.

The image information includes front image information output from a front camera of the pertinent vehicle, and rear image information output from a rear camera of the front vehicle. The front image information and the rear image information may correspond to platooning-related states, and may reflect the characteristics of the real road along which the pertinent vehicle is traveling. Accordingly, the learning device <NUM> may perform reinforcement learning through front image information and rear image information corresponding to the current platooning state such that, even in an exceptional platooning situation, the pertinent vehicle is controlled to safely follow the traveling trajectory of the front vehicle.

The feedback signal corresponds to a reward regarding reinforcement learning. More particularly, the feedback signal indicates one of positive feedback and negative feedback regarding whether the pertinent vehicle follows the traveling trajectory of the front car. Accordingly, the learning device <NUM> may change and modify the policy regarding reinforcement learning according to the feedback signal.

The steering control signal, the braking control signal, and the acceleration control signal may correspond to actions regarding reinforcement learning, and may be generated to perform steering control, braking control, and acceleration control of the pertinent vehicle.

More particularly, the learning device <NUM> may transfer a control signal necessary for traveling of the pertinent vehicle to traveling-related controllers for steering, braking, driving, and the like, thereby controlling the traveling state of the pertinent vehicle.

For example, the learning device <NUM> may output a steering control signal to a steering controller (not illustrated) configured to adjust the rotational angle of the steering wheel, for example, thereby controlling the steering angle of the pertinent vehicle, and may output a braking control signal to a braking controller (not illustrated) configured to adjust the amount of hydraulic braking or to a motor controller (not illustrated) configured to adjust the amount of regenerative braking, thereby controlling the amount of braking of the pertinent vehicle. In addition, the learning device <NUM> may output an acceleration control signal to a powertrain controller (not illustrated) configured to adjust the output torque of the electric motor or engine, thereby controlling the acceleration of the pertinent vehicle.

The compensation determination unit <NUM> may generate a feedback signal corresponding to a reward regarding reinforcement learning on the basis of a steering control signal, a braking control signal, and an acceleration control signal corresponding to actions regarding reinforcement learning.

In addition, compensation determination unit <NUM> may receive the coordinate of a control point regarding the traveling trajectory of the front vehicle from the front vehicle, and may compare the coordinate of the pertinent vehicle with the coordinate of the control point, thereby generating a feedback signal.

In the present embodiment, the control point is defined as a feature point for controlling the shape of a spline curve corresponding to the traveling trajectory of the front vehicle.

The spline curve may correspond to a smooth curve for expressing the traveling trajectory of the front vehicle by using a spline function. Depending on the embodiment, the spline curve may correspond to one of an interpolating spline curve extending through control points, or an approximating spline curve not extending through intermediate control points. A different configuration may be made, depending on the embodiment, regarding whether the approximating spline curve extends through the starting control point and the ending control point.

A method for operating the compensation determination unit <NUM> so as to generate a feedback signal, assuming that the spline curve corresponding to the traveling trajectory of the front vehicle corresponds to an approximating spline curve, will now be described.

When the coordinate of the pertinent vehicle is on the outside of the traveling lane in comparison with the coordinate of a control point, the compensation determination unit <NUM> may determine that the pertinent vehicle has deviated from the traveling trajectory of the front vehicle toward the control point, and may output a feedback signal corresponding to negative feedback. The traveling lane refers to the lane along which the pertinent vehicle is currently traveling.

In addition, when the coordinate of the pertinent vehicle is out of a preconfigured danger distance from the coordinate of the control point, the compensation determination unit <NUM> may determine that the pertinent vehicle has deviated from the traveling trajectory of the front vehicle in the opposite direction to the control point, and may output a feedback signal corresponding to negative feedback.

When negative feedback is input as a result of coordinate comparison between the pertinent vehicle and the control point, the learning device <NUM> may control the amount of braking of the pertinent vehicle to increase through a braking control signal, and may control the steering angle of the pertinent vehicle to follow the traveling trajectory of the front vehicle through a steering control signal.

To the contrary, if the coordinate of the pertinent vehicle is on the inside of the traveling lane in comparison with the control point coordinate, and if the coordinate of the pertinent vehicle is within the preconfigured danger distance from the coordinate of the control point, the compensation determination unit <NUM> may determine the pertinent vehicle stably follows the traveling trajectory of the front vehicle. In this case, the compensation determination unit <NUM> may output a feedback signal corresponding to positive feedback.

