Driver assistance method using a virtual target for adaptive distance control of vehicles

Abstract

The present invention relates to a driver assistance method for a so-called ego-vehicle (EGO) moving in a lane, the method comprising a first step of identifying traffic around the ego-vehicle in the same lane as the ego-vehicle and in adjacent parallel lanes moving in the same direction; a second step of determining a virtual center of gravity target (G), including calculating the position of the virtual center of gravity target, the velocity of the virtual center of gravity target, and the acceleration of the virtual center of gravity target; and a third step of calculating longitudinal speed setpoints, acceleration setpoints, and torque setpoints of the ego-vehicle (EGO), the longitudinal speed setpoint being a function of the position of the virtual center of gravity target (G), the velocity of the virtual center of gravity target (G), and the acceleration of the virtual center of gravity target (G).

Description

[Technical Field]

[0001] The present invention relates to a driver assistance method that uses a virtual target for adaptive driving control of inter-vehicle distance. The present invention finds advantageous applications in the form of a driver assistance method for an ego vehicle in an automobile equipped with such a type of driver assistance system.

[0002] The present invention also relates to computer program products, in which instructions in the computer program product are adapted to carry out such a type of method.

[0003] The present invention further relates to driver assistance systems for ego automobile vehicles.

[0004] The present invention also relates to automobiles, including powertrains, braking means, and assistance systems of this kind. [Background technology]

[0005] Driver assistance technologies are becoming increasingly widespread and are no longer limited to top-of-the-line vehicles.

[0006] These technologies make it possible to simplify driving a car and / or make the behavior of the vehicle driver more reliable.

[0007] Automated driving control systems are routinely installed in modern vehicles and typically function based on regulating the distance between the equipped vehicle (also called the self-vehicle) and the vehicle preceding it in its lane (referred to as the target vehicle or simply the target).

[0008] It is well known that automobiles are equipped with adaptive cruise control (ACC) systems.

[0009] This type of adaptive inter-vehicle driving control system is equipped with means for detecting the vehicle's environment, such as radar, and is therefore capable of detecting other vehicles or objects on the road, and in particular, vehicles preceding it on the road.

[0010] These systems are designed to control the vehicle so that its speed is equal to the driver-set value, except when there are events on the road that require the vehicle to slow down (such as following a vehicle traveling at a different speed than the driver-set value, traffic congestion, or traffic lights), and when such events occur, the vehicle's speed is controlled accordingly.

[0011] Subsequently, the speed of a vehicle equipped with this type of adaptive distance control system can be adjusted to maintain a substantially constant safe distance from the vehicle ahead. Accordingly, this type of system interacts with the engine control system and / or braking system to accelerate or decelerate the vehicle.

[0012] Conventional adaptive speed control systems can be unreliable. Slow speed corrections can cause sudden acceleration or deceleration, leading to discomfort and a lack of reassurance. Such situations can occur particularly when a target vehicle changes lanes or when another vehicle merges into the other vehicle's lane.

[0013] Below, one embodiment of a vehicle equipped with a controller using a classic adaptive inter-vehicle distance control method is described with reference to Figure 1.

[0014] Automobile 10 is any type of automobile, in particular a passenger car or a utility vehicle. In this document, a vehicle that includes means for carrying out the present invention is referred to as the “self” vehicle. This designation is merely to distinguish the vehicle from other surrounding vehicles and does not in itself impose any technical limitations on Automobile 10.

[0015] The automobile 10 or the vehicle itself 10 includes a conventional adaptive inter-vehicle distance control device DISP, which uses a classic adaptive inter-vehicle distance control method, particularly in the form of a controller.

[0016] The classic DISP (Disc-Independent Passenger System), a vehicle's adaptive distance control device, may also be part of a more common driver assistance system.

[0017] For the remainder of this document, the term "target vehicle" refers to a vehicle located in the traffic surrounding the own vehicle 10, and the kinematic attributes of the target vehicle, including position, velocity, and acceleration, are taken into consideration in the calculation of the longitudinal velocity setpoint of the own vehicle.

[0018] The target vehicle may be any type of automobile, in particular a passenger car, a utility vehicle, or a motorcycle.

