• 专利附录
  • 专利说明
  • 权利要求
  • 类似技术
  • 同族专利
  • 引用文献
  • 法律状态
  • 优先权
    • 专利摘要:
    The invention relates to a method of assisting the driving of a motor vehicle on a highways road separated by a safety rail in which the presence is detected (40) and the safety rail is modeled from measurements taken. continuously by at least one laser scanner sensor (20) mounted on the motor vehicle, with determination of an ICONF confidence index associated with the detection by the laser scanner sensor (20); activating (60) an automatic driving mode if the confidence index ICONF is greater than a confidence threshold Ith; this mode is maintained (120) as long as a current confidence index associated with the detection is greater than the confidence threshold I th; and disabling this mode if the current confidence index falls below said confidence threshold; According to the invention, it is estimated (70) the traffic density at the front of the motor vehicle from images captured by a camera (30) on board, so that after activation (60) of the automatic driving mode, said current confidence index taken into account for deactivating and maintaining the mode is a function of a sum of the ICONF confidence index associated with the presence detection of the safety rail and the estimated traffic density.
    公开号:FR3056532A1
    申请号:FR1659239
    申请日:2016-09-28
    公开日:2018-03-30
    发明作者:Thomas Heitzmann；Thomas Liennard；Benazouz BRADAI
    申请人:Valeo Schalter und Sensoren GmbH；
    IPC主号:
    专利说明:

    Holder (s): VALEO SCHALTER UND SENSOREN GMBH Simplified joint-stock company.
    Extension request (s)
    Agent (s): VALEO COMFORT AND DRIVING ASSISTANCE.
    P4) ASSISTANCE TO DRIVING ON HIGHWAY WITH SEPARATE PAVEMENTS BY A SAFETY RAIL.
    FR 3 056 532 - A1
    The invention relates to a method for assisting the driving of a motor vehicle on a fast lane with roads separated by a safety rail in which the presence is detected (40) and the safety rail is modeled on the basis of measurements carried out. continuously by at least one laser scanner sensor (20) mounted on the motor vehicle, with determination of an Iconf confidence index associated with detection by the laser scanner sensor (20); an automatic driving mode is activated (60) if the confidence index Iconf is greater than a confidence threshold l th ; this mode is maintained (120) as long as a current confidence index associated with the detection is greater than the confidence threshold I th; and this mode is deactivated if the current confidence index falls below said confidence threshold; According to the invention, the traffic density at the front of the motor vehicle is estimated (70) from images captured by an on-board camera (30), so that after activation (60) of the automatic driving mode, said current confidence index taken into account for deactivation and maintenance of the mode is a function of a sum of the confidence index Icqnf associated with the detection of the presence of the safety rail and of the estimated traffic density.

    DRIVING ASSISTANCE ON A RAPI WAY FROM TO SEPARATE PAVEMENTS BY A RAI L OF SECURITY
    The present invention relates generally to motor vehicles, and more specifically to a method and a system for assisting driving on a fast track with roadways separated by a safety rail.
    Driving assistance systems for vehicles include various functions, in particular lane change assistance or LCC (initials set for Lane Change Control), cruise control or ACC (initials set for Auto Cruise Control), or stopping and restarting the vehicle depending on traffic conditions and / or signals (lights, stop, give way ...).
    Their general function is thus to define, the trajectory which the vehicle must follow and consequently make it possible to control the bodies for controlling the direction of the vehicle and the braking system, so that this trajectory is effectively followed. The trajectory must be understood here in its mathematical definition, that is to say as being the set of successive positions which must be occupied by the autonomous vehicle over time. Assistance systems must therefore define not only the path to take, but also the speed profile to be observed. To do this, they use a lot of information on the immediate environment of the vehicle (presence of obstacles such as pedestrians, cycles or other motorized vehicles, detection of traffic signs, road configuration ...), coming from various detection means using cameras, radars, lidars equipping the vehicle, as well as information linked to the vehicle itself, such as its speed, its acceleration, its position given for example by a GPS type navigation system. From this different information, a trajectory planning module included in the assistance system determines the trajectory to follow.