Accordingly, the compensation determination unit <NUM> provides the learning device <NUM> with feedback regarding whether the pertinent vehicle follows the traveling trajectory of the front vehicle on the basis of the coordinate of a control point regarding the traveling trajectory of the front vehicle, thereby reducing the data size and the amount of calculation regarding the traveling trajectory of the front vehicle.

In addition, the compensation determination unit <NUM> may generate a feedback signal according to whether the radio signal strength (for example, received signal strength indication (RSSI)) of a radio signal received from the front vehicle is included in a preconfigured range. The preconfigured range regarding the RSSI may be variously configured depending on the embodiment.

The RSSI of the radio signal may indicate the inter-vehicle distance between the pertinent vehicle and the front vehicle. For example, the compensation determination unit <NUM> may determine that the higher the RSSI, the shorter the inter-vehicle distance between the pertinent vehicle and the front vehicle.

If the RSSI of the radio signal is included in the preconfigured range, the compensation determination unit <NUM> may determine that the pertinent vehicle stably maintains the inter-vehicle distance from the front vehicle, and may output a feedback signal corresponding to positive feedback.

To the contrary, if the RSSI of the radio signal is not included in the preconfigured range, the compensation determination unit <NUM> may output a feedback signal corresponding to negative feedback.

More particularly, if the RSSI of the radio signal is higher than the upper threshold of the preconfigured range, the compensation determination unit <NUM> may determine that the inter-vehicle distance between the pertinent vehicle and the front vehicle is short, and may output a feedback signal corresponding to negative feedback. The learning device <NUM> may control the amount of braking of the pertinent vehicle to increase through a braking control signal.

To the contrary, if the RSSI of the radio signal is lower than the lower threshold of the preconfigured range, the compensation determination unit <NUM> may determine that the inter-vehicle distance between the pertinent vehicle and the front vehicle is long, and may output a feedback signal corresponding to negative feedback. The learning device <NUM> may control the acceleration of the pertinent vehicle to increase through an acceleration control signal.

Accordingly, the compensation determination unit <NUM> according to the present embodiment may provide the learning device <NUM> with feedback regarding whether the inter-vehicle distance between the pertinent vehicle and the front vehicle is stably maintained through the RSSI of the radio signal, thereby controlling the learning device <NUM> to learn acceleration and braking characteristics regarding the distance from the front vehicle.

In connection with implementation, the compensation determination unit <NUM> corresponds to a controller dedicated to feedback regarding reinforcement learning of the learning device <NUM>, and to this end may include a communication device configured to communicate with another controller or sensor, a memory configured to store an operating system, logic commands, input/output information, and the like, and at least one processor configured to perform determination, calculation, determination, and the like necessary for corresponding function control.

The inferring neural network device <NUM> may periodically update a parameter regarding a neural network included in the learning device <NUM> after stabilization of reinforcement learning regarding platooning performed by the learning device <NUM>.

The inferring neural network device <NUM> may receive front image information and rear image information and may control the pertinent vehicle so as to follow the traveling trajectory of the front vehicle, on the basis of the updated parameter, without feedback from the compensation determination unit <NUM>. The inferring neural network device <NUM> may output a steering control signal, a braking control signal, and an acceleration control signal as in the case of the learning device <NUM> such that the pertinent vehicle is controlled to follow the traveling trajectory of the front vehicle.

Accordingly, the inferring neural network device <NUM> may perform steering control, braking control, and acceleration control of the pertinent vehicle only through image information without additional reinforcement learning after stabilization of reinforcement learning regarding platooning, thereby reducing the amount of calculation regarding reinforcement learning of the platooning control device.

<FIG> is a sequence diagram illustrating a process of exchanging information between a front vehicle and a pertinent vehicle during platooning according to an embodiment of the present disclosure.

In <FIG>, the pertinent vehicle R has the configuration described with reference to <FIG>, and the front vehicle F is assumed to be a vehicle which is platooning together with the pertinent vehicle R, and which support communication with the pertinent vehicle R directly or through an infrastructure or the like.

