Abstract:
An object collision warning system for a motor vehicle comprises a sensing means ( 11 ) adapted to sense a surrounding of the motor vehicle and a processing means ( 14 ) adapted to detect objects in a surrounding of the motor vehicle by processing a signal provided by the sensing means ( 11 ), to perform an estimation of a collision probability between the vehicle and the detected object, and to output a corresponding signal in case the collision probability is non-negligible. The processing means ( 14 ) is adapted to determine, after having passed a curve, information describing the passed curve, to store the curve describing information, and to use the curve describing information of at least one previously passed curve for determining the vehicle path in a current curve in the estimation of the collision probability.

Description:
CROSS-REFERENCE TO RELATED APPLICATIONS 
     This application claims priority to European Patent Application No. 10000685.7, filed Jan. 25, 2010 and PCT/SE2010/051459, filed Dec. 22, 2010. 
     FIELD OF THE INVENTION 
     The invention relates to an object collision warning system for a motor vehicle of the type having a sensing means adapted to sense a surrounding of the motor vehicle and a processing means, said processing means being adapted to detect objects in a surrounding of the motor vehicle by processing a signal provided by said sensing means, to perform an estimation of a collision probability between the vehicle and the detected object, and to output a warning signal in case the collision probability is non-negligible. The invention relates furthermore to a corresponding object collision warning method. 
     BACKGROUND OF THE INVENTION 
     US 2005 0225477 A1 discloses a vehicle collision warning system comprising a road curvature estimation subsystem for estimating the curvature of the roadway using measurements from host vehicle motion sensors, a target state estimation subsystem for estimating the state of a target vehicle on the basis of a radar measurement, and a control subsystem for determining whether or not the host vehicle is at risk of collision with the target, and if so, for determining and effecting corresponding action. 
     The goal of a pedestrian warning system is to warn the driver of the vehicle if there is a pedestrian on the road ahead of the vehicle or close to the road and walking towards the road. To be able to warn correctly for the pedestrian the system must know where the pedestrian is and where the road is ahead of the vehicle, which is particularly difficult in curves. For a warning to be meaningful for the driver, the warning must be activated several seconds before the predicted time of collision. However, predicting a curved path of the vehicle based on the vehicle dynamics generally works well at most a few hundred milliseconds ahead. 
     SUMMARY OF THE INVENTION 
     The object of the invention is to provide an object collision warning system and method with an improved prediction performance. 
     The invention solves this object with the features of the claims. The invention is based on the assumption that consecutive curves belonging to the same type of road have similar characteristics. Therefore, using stored curve parameters describing previously passed curves allows to predict how the next curve to be passed will behave already at the beginning of the curve, and not only in the middle of the curve as is the case if the vehicle dynamics alone are used to predict the vehicle path. Consequently, a collision risk in particular with an object in a curve can reliably be determined significantly earlier than in the prior art. 
     After having passed a curve, curve information describing this curve is preferably determined from measured vehicle motion variables, including but not limited to vehicle yaw as determined from a yaw sensor and/or vehicle speed, and stored preferably in an electronic memory. Curve information suited for describing a curve preferably includes one or more curve variables like curve length, curve radius and/or a prediction reliability. 
     Preferably the memory is adapted to store a plurality of information sets describing a plurality of previously passed curves. The use of information from a plurality of previously passed curves may lead to an enhanced quality of the vehicle path prediction in comparison to using information from the last passed curve only. If information from a plurality of previously passed curves is used, the curve information of different curves is preferably weighted in said estimation of the collision probability. In particular, the weight of the curve information is chosen smaller for a curve which has been passed longer ago, since the current curve may be assumed to be most similar to the most lastly passed curves. 
     The sensing means preferably is an imaging means adapted to record images from a surrounding of the motor vehicle. However, the invention is not limited to imaging means or vision systems, but is also applicable to non-vision sensing means based for example on lidar, radar or the like. 
    
    
     
       BRIEF DESCRIPTION OF THE DRAWINGS 
       In the following the invention shall be illustrated on the basis of preferred embodiments with reference to the accompanying drawings, wherein: 
         FIG. 1  shows in diagrammatic form a safety system for a motor vehicle; 
         FIG. 2  shows a schematic diagram explaining the prediction of the forthcoming vehicle path; and 
         FIG. 3  shows a schematic diagram explaining the update of parameters used for the calculation of curve information. 
     
    
    