    Car manufacturers and equipment manufacturers have recently become interested in a specific feature for assisting driving on fast lanes such as a highway in heavy traffic conditions. The purpose of this ad hoc assistance is to allow the vehicle to move automatically on suitable sections of road, such as expressways with separate roads. The system is envisaged to operate below a certain speed, typically of the order of 50-70 km / h, and mainly in traffic jam conditions, to allow the car to follow itself the traffic lanes, while adapting its speed to traffic and to regulatory speed. Such an automated driving mode does not require continuous monitoring by the driver. The latter can thus perform a secondary activity. During an automated driving phase, the system can nevertheless ask the driver to regain control of his vehicle, in the event that he leaves, for example, a suitable section of road or in the event of faster driving speed.
    To achieve such functionality, the use of a navigation module, for example of the GPS type, on board the vehicle, might seem sufficient since such a navigation module provides the attributes of the road on which we are (in the occurrence of the functional class and the speed limit of the road taken, making it possible to derive the type of road and in particular to know whether one is on a motorway or other), attributes from which one could model the road context, and therefore authorize the activation of the automated mode if you are on a motorway-type expressway. However, a navigation module alone is not considered sufficient in terms of security because its information may contain errors. In addition, a navigation module is not always operational when driving.
    However, one way to recognize that a vehicle is moving on a motorway-type expressway is the presence of a safety rail separating the roadways (reverse traffic lanes) from this expressway.
    In a known system of this type currently being developed and tested, and the operation of which is shown diagrammatically in FIG. 1, the activation of the automatic driving mode on fast track can be authorized (step 60 in FIG. 1) as soon as we know, through a navigation module 10, that we are on a fast track and that we have succeeded in detecting, with a sufficient confidence index, the presence of the safety rail on this expressway. More precisely, the measurements supplied continuously by a laser scanner sensor 20 on board the vehicle are used to detect the presence of the safety rail, and to model this rail. In addition, obstacle detection, such as third-party vehicles located at the front of the vehicle, is carried out in parallel from images captured by a front camera 30 also on board the vehicle.
    To illustrate the principle of detection of the safety rail, FIGS. 2a, 2b and 2c illustrate, by way of example, the points of impact of the laser scanner sensor 20 obtained for different driving situations of a vehicle 1 equipped with the previous system, this vehicle 1 moving on a highway with a separating safety rail 2. In the three figures, the cone 3 illustrates the observation area of the laser scanner sensor 20. The reference 4 represents the obstacles, typically third-party vehicles , present on the front area of the vehicle 1, detected moreover thanks to the images captured by the front camera 30. In the case of FIG. 2a (light traffic at the front of the vehicle 1), it can be seen that the presence of the third vehicle 4 is not troublesome for determining the presence of the safety rail 2. All of the impact points located along this rail, represented in the form of circles, will be used for modeling the rail, for example in using Ransac's algorithm (abbreviation used for RANdom SAmple Consensus) or equivalent, used to determine a straight line that best fits the impact points obtained. To discriminate the measurements to be used for modeling the rail, between the impact points located along the rail 2 and those resulting from the presence of a third vehicle 4 (represented by squares), the detection information is advantageously used. obstacles resulting from the processing of images captured by the camera 30.
    FIGS. 2b and 2c on the other hand show cases where the traffic at the front of the vehicle 1 is much greater, with in particular the presence of third-party vehicles 4 which obstruct the action of the laser scanner 20 in particular on the safety rail 2 Thus, all the impact points represented in the form of triangles along the rail 2 above the straight line 5 are in practice not available and therefore cannot be used for the detection and modeling of the safety rail. It is therefore intuitively conceived that the detection of a safety rail by the laser scanner sensor 20 is less reliable in the case of heavy traffic, due to a smaller number of impact points available for modeling the safety rail. detected.