[0019] The DISP (Distant Inter-Vehicle Stratos) adaptive driving control device for automobiles requires the perception of vehicles located in the surrounding traffic, particularly using sensors C such as perception sensors and position sensors. Therefore, in practice, determining the kinematic attributes Att_Target, which are the relative dynamic presence of surrounding vehicles in terms of lane, position, speed, and acceleration, is required by sensors C to correctly identify the position of surrounding vehicles. Data from perception sensors is processed, in particular by merging the data, to identify and characterize surrounding objects that possess those kinematic attributes. These perception sensors may employ a variety of technologies (e.g., ultrasonic, radar, lidar, video cameras, and position sensors), particularly inertial measurement units, or odometers, or other GPS (global positioning satellite systems). Characterization of objects allows for the recognition of the environment surrounding one's own vehicle and the classification of objects by type, such as vehicles (motorcycles, cars, heavy trucks, bicycles, etc.), pedestrians, infrastructure, and traffic signals, and in this specification, only vehicles are considered as targets. Data from these sensors, particularly those associated with maps, can also identify road shapes (slope, curves, etc.), and navigation information can provide background information (type of area (urban, suburban, rural), type of road (highway, town, interstate highway, etc.), speed limit) as a function of the perceived vehicle position. This information may be merged with recognition of signs, road traffic information, and shape information (slope, curves, number of lanes, etc.). The kinematic attribute Att_Target of the identified target is then sent to the distance control unit CD, which also receives driver data DC as input. Driver data DC consists of a control speed and a default follow time (default is 2 seconds) selected by the driver, and is also converted to a default follow distance setpoint selected by the driver as a function of the vehicle's speed, for example using a table.

[0020] Based on this information, the distance control unit CD generates a longitudinal speed setting value Vc as an output, which enables automatic adaptation, using a default follow distance setting value relative to the preceding vehicle in the same lane, and the vehicle adjusts its speed to maintain that distance. In particular, the vehicle's speed setpoint Vc and speed Vm, obtained from the measurement results from sensor C (specifically, the wheel speed sensor, from which the measurement results are averaged and associated with the Kalman filter), are then sent to the input of the speed control unit CV, which generates an acceleration setpoint Ac as an output, and after looping with the vehicle's acceleration data Am obtained from sensor C (specifically from the inertial measurement unit) or measured wheel speed, the vehicle's acceleration is automatically increased to reach a control speed selected by the driver as soon as the lane ahead of the vehicle is clear, and the torque of actuator A (engine, brakes, etc.) is controlled by a torque setpoint Cc, which is a function of the integrated acceleration command when leaving the loop, generated at the output of the torque control unit CC, thus enabling assistance to the driver in driving operations. However, this device considers only one target at a time (a target located in front of the vehicle within its lane), making the vehicle extremely sensitive, especially to vehicle merging between itself and the vehicle ahead, and lane changes by the target vehicle, causing the set values ​​to become suddenly irregular and degrading the smoothness of control.

[0021] Document FR2912981, relating to autonomous driving methods for vehicles equipped with ACC systems, is also known. The purpose of this document is to improve the smoothness of vehicle behavior to enhance user comfort, but this method consumes a lot of energy because it requires numerous calculations to determine the dynamic components tracked by the ACC system in the case of multiple targets. [Overview of the project]

[0022] One object of the present invention is to correct at least some of the disadvantages of the prior art by providing a driver assistance method for a host vehicle moving within a lane, the method including the following. - A first step of identifying the traffic around the host vehicle within the host vehicle's lane and within at least one adjacent parallel lane in the same traffic direction. - A second step of determining a virtual barycentric target, including calculating the position, speed, and acceleration of the virtual barycentric target. - A third step of calculating a longitudinal speed set value, an acceleration set value, and a torque set value of the host vehicle, wherein the longitudinal speed set value is a function of the position, speed, and acceleration of the virtual barycentric target. According to the present invention, without changing the ACC control loop as such, it is possible to change only the input of the ACC control loop, predict the trajectories of vehicles located in the surrounding traffic, and consider multiple targets, including the case where there is no target within the host vehicle's lane, while improving the smoothness of the ACC control loop.

[0023] According to an advantageous feature, the surrounding traffic includes at least two target vehicles preceding the host vehicle moving within the host vehicle's lane or within an adjacent parallel lane in the same traffic direction, thereby making it possible to consider not only the lateral vehicles but also the vehicles decelerating ahead of the vehicle preceding the host vehicle.

[0024] According to another advantageous feature, the first identification step is a sub-step of detecting each of at least two target vehicles, and for each target vehicle, determining the position of the target vehicle relative to the host vehicle, the speed of the target vehicle, and the acceleration of the target vehicle as outputs, and in particular, including a sub-step involving determining the trajectory of the target vehicle, thereby making it possible to anticipate the safety distance by taking into account the predicted trajectory, whether the position is relative or absolute.

[0025] According to another advantageous feature, the second step of determining the virtual center of gravity target consumes the pre-selected control speed, the default following distance set value, and the result of the identification step as inputs, thereby making it possible to construct the virtual center of gravity target in consideration of the control speed and the following distance set value that can be customized by the driver based on easily usable information.