     DETAILED DESCRIPTION OF THE INVENTION 
     The safety/vision system  10  is mounted in a motor vehicle and comprises an imaging means  11  for recording images of a region surrounding the motor vehicle, for example a region in front of the motor vehicle. Preferably the imaging means  11  comprises one or more optical and/or infrared imaging devices  12   a ,  12   b , in particular cameras, where infrared covers near IR with wavelengths below  5  microns and/or far IR with wavelengths beyond 5 microns. Preferably the imaging means  11  comprises a plurality of imaging devices  12   a ,  12   b  in particular forming a stereo imaging means  11 ; alternatively only one imaging device forming a mono imaging means can be used. 
     The imaging means  11  is preferably coupled to an image pre-processor  13  adapted to control the capture of images by the imaging means  11 , receive the electrical signal containing the image information from the image sensors  12   a ,  12   b , warp pairs of left/right images into alignment and create disparity images, which per se is known in the art. The image pre-processor  13  may be realized by a dedicated hardware circuit. Alternatively the pre-processor  13 , or part of its functions, can be realized in the electronic processing means  14 . 
     The pre-processed image data is then provided to an electronic processing means  14  where image and data processing is carried out by corresponding software. In particular, possible objects surrounding the motor vehicle, such as pedestrians, other vehicles, bicyclists or large animals, are identified, which preferably includes classification and verification steps. The position of identified objects in the recorded images is tracked over time. Information relating to an identified object is preferably displayed to the driver on a display means  19 . 
     Furthermore, an expected path of the vehicle is calculated on the basis of vehicle dynamics information obtained from vehicle sensors  15 ,  16 , and  17  comprising a speed sensor  15 , a yaw sensor  16  and/or a steering angle sensor  17 . When the processing means  14  estimates on the basis of the position of an identified object in the scene and the expected path of the vehicle that there is a non-negligible risk of collision, the processing means  14  outputs a corresponding signal in order to activate or control one or more vehicle safety means  18  in a suitable manner. For example, means  18  could be in the form of a warning adapted to warn the driver is preferably activated. Such a warning may suitably provide optical, acoustical and/or haptical warning signals, which includes displaying an optical warning on the display means  19 . Further safety means  18  may be activated or suitably controlled, for example restraint systems such as occupant airbags or safety belt tensioners; pedestrian airbags, hood lifters and the like; or dynamic vehicle control systems such as brakes. 
     The electronic processing means  14  is preferably programmed or programmable and may comprise a microprocessor or micro-controller. Expediently, the electronic processing means  14  has access to an electronic memory means  25 . The image pre-processor  13 , the electronic processing means  14  and the memory means  25  are preferably realized in an on-board electronic control unit (ECU) and may be connected to the imaging means  11  via a separate cable or alternatively via a vehicle data bus. In another embodiment the ECU and a camera of imaging means  12   a ,  12   b  can be integrated into a single unit. All steps from imaging, image pre-processing, image processing to activation or control of safety means  18  are performed automatically and continuously during driving in real time. 
     The determination of an expected path of the vehicle in the processing means  14  is explained in detail using  FIG. 2 . The input values  30  are obtained from vehicle dynamics sensors  15  to  17  and may in particular comprise the vehicle speed, yaw rate and steering angle. The input values  30  are continuously updated within fixed time intervals, and input into a Kalman filter  31  providing filtered vehicle parameters  32 , in particular a filtered yaw rate and filtered vehicle speed. 
     The filtered vehicle parameters  32  are provided to a change detector  33  which is adapted to detect changes between straight road and curve. Output  34  of the change detector  33  are the last start time of a curve, the last end time of a curve and an indicator indicating whether the vehicle currently is in a curve or on a straight road. From the last start time of a curve, the current vehicle speed and yaw rate, the time when the vehicle has passed half of the curve is estimated in the half time estimator  35 . The start time and end time of the last curve, the half time output by the half time estimator  35 , as well as the speed and yaw rate of the last curve are stored in a memory  36  which may be realized in the electronic memory means  25  shown in  FIG. 1 . 
     When the vehicle drives through a curve, the expected curve length of the current curve and the expected total curve bending/radius of the current curve can be extracted in corresponding lookup tables  37 ,  38  stored in a memory, for example memory means  25  shown in  FIG. 1 . In the table  37  values of curve length are stored for the ranges of yaw rate and speed occurring in practice. In the table  38  values of total curve bending are stored for the ranges of yaw rate and speed occurring in practice. The use of tables  37 ,  38  is preferred because it is easier to update tables based on new measurements in comparison to updating a corresponding algorithm. 
     Based on the information from the curve length table  37 , the curve bending table  38  and the curve half time estimator  35 , final values for the estimated curve length of the current curve and the bending of the current curve are calculated in the curve length and bending estimator  39 . Based on the estimated curve length and curve bending, and information on the last curve stored in the memory  36 , the path of the vehicle is predicted in the vehicle path predictor  40 . The output  41  of the vehicle path predictor  40  may for example be longitudinal position and lateral position of the vehicle at certain forthcoming times. This vehicle path information  41  can be used for reliably estimating the probability of a collision with a detected object in front of the motor vehicle. 
     When a curve has ended, the exact curve length and curve bending of this last curve are calculated in the update section  42 . Based on these exact curve values of the lastly passed curve, the update section  42  then calculates new values for the tables  37 ,  38  employing a general model of curve progression. As an example, the general model may be based on general construction requirements, such that a curve usually has a start section with a linearly increasing curvature, a middle section of essentially constant curvature and an end section with a linearly decreasing curvature; a certain minimum length of the road in terms of minimum time, for example 3 s, at the speed limit of the road; etc. The new values for the tables  37 ,  38  are preferably calculated on the basis of information not only of the ultimately passed curve, but on a plurality of lastly passed curves, where the influence of a curve is preferably weighted with a decreasing weight, for example an exponentially decreasing weight, the longer ago the curve has been passed. The new values for the tables  37 ,  38  are then written into the tables  37 ,  38  in order to complete the table update. 
       FIG. 3  illustrates a general scheme for updating the parameters of a parametric curve describing model used in the calculation of curve information. When a curve is passed, curve describing variables like curve length, curve radius and/or a prediction error are calculated in step  43  using input values  30  describing the dynamics or kinematics of the vehicle, in particular vehicle speed, vehicle yaw and/or steering angle as measured with speed sensor  15 , yaw sensor  16  and/or steering angle sensor  17 ; and on the basis of a parametric curve model describing how to predict a curve from measurement values  30  and variable input parameters  44 . The determined curve variables are used in step  45  to calculate a loss function describing how good the model could predict the lastly passed curve. In step  46  update parameters which better fit the lastly passed curves are calculated based on the loss function, and fed back into the curve variable calculation  43 . Initially, if no updated parameters are yet available, a set of initial parameters  48  are used as input parameters in the curve variable calculation  43 . 
     While the above description constitutes the preferred embodiment of the present invention, it will be appreciated that the invention is susceptible to modification variation, and change without departing from the proper scope and fair meaning of the accompanying claims.