    Thus, with the previous system, and as shown diagrammatically in FIG. 1, provision is made for a step 40 of detecting the presence and modeling of the safety rail 2, carried out on the basis of measurements carried out continuously by at least the scanner sensor. laser 20, includes the determination of a confidence index I conf associated with the detection of the rail by the laser scanner sensor 20.
    As described above, this Iconf confidence index will be variable as a function of the traffic conditions at the front of the vehicle, these traffic conditions can be modeled by a parameter O representative of the number and / or of the position of the obstacles detected at the front of the vehicle 1, in other words an occupancy grid at the front of the vehicle 1. The curve Ci in FIG. 3 illustrates an example of variations of this Iconf confidence index as a function of the parameter O.
    This confidence index, recalculated in real time, is compared during a step 50, to a confidence threshold l t h, so that it is possible to authorize the activation of the automatic driving mode (step 60). when the confidence index Iconf is greater than the confidence threshold lth for at least a predefined duration corresponding to a minimum driving distance D t h traveled by the motor vehicle 1.
    The system furthermore provides for the maintenance of the automatic driving mode as long as a current confidence index associated with the detection is greater than the confidence threshold lth and the deactivation of the automatic driving mode when the current confidence index drops below the confidence threshold.
    One of the problems posed by the previous system resides in the fact that, when the vehicle 1 has just been authorized to switch to automatic driving mode thanks to the detection of the safety rail associated with a sufficient confidence index, the conditions of traffic can be brought to evolve very quickly, in particular by an increase in traffic. In some cases, this can cause the system to quickly deactivate the automatic driving mode, even when the functionality is supposed to remain active in traffic conditions.
    The present invention aims to address this particular problem.
    To do this, the invention relates to a method of assisting in driving a motor vehicle on a fast lane with roads separated by a safety rail comprising:
    a step of detecting the presence and modeling of the safety rail from measurements carried out continuously by at least one laser scanner sensor mounted on the motor vehicle, comprising the determination of an Iconf confidence index associated with the detection by the laser scanner sensor;
    a step of activating an automatic driving mode when said confidence index Iconf is greater than a confidence threshold l t h for at least a predefined duration corresponding to a minimum driving distance D t h traveled by the motor vehicle ;
    a step of maintaining said automatic driving mode as long as a current confidence index associated with the detection is greater than said confidence threshold l t h; and
    -a step of deactivating said automatic driving mode when the current confidence index falls below said confidence threshold;
    said method further comprising a step of estimating the traffic density at the front of the motor vehicle from images captured by an on-board camera on said motor vehicle, method in which, after activation of said automatic driving mode, said current confidence index taken into account in the deactivation and maintenance steps is a function of a sum of the Iconf confidence index associated with the detection of the presence of the safety rail and the estimated traffic density.
    In addition to the main characteristics which have just been mentioned in the preceding paragraph, the method according to the invention may have one or more additional characteristics among the following:
    the Iconf confidence index associated with detection by the laser scanner sensor is advantageously determined as a function of the number and / or of the position of objects detected at the front of the motor vehicle from images captured by the camera;
    - the modeling of the safety rail preferably uses the measurements made continuously by the laser scanner sensor, from which the measurements coinciding with objects detected at the front of the motor vehicle have been removed from the images captured by the camera.
    The invention also relates to a system for assisting the driving of a motor vehicle on a fast lane with roads separated by a safety rail comprising a laser scanner sensor and a camera on board said motor vehicle, said system being configured to implement the following steps:
    a step of detecting the presence and of modeling the safety rail from measurements carried out continuously by at least said laser scanner sensor, comprising the determination of an Iconf confidence index associated with the detection by the laser scanner sensor;
    a step of activating an automatic driving mode when said confidence index Iconf is greater than a confidence threshold Ith for at least a predefined duration corresponding to a minimum driving distance D t h traveled by the motor vehicle;
    a step of maintaining said automatic driving mode as long as a current confidence index associated with the detection is greater than said confidence threshold l t h;
    -a step of deactivating said automatic driving mode when the current confidence index falls below said confidence threshold; and
    a step of estimating the traffic density at the front of the motor vehicle from images captured by said camera, a system in which, after activation of said automatic driving mode, said current confidence index taken into account in the deactivation and maintenance steps is a function of a sum of the Iconf confidence index associated with the detection of the presence of the safety rail and the estimated traffic density.