[0026] The second step of determining the virtual center of gravity target advantageously includes a filtering step, such as selecting only some of the targets, as a function of the speed of the target or a function of the interval between the targets over time.

[0027] According to another advantageous feature, at least one of the target vehicles is located in the host vehicle's lane, making it possible to consider the phenomenon of deceleration by the preceding vehicle, although one of these vehicles preceding itself is also leaving the lane.

[0028] The advantage related to the feature that at least one of the target vehicles is located in an adjacent lane and is trying to merge into the host vehicle's lane is that it makes it possible to consider the merging phenomenon by prediction.

[0029] According to another advantageous feature, the second step of determining the virtual center of gravity target uses the target change prediction factor determined for each target, thereby making it possible to dynamically weight the center of gravity and thus improve the smoothness of the ACC.

[0030] According to another advantageous feature, the modification of the target coefficient is a function of the relative lateral distance between the trajectory of the own vehicle and at least one target vehicle, or a function of the relative lateral distance between the center of the lane in which the own vehicle is moving and at least one target vehicle, thereby allowing for the neglect of curves in the road.

[0031] According to another favorable feature, the change in the target coefficient is a function of the estimated time of intersection of the estimated trajectory of the target vehicle with the estimated trajectory of the own vehicle, thereby allowing weighting to be applied using other information provided by the module for the identification of surrounding traffic.

[0032] The change in the target coefficient is advantageously a function of the pre-target vehicle's tracking time, which allows for smooth consideration of the deceleration of the vehicle moving ahead of the vehicle preceding it.

[0033] According to another advantageous feature, the change in the target coefficient is a function of the stiffness coefficient, thereby guaranteeing an update function, especially since this stiffness coefficient is unique for all targets to which it converges.

[0034] The present invention also relates to a computer program product which includes program code instructions stored in a computer-readable medium containing instructions, which, when the program is executed by the computer, cause the computer to execute the method of the present invention, and this program product has advantages similar to the advantages of the method, and this program product is easily installed in a car computer.

[0035] The present invention also relates to a system for a self-propelled vehicle moving within a lane, which comprises the following: - A module for identifying traffic around a vehicle within its own lane and in at least one adjacent parallel lane in the same direction of travel. - A module for determining the virtual center of gravity target, including the calculation of the virtual center of gravity target's position, velocity, and acceleration. - A module for calculating longitudinal speed setpoints, acceleration setpoints, and torque setpoints of its own vehicle, wherein the longitudinal speed setpoint is a function of the position of a virtual center of gravity target, the velocity of the virtual center of gravity target, and the acceleration of the virtual center of gravity target, and the module has advantages similar to those of a method using means routinely available in a vehicle, such as radar and video cameras.

[0036] The present invention also relates to a self-driving vehicle including a powertrain, acceleration means and braking means, as well as a driver assistance system according to the present invention, thereby enabling simple adaptation to a vehicle equipped with an ACC device, whether autonomous or not.

[0037] Other objects, features, and advantages of the present invention will become apparent when you read the following description, which is given by non-limiting, mere examples, with reference to the attached drawings. [Brief explanation of the drawing]

[0038] [Figure 1] This is a schematic diagram illustrating a classic, conventional ACC device, as already mentioned. [Figure 2] This is a schematic diagram illustrating the driver assistance system according to the present invention. [Figure 3] This figure shows the application of the method according to the present invention. [Figure 4] This figure shows the relative lateral distance in a curve in one use case of the present invention. [Figure 5] This figure illustrates trajectory prediction in one use case according to the present invention. [Figure 6] This figure illustrates another use case of the present invention. [Modes for carrying out the invention]

[0039] Throughout this document, directions and orientations are specified with reference to the long-established right-handed coordinate system XYZ used in automotive design, where X specifies the longitudinal direction of the vehicle in the direction of forward movement, Y is the transverse direction relative to the leftward-facing vehicle, and Z is the vertical direction upward. The concepts of "forward" and "rearward" are indicated with reference to the normal direction of the vehicle's forward movement. Throughout this description, the term "substantially" means that slight differences in a determined nominal quantity may be permitted; for example, "substantially constant" means that differences of the order of 5% are permitted in the context of this invention. For further clarity, identical or similar elements are represented by the same reference symbol in all figures.

[0040] According to one aspect of the present invention, one embodiment of the adaptive inter-vehicle distance control system 1 for an automobile EGO, which is part of a more general driver assistance system, is schematically shown in Figure 2. Elements of system 1 that are the same as the elements constituting the device DISP in Figure 1 have the same reference numerals.