The front vehicle F may down-scale and compress image information output from a rear camera, thereby generating rear image information (S101), and the pertinent vehicle R may down-scale and compress image information output from a front camera, thereby generating front image information (S103).

The front vehicle F may transmit rear image information and a radio signal to the pertinent vehicle R, and the pertinent vehicle R may transmit front image information and a radio signal to the front vehicle F (S105).

The front vehicle F may restore the received front image information and may measure the RSSI of the radio signal received from the pertinent vehicle R (S107). Likewise, the pertinent vehicle R may restore the rear image information and may measure the RSSI of the radio signal received from the front vehicle F (S109).

The front vehicle F may generate a vision-based trajectory through image information output from the rear camera and the front image information received from the pertinent vehicle R (S111), and may generate the coordinate of a control point according to the vision-based trajectory (S113).

The front vehicle F may transmit the coordinate of the control point to the pertinent vehicle R (S115).

The pertinent vehicle R may conduct feedback regarding reinforcement learning on the basis of the coordinate of the control point and a measurement value regarding the RSSI of the radio signal (S117), and may perform steering control, braking control, and acceleration control of the pertinent vehicle R according to the feedback, thereby following the traveling trajectory of the front vehicle F (S119).

<FIG> illustrates front and rear images of vehicles which are platooning according to an embodiment of the present disclosure.

Referring to <FIG>, a first front vehicle F<<NUM>> is positioned in front of a pertinent vehicle R, and a second front vehicle F<<NUM>> is positioned in front of the first front vehicle F<<NUM>>. A front image FV may be captured through the front camera of each vehicle, and a rear image RV may be captured through the rear camera of each vehicle.

The learning device <NUM> of the pertinent vehicle R determines an overlapping part between a rear image RV of the first front vehicle F<<NUM>> and a front image FV captured by the pertinent vehicle R on the basis of front image information of the pertinent vehicle R and rear image information of the first front vehicle F<<NUM>>, and uses the determined degree of overlapping between the rear image RV and the front image V as learning data regarding reinforcement learning.

For example, the learning device <NUM> may determine the degree of overlapping on the basis of lanes, shapes marked on road surfaces (for example, road surface signs), feature point extraction, and the like, but this is only an example and is not limiting in any manner.

<FIG> illustrates an exemplary process for generating a control point regarding the traveling trajectory of a front vehicle according to an embodiment of the present disclosure.

Referring to <FIG>, the front vehicle F may generate a vision-based trajectory on the basis of rear image information output through a rear camera and front image information received from a rear vehicle. The front vehicle F may then generate coordinates of a control point regarding the traveling trajectory of the front vehicle through the vision-based trajectory.

<FIG> is a flowchart illustrating a process of performing feedback regarding reinforcement learning on the basis of a control point regarding the traveling trajectory of a front vehicle according to an embodiment of the present disclosure.

It will be assumed in <FIG> that the learning device <NUM> is controlling the pertinent vehicle so as to follow the traveling trajectory of the front vehicle as a result of reinforcement learning performed on the basis of image information and a feedback signal.

The compensation determination unit <NUM> receives the coordinate of a control point regarding the traveling trajectory of the front vehicle from the front vehicle (S201). The platooning control device may generate the traveling trajectory of the pertinent vehicle through the coordinate of the control point regarding the traveling trajectory of the front vehicle (S203).

The compensation determination unit <NUM> compares the coordinate of the pertinent vehicle with the coordinate of the control point (S205, S211), and generates a feedback signal according to the result of comparison (S207, S213).

The compensation determination unit <NUM> may first determine whether the coordinate of the pertinent vehicle is on the outside of the traveling lane in comparison with the coordinate of the control point (S205).

When the coordinate of the pertinent vehicle is on the outside of the traveling lane in comparison with the coordinate of the control point (YES in S205), the compensation determination unit <NUM> may output a feedback signal corresponding to negative feedback. The learning device <NUM> may control the amount of braking of the pertinent vehicle to increase according to the negative feedback and may control the steering angle of the pertinent vehicle (S209).

When the coordinate of the pertinent vehicle is on the inside of the traveling lane in comparison with the coordinate of the control point (NO in S205), the compensation determination unit <NUM> may determine whether the coordinate of the pertinent vehicle is outside a preconfigured danger distance from the coordinate of the control point (S211).