    The invention will be better understood on reading the following description, made with reference to the appended figures, in which:
    - Figure 1, already described, describes possible steps for activating an automatic driving mode on detection of the presence of a safety rail separating the carriageways from a fast lane;
    - Figures 2a, 2b and 2c, described above, illustrate three examples of impact points obtained by a laser scanner sensor in three different driving situations;
    - Figure 3 schematically shows the pattern of variations in a confidence index associated with the detection of a safety rail, depending on the occupancy of the environment located in front of the vehicle;
    - Figure 4 illustrates steps of a driving assistance process in a possible implementation according to the invention;
    - Figure 5 shows schematically the pattern of variations in traffic density depending on the occupancy of the environment located in front of the vehicle; - Figure 6 schematically shows the shape of the variations of a current confidence index associated with the detection of a rail and calculated according to the principles of the invention.
    With reference to FIG. 4, and as already described previously in relation to FIG. 1, a driving assistance system for a motor vehicle using a fast lane with roads separated by a safety rail comprises a laser scanner sensor 20 and a front camera 30 on board the vehicle. The system also preferably uses information supplied by a navigation module 10, for example of the type
    GPS, in particular the attributes delivered by such a module 10 making it possible to know that the vehicle is indeed on a fast track with roadways separated by a safety rail.
    All of this data will be able to be combined in a step 40 in order to allow presence detection and modeling of the safety rail, detection to which an Iconf confidence index can be associated. We can use a formalism of the Dempster-Shafer type, or any other learning algorithm like Adaboost or SVM (English initials set for Support Vector Machine) to deliver security rail detection information with its associated confidence index.
    The modeling of the safety rail 2 uses the measurements made by the laser scanner sensor 20 (impact points), from which the measurements coinciding preferably with objects detected at the front of the motor vehicle have been removed from the images captured by the camera. One can use for this modeling the aforementioned algorithm of Ransac, or any other equivalent algorithm making it possible to determine a line which is adjusted as well as possible to the retained points of impact.
    The Iconf confidence index associated with detection by the laser scanner sensor 20 is advantageously determined as varying as a function of the number and / or of the position of objects detected at the front of the motor vehicle from images captured by the camera. (see curve Ci in FIG. 3 which gives an example of variations of this Iconf confidence index as a function of the parameter O representative of the number and / or of the position of the obstacles detected by the camera at the front of the vehicle). The confidence index can also take into account other information resulting from image processing, for example the identification of the marking lines on the ground allowing to know if the vehicle is moving on a track more or less close to the rail. of security.
    The activation of the driving mode is triggered by the system when the confidence index Iconf is greater than a confidence threshold Ith for at least a predefined duration corresponding to a minimum driving distance D t h traveled by the motor vehicle (steps 50 and 60).
    To avoid, once this mode is activated, an unwanted deactivation even though the vehicle is still on the fast lane with intensifying traffic conditions, it is planned to complete the treatment with the following steps:
    An estimate is made (step 70) of the traffic density at the front of the motor vehicle from images captured by the front camera 30, and more specifically from third-party vehicles detected by the processing of these images. Several parameters can be used, alone or in combination, to establish an estimate, denoted Dens below and in the figures, of the traffic density:
    A first parameter Pi relates to the observation of the environment of the vehicle, linked to the presence of third-party vehicles at the front of the vehicle 1. This first parameter is for example a function of the number of third-party vehicles detected over a given time window (for example for one minute) using the images from the camera 30, an estimate of the overall average speed of these third-party vehicles, and the speed limit on the fast lane (obtained via the navigation module 10).