[0041] In this driver assistance system according to the present invention, sensors C such as perceptual sensors are present that enable the measurement of not only the vehicle's own motion but also the perception of the environment, which, as herein referred to, can provide information on the vehicle's own speed and acceleration, as well as the position, speed, and acceleration of objects in the environment, and can also provide predictions of the trajectories of those objects. Thus, in practice, in order to correctly predict the vehicle's trajectory, it is necessary to determine environmental information Env by sensors C, which includes kinematic attributes such as the presence of surrounding vehicles in the lane, dynamic relative position, speed, and acceleration. This environmental information is given, for example, within the vehicle's own reference frame and positioned at the height of the vehicle's rear axle, but any other position in the reference frame is possible. Similarly, environmental information regarding the position of other vehicles is often based on the detection of the rear axle. All of this environmental information Env, obtained by merging data from sensors C, is generated indexed per object at the output of what is herein referred to as an identification module (not shown) for the identification of traffic around the vehicle, on the vehicle's own lane and on adjacent parallel lanes in the same direction of travel, and is transmitted as input to module CBV for the determination of a virtual center of gravity target. The identification module merges the information already obtained from sensor C and information from signal lights such as turn signals of surrounding vehicles. Next, as shown in Figure 3 and subsequent figures, a change in the position reference system may be made upstream or in module CBV to determine the distance between bumpers rather than the longer distance between axes.

[0042] The module CBV also consumes driver data DC as input, which consists of a control speed selected by the driver and a default follow time selected by the driver, and the driver data DC is also converted to a default follow distance setpoint selected by the driver. The module CBV determines a virtual center of gravity target within its own vehicle's lane as a function of these inputs and calculates the position, velocity, and acceleration of the virtual center of gravity target. The module CBV takes into account information from various objects, in particular, selecting an object located upstream of its own vehicle as a target, whether the object is within or to the side of its own vehicle's lane, and from that information, it is possible to infer the target vehicle, predict the movement of the target vehicle, and therefore, to react like a human driver, by predetermining a predicted trajectory with respect to the target vehicle. Filters may be added to the module CBV to filter out small oscillations related to measurement errors, such as considering only targets whose relative lateral velocity with respect to itself exceeds a threshold. This module CBV is placed upstream of the control loop of the actuator A subsystem (engine, brakes, etc.) and provides control setpoints like a classic ACC target without requiring modification of the ACC logic, thereby facilitating the integration of the module CBV. In practice, these kinematic attributes of the virtual center of gravity target, Att_CBV, which are the lane, dynamic and relative position, velocity, and acceleration of the virtual center of gravity target, are generated as the output of the module CBV for the determination of the virtual center of gravity target and sent as input to loop BCD for the calculation of dynamic setpoints, including the longitudinal velocity setpoint Vc and acceleration setpoint Ac of the vehicle itself, the aforementioned longitudinal velocity setpoint being a function of the virtual target's position, the virtual target's velocity, and the virtual center of gravity target's acceleration. The dynamic setpoint calculation loop BCD comprises the following:

[0043] Distance control unit CD consumes driver data DC, which consists of a driver-selected control speed and a driver-selected default follow time, in addition to the kinematic attributes Att_CBV of the virtual center of gravity target, and is also converted into a driver-selected default follow distance setpoint. It generates a longitudinal speed setpoint Vc as an output, corresponding to the required speed control magnitude, thereby enabling the driver to meet expectations regarding safe distance along with overall smoothness.

[0044] - As before, a speed control unit CV consumes the vehicle's speed setpoint Vc and speed Vm as inputs and generates an acceleration setpoint Ac as an output.

[0045] - A torque control unit CC, as before, consumes an integrated acceleration command as input when leaving the loop and generates a torque setpoint Cc as output that controls actuator A, wherein the torque setpoint Cc enables the control of the wheel in a manner such as tracking a speed setpoint Vc.

[0046] The Module CBV for determining a single virtual center of gravity target allows for the consideration of parallel surrounding vehicles that may merge in the near future and become targets tracked by ACC, but this Module CBV also aims to ensure the normal control function of ACC when no vehicles are detected either in front or to the side, or when only one target vehicle is detected within the vehicle's lane. This method can also be advantageously applied to a vehicle V0 that leaves its own vehicle's lane to change lanes (also called cutting in). This vehicle is no longer indexed if, after entering the lane to the side of its own vehicle, it becomes vehicle Vx or leaves the field of approach of its own vehicle, which is determined by a default distance from its own vehicle as a function of the sensor's perceived distance and / or in particular a follow distance setpoint which may be customized by the driver.