When the coordinate of the pertinent vehicle is outside the preconfigured danger distance from the coordinate of the control point (YES in S211), the compensation determination unit <NUM> may output a feedback signal corresponding to negative feedback (S207). The learning device <NUM> may control the amount of braking of the pertinent vehicle to increase according to the negative feedback and may control the steering angle of the pertinent vehicle (S209).

When the coordinate of the pertinent vehicle is within the preconfigured danger distance from the coordinate of the control point (NO in S211), the compensation determination unit <NUM> may output a feedback signal corresponding to positive feedback (S213).

<FIG> illustrates a process of performing feedback according to the coordinate of the pertinent vehicle during platooning according to an embodiment of the present disclosure.

Referring to the left of <FIG>, first to fourth control points <<NUM>:<NUM>> regarding the traveling trajectory of the front vehicle F are illustrated.

The center of <FIG> corresponds to a case in which the coordinate of the pertinent vehicle R is on the outside of the traveling lane in comparison with the coordinate of the second control point <<NUM>>. The compensation determination unit <NUM> may then output a feedback signal corresponding to negative feedback.

The right of <FIG> corresponds to a case in which the coordinate of the pertinent vehicle R is on the inside of the traveling lane in comparison with the coordinate of the second control point <<NUM>>, and is within a danger distance from the coordinate of the second control point <<NUM>>. The compensation determination unit <NUM> may then output a feedback signal corresponding to positive feedback.

<FIG> is a flowchart illustrating a process of performing feedback regarding reinforcement learning on the basis of RSSI of a radio signal received from the front vehicle according to an embodiment of the present disclosure.

The compensation determination unit <NUM> may receive a radio signal from the front vehicle (S301), and may measure the RSSI of the radio signal (S303).

The compensation determination unit <NUM> may determine whether the RSSI of the radio signal is included in a preconfigured range (S305, S311), and may output a feedback signal corresponding to one of positive feedback and negative feedback according to the result of determination (S307, S313, S317).

The compensation determination unit <NUM> may first determine whether the RSSI of the radio signal is lower than the upper threshold of the preconfigured range (S305).

When the RSSI is higher than the upper threshold of the preconfigured range (NO in S305), the compensation determination unit <NUM> may output a feedback signal corresponding to negative feedback (S307). The learning device <NUM> may control the amount of braking of the pertinent vehicle to increase according to the negative feedback (S309).

When the RSSI is lower than the upper threshold of the preconfigured range (YES in S305), the compensation determination unit <NUM> may determine whether the RSSI is higher than the lower threshold of the preconfigured range (S311).

When the RSSI is lower than the lower threshold of the preconfigured range (NO in S311), the compensation determination unit <NUM> may output a feedback signal corresponding to negative feedback (S313). The learning device <NUM> may control the acceleration of the pertinent vehicle to increase according to the negative feedback (S315).

When the RSSI is higher than the lower threshold of the preconfigured range (YES in S311), the compensation determination unit <NUM> may output a feedback signal corresponding to positive feedback (S317).

Claim 1:
A platooning control device of a pertinent vehicle (R) comprising:
a learning device (<NUM>) configured to perform reinforcement learning on the basis of image information and a reward and to control the pertinent vehicle (R) so as to follow a traveling trajectory of a front vehicle (F) according to a result of the reinforcement learning; and
a compensation determination unit (<NUM>) configured to receive a coordinate of a control point regarding the traveling trajectory of the front vehicle (F) from the front vehicle (F) and to compare a current coordinate of the pertinent vehicle (R) with the coordinate of the control point, thereby generating the reward,
wherein the control point corresponds to a point among points for controlling the shape of a spline curve corresponding to the traveling trajectory of the front vehicle (F),
wherein the image information comprises front image information output from a front camera of the pertinent vehicle (R) and rear image information output from a rear camera of the front vehicle (F),
wherein the learning device (<NUM>) is further configured to:
determine an overlapping part between a rear image of the front vehicle (F) and a front image of the pertinent vehicle (R) on the basis of the front image information and the rear image information;
determine a degree of overlapping between the rear image and the front image according to the determined overlapping part as learning data regarding the reinforcement learning; and
use the determined degree of overlapping as learning data regarding the reinforcement learning.