    A second parameter P2 relates to the movement behavior of the vehicle 1, depending for example on its average speed observed over a given time window (for example for one minute), the number of positive or negative accelerations observed, and the limitation of speed on the expressway (obtained via the navigation module 10).
    A third possible parameter P3 relates to the movement behavior of vehicle 1 relative to the third vehicle closest to vehicle 1. This third parameter is a function, for example, of the average speed of vehicle 1 observed over a given time window (for example during one minute), the number of positive or negative accelerations observed, the average distance between vehicle 1 and the nearest third vehicle, and the speed limit on the expressway (obtained via the navigation module 10).
    Other parameters can be envisaged, such as, in the case where certain detected third vehicles are two-wheelers, a parameter P4 relating to the behavior of the two wheels with respect to detected third vehicles, in particular a comparison of the speeds of these two wheels with those of third-party vehicles.
    The Dens estimate of traffic density can then be obtained from a weighted sum of the different parameters used, for example according to the equation:
    Dens = -
    Σ α Λ k = 1
    k = 1 in which represents the weighting coefficient associated with each parameter P p .
    The curve C2 represented in FIG. 5 illustrates the variations in the density of traffic Dens as a function of the parameter O.
    At the end of step 70, the estimated traffic density is used in the calculation of a new confidence index I'conf associated with the detection of the safety rail, which is a function of a sum of the index Trust Iconf associated with the detection of the presence of the safety rail and the estimated traffic density.
    This can be formulated mathematically by the expression ^ ONF min ( I CONF + Dens; 1)
    In other words, since it is already known that a safety rail has been detected with a sufficient confidence index, and that as a result, the automatic mode has been activated (step 60), the value is increased the confidence index to avoid premature deactivation of the automatic driving mode due to increased traffic.
    The confidence index I'conf calculated in step 80 may possibly, during a step 90, be reduced by a value taking into account the distance traveled by the motor vehicle 1 during which the confidence index I conf is weak.
    It is this new value of the confidence index I'conf which is taken into account in the comparison (step 100) with the confidence threshold l t h to decide whether the automatic driving mode can be maintained (step 120). or on the contrary be deactivated (step 110) depending on whether the value of the confidence index I'conf is greater or less than the confidence threshold
    The curve C3 represented in FIG. 3 illustrates the variations of the confidence index I’conf as a function of the parameter O, resulting from the sum of the curves Ci and C2. By comparing with the curve Ci of FIG. 3, we note that by taking into account the Dens estimate of the traffic density in the calculation of the confidence index, we maintain a greater value of this confidence when the traffic intensifies, reducing the risk of unwanted deactivation of the automatic driving mode.
    When traffic becomes fluid again, two scenarios can arise: either the detection of the safety rail is associated with a sufficient confidence index, so that the automatic driving mode will be maintained, or the confidence index becomes too low and leads to deactivation of the automated driving mode.
    Of course, other conditions independent of the calculation of the conf may require immediate deactivation of the automatic driving mode, such as detection by the camera that the vehicle is crossing an exit line, entering a city or the presence of a traffic light, or the fact that navigation predicts an unfavorable context to that of a road with a separate carriageway.
    权利要求:
    Claims (5)
    [1" id="c-fr-0001]
    1. A method of assisting in driving a motor vehicle (1) on a fast lane road separated by a safety rail (2) comprising:
    a step (40) of detecting the presence and of modeling the safety rail (2) from measurements carried out continuously by at least one laser scanner sensor (20) mounted on the motor vehicle (1), comprising the determination of 'an Iconf confidence index associated with detection by the laser scanner sensor (20);
    a step (60) of activating an automatic driving mode when said confidence index Iconf is greater than a confidence threshold I t h for at least a predefined duration corresponding to a minimum driving distance D t h traveled by the motor vehicle (1);
    a step (120) of maintaining said automatic driving mode as long as a current confidence index associated with the detection is greater than said confidence threshold l t h; and
    - a step (110) of deactivating said automatic driving mode when the current confidence index falls below said confidence threshold;
    said method further comprising a step (70) of estimating the traffic density at the front of the motor vehicle (1) from images captured by a camera (30) on board said motor vehicle (1), method in which, after activation (60) of said automatic driving mode, said current confidence index taken into account in the deactivation and maintenance steps (110, 120) is a function of a sum of the Iconf confidence index associated with detection of the presence of the safety rail and the estimated traffic density.