[0047] As shown in Figure 3, the vehicle EGO in the straight line perceives two target vehicles, V0 ahead in the lane of the vehicle EGO, and V1 in the left lane, slightly ahead and to the left of the vehicle EGO, as determined by the module for identifying surrounding traffic. The right-turn signal of vehicle V1 is activated, indicating that vehicle V1 is about to move into the lane of the vehicle EGO. Three lanes in the same direction of travel are represented by short dashed lines, and solid lines represent the separation of these lanes from potential lanes in the opposite direction. In this example, target vehicle V1 is to the left of the vehicle EGO in the direction of forward travel, but alternatively, target vehicle V1 may be to the right of the vehicle EGO and, for example, is about to enter the lane of the vehicle EGO by activating its left-turn signal. Thus, here, the virtual center of gravity target G corresponds to the center of gravity G of the system (A,a)(B,b), where a+b≠0, a and b are weighting coefficients, and for any point O considered as the origin, the following equation is obtained: [Formula 1] TIFF0007857869000001.tif11170

[0048] The module for identifying surrounding traffic provides a shape reference to module CBV for determining a virtual center of gravity target G, within a reference system related to the vehicle itself (located at the center of the rear axle in this specification, but other selections of the reference system are also possible), corresponding to points A and B, which are the positions of the vehicle V0 and V1. Module CBV then brings about a change in the reference system, for example, by associating the new reference system of the vehicle itself with the front of the vehicle's bumper.

[0049] X0, the distance from the first target vehicle (particularly from its rear bumper), lies within the field of direct approach of the vehicle itself, within a relative reference frame related to the vehicle itself.

[0050] X1 is the distance from the second target vehicle (particularly from its front bumper) and represents the risk of entering the approach zone of the own vehicle within a relative reference frame related to the own vehicle.

[0051] TIFF0007857869000002.tif8170 represents the relative speeds of the first and second vehicles, respectively.

[0052] Similarly TIFF0007857869000003.tif8170 represents the relative accelerations of the first vehicle and the second vehicle, respectively.

[0053] The objective of the virtual center of gravity target method is to predict target changes by adding a dynamic offset o_d to a follow distance setpoint d_s_c provided by the driver and applied to the target vehicle V0 in the lane. The sum of the dynamic offset distance o_d and the follow distance setpoint d_s_c constitutes the vehicle-to-vehicle distance that should remain between the front of the self-vehicle EGO and the rear of the target vehicle V0 sharing the same lane. The offset o_d is obtained by projecting the virtual center of gravity target G onto its own trajectory, and then the offset o_d, along with positional information and other kinematic attributes Att_CBV regarding the virtual center of gravity target G, is sent to module CD. Center of gravity weighting coefficients a and b are selected corresponding to the target change prediction coefficients. These coefficients can be obtained in two different ways.

[0054] The first method is based on the lateral position of the merging vehicle V1. Therefore, the closer the lateral target vehicle V1 is to the lane of the vehicle EGO, the more likely the target vehicle V1 is to be merging into the aforementioned lane. Preferably, the lateral distance Y1' relative to the trajectory and / or center of the lane (in this specification, these coincide and are represented by long dashed lines) of the vehicle EGO is used, rather than the vehicle's longitudinal coordinate Y1. This allows for avoidance of disturbances related to the orientation of the vehicle EGO within the lane (a lateral error of 5° = 8 meters at 100 meters) in the form of a value obtained by multiplying the tangent by the distance to the target. Thus, this method can work on curves.

[0055] Figure 4 visually shows the errors occurring in the curve. The values of Y1 and Y2 correspond to the vertical coordinates of the rear ends (e.g., the center of the bumper) of the lateral target vehicles V1 and V2 in the relative reference system related to the host vehicle. The values of Y1' and Y2' correspond to the lateral distances of the lateral target vehicles V1 and V2 relative to the center of the lane of the host vehicle EGO. It can be seen that Y1'>Y2' with respect to the lateral distance, while Y1<Y2 in terms of the vertical coordinate. The values of the lateral distance are provided by the module for identifying the surrounding traffic, especially under the same conditions as the position of the target. Therefore, the weighting coefficients a and b are obtained as functions of the lateral distance. In particular, a is obtained as a function of Y1', and b is obtained as a function of the reciprocal of Y1'.

[0056] The second method shown in Figure 5 is here for use as a target change prediction coefficient for the estimated time of intersection with the trajectory of the host vehicle EGO, which corresponds to 3 seconds (T + 3). The estimated time of intersection with the trajectory of the host vehicle EGO is based on trajectory prediction, and both are provided by the module for identifying the surrounding traffic under the same conditions as the position of the target.

[0057] Hereinafter, a prediction coefficient based on the lateral position of the merging vehicle V1 is used, and this application is the same as the second method.

[0058] It is preferable to adopt the coefficient k of rigidity or the update coefficient for the weighting coefficients a and b. In particular, in the following method, a = Y1' and b = k / Y1'.

[0059] Therefore, the stiffness coefficient k is placed at a level of magnitude X1, where X1 is the magnitude representing the merging vehicle V1. From a behavioral perspective, this coefficient allows us to define the intensity of considering the target introduced into the calculation of the virtual target, with a larger k being more predictive of the self-vehicle. Assuming the problem is symmetric with respect to axis x, the coefficient k for the target on the right and the coefficient k for the target on the left are identical, thereby allowing the method to be applied in a country where drivers drive on the right as in a country where drivers drive on the left, without any specific changes. The choice to use inversely proportional (a=1 / b) weighting coefficients a, b allows us to maintain a ratio modulo the stiffness coefficient, which is essential for the equilibrium, expressiveness, and balance of the center of gravity and facilitates the management of constraints. Next, the equations for the three magnitudes of the position, velocity, and acceleration of the center of gravity G of the target vehicles V0, V1 are obtained as follows: [Formula 2] TIFF0007857869000004.tif25170 and TIFF0007857869000005.tif14170

[0060] These magnitudes are calculated at each time increment so that the center of gravity G accepts, and dynamically does so, the variation in distance to the target, velocity, and acceleration during the maneuver. The values ​​of Y1' and k / Y1' are coefficients that are considered unitless and are therefore not considered in the derivation here, and as already mentioned, Y1' can also be the merging time, if Y1' corresponds to the lateral distance. It is also possible to calculate XG and then differentiate that value to obtain the magnitudes of the other velocity and acceleration. Furthermore, the fact that the weighting coefficients a, b are functions of the lateral position of the lateral target V1 or functions of the merging time makes it possible to consider the motion state of the merging. The further the target vehicle V1 merges into the lane of its own vehicle EGO, the more the magnitude of the merging vehicle V1 is taken into consideration. Conveniently, only a single coefficient of stiffness k is needed in the three equations, which makes it possible to greatly simplify the update. This stiffness coefficient k can be adapted according to the speed of the vehicle itself so that it behaves differently depending on the situation (a freely flowing interstate highway or a heavily congested urban ring road), and its value may be in particular within the range [0;10], and is preferably a value of 1.

[0061] Creating a virtual center of gravity target G using distance, velocity, and acceleration is advantageously non-intrusive in conventional ACC loops. Therefore, the method of the present invention never alters the tuning of the powertrain control, whether the powertrain is an internal combustion engine or electric, nor ever alters the tuning of the brake device. Furthermore, as already shown in Figure 3, the method has the advantage of a spatial representation that allows for easy visualization during updates, graphically displaying the calculations performed to generate the center of gravity G at the positions of target vehicles V0 and V1. Physically speaking, the application of the method involves adding an offset o_d to the follow distance setpoint d_s_c.

[0062] Vehicle V1 merges into the lane of its own vehicle EGO, V0, and is then indexed as vehicle V0.

[0063] As already mentioned, this method can also favorably consider targets located on either the right or left side, and in particular, it allows for the consideration of multiple targets regardless of whether the target is to the left and / or right in front of the self-vehicle EGO. Similar to the merging of target vehicle V1 between the self-vehicle EGO and the vehicle V0 preceding the self-vehicle EGO, this method also applies to three or more targets, as shown in Figure 6. Here, the virtual center of gravity target G corresponds to the center of gravity G of systems (A,a), (B,b), (C,c), where a+b+c≠0, and a, b, and c are weighting coefficients, and for any point O considered as the origin, the following equation is obtained: [Formula 3] TIFF0007857869000006.tif11170

[0064] Equation 3 gives the following equation. [Equation 4] TIFF0007857869000007.tif17170

[0065] NbTm is the number of missing lateral targets, allowing for the application of methods that include instances of missing targets while adhering to homogeneous dynamic behavior. Furthermore, a default value needs to be defined for when one or more targets are missing. Thus, if any vehicle in the self-ego lane is absent, X0 is considered equal to the follow distance setting value d_s_c. TIFF0007857869000008.tif8170 is considered to be equal to the control speed selected by the driver. TIFF0007857869000009.tif8170 is considered equal to 0, and similarly, if there is no lateral target Vi, TIFF0007857869000010.tif7170 is considered equal to 0, and Yi' is considered equal to 1. Therefore, if target vehicles V1 and V2 are missing, X G =X0.