    [2" id="c-fr-0002]
    2. Method according to claim 1, characterized in that the Iconf confidence index associated with the detection by the laser scanner sensor (20) is determined as a function of the number and / or of the position of objects detected at the front of the motor vehicle (1) from images captured by the camera (30).
    [3" id="c-fr-0003]
    3. Method according to any one of the preceding claims, characterized in that the modeling of the safety rail (2) uses the measurements carried out continuously by the laser scanner sensor (20), from which the measurements coinciding with objects have been removed. detected at the front of the motor vehicle (1) from images captured by the camera (30).
    [4" id="c-fr-0004]
    4. Assistance system for driving a motor vehicle (1) on a fast lane road separated by a safety rail (2) comprising a laser scanner sensor (20) and a camera (30) on board said vehicle automotive (1), said system being configured to implement the following steps:
    a step (40) of detecting the presence and modeling of the safety rail (2) from measurements carried out continuously by at least said laser scanner sensor (20), comprising the determination of an Iconf confidence index associated with detection by the laser scanner sensor (20); a step (60) of activating an automatic driving mode when said confidence index Iconf is greater than a confidence threshold l t h for at least a predefined duration corresponding to a minimum driving distance D t h traveled by the motor vehicle (1);
    a step (120) of maintaining said automatic driving mode as long as a current confidence index associated with the detection is greater than said confidence threshold l t h;
    - a step (110) of deactivating said automatic driving mode when the current confidence index falls below said confidence threshold; and
    a step (70) of estimating the traffic density at the front of the motor vehicle (1) from images captured by said camera (30), a system in which, after activation (60) of said driving mode automatic, said current confidence index taken into account in the deactivation and maintenance steps (110, 120) is a function of a sum of the Iconf confidence index associated with the presence detection
    [5" id="c-fr-0005]
    5 of the safety rail and the estimated traffic density.
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    同族专利:
    公开号 | 公开日
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    EP3519267A1|2019-08-07|
    US20210276563A1|2021-09-09|
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    法律状态:
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    2018-03-30| PLSC| Search report ready|Effective date: 20180330 |
    2018-09-28| PLFP| Fee payment|Year of fee payment: 3 |
    2019-09-30| PLFP| Fee payment|Year of fee payment: 4 |
    2020-09-30| PLFP| Fee payment|Year of fee payment: 5 |
    2021-09-30| PLFP| Fee payment|Year of fee payment: 6 |
    优先权:
    申请号 | 申请日 | 专利标题
    FR1659239A|FR3056532B1|2016-09-28|2016-09-28|DRIVING ASSISTANCE ON HIGHWAYS WITH SEPARATE ROADS THROUGH A SAFETY RAIL|
    FR1659239|2016-09-28|FR1659239A| FR3056532B1|2016-09-28|2016-09-28|DRIVING ASSISTANCE ON HIGHWAYS WITH SEPARATE ROADS THROUGH A SAFETY RAIL|
    PCT/EP2017/074710| WO2018060379A1|2016-09-28|2017-09-28|Driving assistance on a highway with carriageways separated by a guardrail|
    EP17772444.0A| EP3519267B1|2016-09-28|2017-09-28|Driving assistance on a highway with carriageways separated by a guardrail|
    US16/336,679| US20210276563A1|2016-09-28|2017-09-28|Assistance in driving on a fast road with carriageways separated by a safety rail|
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