[0066] The ACC targeting issue is adhering to the relative follow time with the preceding target, and therefore, for the benefit of the own vehicle, maintaining an additional safety distance when the follow time between two preceding vehicles in the lane becomes dangerous. Thus, this method makes it possible to monitor not only the merging of lateral target vehicles, but also the behavior of vehicles preceding the vehicle that the own vehicle is following. In fact, this method makes it possible to apply not only to multiple targets V1 and V2 that are attempting to merge in front of the own vehicle EGO, but also to consider vehicle VP preceding vehicle V0 that the own vehicle EGO is following. In fact, as shown in Figure 6, within the lane of the own vehicle EGO, the own vehicle EGO is following vehicle V0, and vehicle V0 itself is following vehicle VP (called the pre-target vehicle), and in the lateral lane, there are vehicle V1 to the left front of the own vehicle and vehicle V2 to the right front of the own vehicle. Initially, if the interest lies only in the vehicle itself (EGO), the vehicle preceding the vehicle itself (V0), and the pre-target vehicle (VP), then X is the distance between the vehicle itself (EGO) and the pre-target vehicle (VP). P , the time derivative in the pre-target situation. TIFF0007857869000011.tif8170 and the stiffness coefficient k P , as well as weighting coefficients TIFF0007857869000012.tif9170 and Using TIFF0007857869000013.tif8170, the following equation may be written, where k is a single coefficient of stiffness. P is, size X P It will be placed at that level. [Formula 5] TIFF0007857869000014.tif20170

[0067] Here, the weighting coefficients a and b depend on the tracking time of the pre-target VP, which physically represents the time of trajectory intersection in the vertex coordinate system. Therefore, two targets V0 and VP are always considered, and it is preferable to perform filtering on threshold speed, such as considering the pre-target VP only when its relative speed to V0 is below a threshold, so that the self-vehicle is not affected by fluctuations in each change of the pre-target VP's speed. Thus, the pre-target VP is considered only when the difference in speed of a pre-target lower than the self's speed is below a predetermined threshold, and this threshold is, for example, a few km / h, by filtering small oscillations and considering only nearby pre-targets. This threshold becomes an update parameter, and finally, the relative distance to the target may be considered to be the same as the tracking time threshold, that is, filtering may be applied by considering the pre-target VP as a function of the pre-target's speed as a function of the driver's adjustment of tracking time, such as considering the pre-target VP only when the time between VP and EGO is below a predetermined threshold. Stiffness coefficient k P The value of is preferably within the range of [0;10], and it has been found that the higher this value, the earlier the brakes are applied. Therefore, the adjustment of this value can be a function of the mode selected by the driver, for example, the value in sport mode will be higher than the value in city mode. To account for the absence of a pre-target VP, as before, an indicator F of the pre-target VP is used, with a value of 1 indicating the absence of a pre-target and a value of 0 indicating the presence of a pre-target. Pabs If is being used and the pretarget is missing, X G A default value is selected such that =X0. [Formula 6] TIFF0007857869000015.tif35170

[0068] It is preferable that XG is calculated and then differentiated to obtain the magnitudes of other speeds and accelerations. This method may consider four (or more) targets as shown in FIG. 6, and V1, V2, V0, and VP with a default value X0 are considered equal to the following distance setting value d_s_c, TIFF0007857869000016.tif8170 is considered equal to the control speed selected by the driver, TIFF0007857869000017.tif8170 is considered equal to 0. Similarly, when there is no lateral target Vi, TIFF0007857869000018.tif8170 is considered equal to 0. NbTm is the number of missing lateral targets. When there is no pre-target, when the lateral target vehicle is missing, X G = X0 is obtained, and F Pabs = 1. [Equation 7] TIFF0007857869000019.tif56170

[0069] It is preferable that XG is calculated and then differentiated to obtain the magnitudes of other speeds and accelerations. If the perception sensor enables this, more targets (especially lateral targets) can be considered.

[0070] By this method, the ACC system comes to exhibit more comfortable behavior with respect to changes in the target, and the safety is enhanced by predicting the movement of the target vehicle by this method. Therefore, the behavior of the host vehicle EGO approaches that of a human driver and becomes smooth in traffic.

Claims

1. A driver assistance method for an autonomous vehicle (EGO) moving within a lane, A first step of identifying the traffic around the vehicle within the lane of the vehicle and in at least one adjacent parallel lane in the same direction of travel, A second step of determining a virtual center of gravity target (G) of a target vehicle included in the surrounding traffic, the second step of determining the virtual center of gravity target (G) includes calculating the position of the virtual center of gravity target which is the center of gravity of the position of the target vehicle, the velocity of the virtual center of gravity target which is the center of gravity of the velocity of the target vehicle, and the acceleration of the virtual center of gravity target which is the center of gravity of the acceleration of the target vehicle, A third step of calculating a longitudinal velocity set value (Vc), an acceleration set value (Ac), and a torque set value (Cc) in the direction of travel of the vehicle, wherein the longitudinal velocity set value (Vc) is a function of the position of the virtual center of gravity target (G), the velocity of the virtual center of gravity target (G), and the acceleration of the virtual center of gravity target (G), The steps include controlling the vehicle according to the calculated longitudinal speed set value (Vc), acceleration set value (Ac), and torque set value (Cc), The second step of determining the virtual center of gravity target uses the target change prediction coefficient determined for each target, The target change prediction coefficient is, A function of the relative lateral distance (Y1', Y2') between the trajectory of the self-vehicle (EGO) and at least one target vehicle, or a function of the relative lateral distance between the center of the lane in which the self-vehicle (EGO) is moving and at least one target vehicle (V1, V2), Is it a function of the estimated time of the intersection of the estimated trajectory of the target vehicle and the estimated trajectory of the self-vehicle (EGO)? This is a function of the tracking time of a pre-target vehicle (VP) preceding one of the target vehicles (V0), or, Renewal coefficient (k, k Ｐ A driver assistance method characterized by being a function of ).

2. The driver assistance method for an EGO (Ego) according to claim 1, characterized in that the surrounding traffic includes at least two target vehicles (V0, V1, V2, VP) that are preceding the EGO or are moving within the EGO's lane or within adjacent parallel lanes in the same direction of travel.

3. Driver assistance method for an Own Vehicle (1) according to claim 2, characterized in that the first step of identification includes a substep of detecting each of the at least two target vehicles (V0, V1, V2, VP), and for each target vehicle, the substep of determining the position of the target vehicle relative to the Own Vehicle (EGO), the speed of the target vehicle, and the acceleration of the target vehicle as outputs, and determining the trajectory of the target vehicle.

4. A driver assistance method for an autonomous vehicle (EGO) according to any one of claims 1 to 3, characterized in that the second step of determining the virtual center of gravity target uses a pre-selected control speed, a predetermined follow distance setting value (d_s_c), and the result of the identification step as inputs.

5. A driver assistance method for an EGO (self-propelled vehicle) according to any one of claims 1 to 4, characterized in that the second step of determining the virtual center of gravity target includes a filtering step.

6. A driver assistance method for an EGO vehicle according to any one of claims 2 to 5, characterized in that at least one of the target vehicles (V1, V2) is located in an adjacent lane and is about to merge into the lane of the EGO vehicle.

7. A driver assistance system (1) for an autonomous vehicle (EGO) moving within a lane, A module for identifying traffic around the vehicle, within the lane of the vehicle and in at least one adjacent parallel lane in the same direction of travel, A module (CBV) for determining a virtual center of gravity target of a target vehicle included in the surrounding traffic, comprising the calculation of the position of the virtual center of gravity target which is the center of gravity of the position of the target vehicle, the velocity of the virtual center of gravity target which is the center of gravity of the velocity of the target vehicle, and the acceleration of the virtual center of gravity target which is the center of gravity of the acceleration of the target vehicle, Modules (CD, CV, CC) for calculating the longitudinal velocity set value (Vc), acceleration set value (Ac), and torque set value (Cc) in the direction of travel of the self-propelled vehicle (EGO), wherein the longitudinal velocity set value (Vc) is a function of the position of the virtual center of gravity target (G), the velocity of the virtual center of gravity target (G), and the acceleration of the virtual center of gravity target (G), The system includes a module that controls the vehicle according to the calculated longitudinal speed set value (Vc), acceleration set value (Ac), and torque set value (Cc), The module for determining the virtual center of gravity target (CBV) uses the target change prediction coefficient determined for each target, The target change prediction coefficient is, A function of the relative lateral distance (Y1', Y2') between the trajectory of the self-vehicle (EGO) and at least one target vehicle, or a function of the relative lateral distance between the center of the lane in which the self-vehicle (EGO) is moving and at least one target vehicle (V1, V2), Is it a function of the estimated time of the intersection of the estimated trajectory of the target vehicle and the estimated trajectory of the self-vehicle (EGO)? This is a function of the tracking time of a pre-target vehicle (VP) preceding one of the target vehicles (V0), or, Renewal coefficient (k, k Ｐ A driver assistance system (1) characterized by being a function of ).

8. An EGO (automobile) comprising a powertrain, acceleration means and braking means, characterized in that it comprises the driver assistance system (1) described in claim 7.

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