Patent Publication Number: US-11040714-B2

Title: Vehicle controller and method for controlling a vehicle

Description:
CROSS-REFERENCE TO RELATED APPLICATIONS 
     This application is a Continuation in Part Application that claims priority to U.S. Nonprovisional application Ser. No. 16/145,226, filed on Sep. 28, 2018, which is hereby incorporated by reference in its entirety. 
    
    
     TECHNICAL FIELD 
     Exemplary implementations described herein generally relate to vehicle controllers and methods for controlling a vehicle. 
     BACKGROUND 
     Autonomous driving, e.g. the use of self-driving vehicles, has a lot of potential benefits such as reduced mobility and infrastructure costs, increased safety, increased mobility, increased customer satisfaction, and reduced crime. These benefits also include a potentially significant reduction in traffic collisions, resulting injuries and related costs, including less need for insurance. Automated cars are predicted to increase traffic flow, provide enhanced mobility for children, the elderly, disabled, and the poor, relieve travelers from driving and navigation chores, lower fuel consumption, significantly reduce needs for parking space, reduce crime, and facilitate business models for transportation as a service, especially via the sharing economy. 
     For autonomous driving, a vehicle needs to estimate and evaluate possible future hazards in its surroundings. Furthermore, it is desirable to achieve a driving behavior of the vehicle to minimize risk and maximizing utility. Accordingly, efficient approaches for controlling a vehicle based on information about the vehicle&#39;s surroundings are desirable. 
    
    
     
       BRIEF DESCRIPTION OF THE DRAWINGS 
       In the drawings, like reference characters generally refer to the same parts throughout the different views. The drawings are not necessarily to scale, emphasis instead generally being placed upon illustrating the principles of the invention. In the following description, various aspects are described with reference to the following drawings, in which: 
         FIG. 1  shows an example of an autonomous driving scenario. 
         FIG. 2  shows a vehicle, for example corresponding to one of the vehicles of the autonomous driving scenario of  FIG. 1 . 
         FIG. 3  shows an example of a neural network for modeling an environment of a vehicle. 
         FIG. 4A  shows possible paths of a first car and a second car. 
         FIG. 4B  shows corresponding prototypical paths for the first car and the second car, respectively. 
         FIG. 5  shows an example of a convolutional neural network. 
         FIG. 6  illustrates the inputs and the output of a convolutional neural network for determining a target velocity profile for a vehicle. 
         FIG. 7  shows a vehicle controller. 
         FIG. 8  shows a flow diagram illustrating a method for controlling a vehicle, for example performed by a vehicle controller. 
     
    
    
     DESCRIPTION OF EXEMPLARY IMPLEMENTATIONS 
     The following detailed description refers to the accompanying drawings that show, by way of illustration, specific details and aspects of this disclosure in which the invention may be practiced. Other aspects may be utilized and structural, logical, and electrical changes may be made without departing from the scope of the invention. The various aspects of this disclosure are not necessarily mutually exclusive, as some aspects of this disclosure can be combined with one or more other aspects of this disclosure to form new aspects. 
       FIG. 1  shows an example of an autonomous driving scenario  100 . 
     In the example of  FIG. 1 , there is a crossing of two streets  101 . Vehicles  102  (e.g. cars, vans or bikes) are driving (or standing when waiting or being parked) on the streets  101 . There are also fixed objects  103  like traffic lights, signs, garbage boxes etc. near or on one of the streets  101 . Furthermore, there are pedestrians  104  or also animals  105  near or on one of the streets  101 . 
       FIG. 2  shows a vehicle  200 , for example corresponding to one of the vehicles  102 . 
     For autonomous driving, the vehicle  200  is provided with a vehicle controller  201 . The vehicle controller  201  includes data processing components, e.g. a processor (e.g. a CPU (central processing unit))  202  and a memory  203  for storing control software according to which the vehicle controller  201  operates and stores data based on which the processor  202  operates. The data stored in the memory  203  may for example include sensor data acquired by one or more sensors  204 . The one or more sensors  204  may for example include a camera for taking photos of the vehicle&#39;s environment, a radar sensor or a LIDAR (light detecting and ranging) sensor or others. 
     The data stored in the memory  203  may also include data the vehicle  200  receives from one or more other devices (e.g. another vehicle, a roadside unit or a network server), for example by means of V2X (vehicle-to-everything) communication. For this, the vehicle controller  201  may be coupled to (or include) a communication subsystem  205  of the vehicle  200 . The communication subsystem  205  supports radio communication with the one or more other devices, e.g. according to a mobile radio communication standard such as LTE (Long Term Evolution) or 5G (Fifth Generation) via a cellular network or also according to a direct communication (bypassing network side components). It may include corresponding components like a baseband processor, a transceiver one or more antennas etc. 
     The vehicle controller may perform environmental modeling, i.e. determine the presence of objects (fixed objects, pedestrians, animals, other vehicles) in the vehicle&#39;s surroundings, the velocity of the objects in the vehicle&#39;s surroundings, the width and direction of the street and possible intersecting streets etc. based on sensor data such as LIDAR data, radar data or one or more photos (e.g. greyscale or color photos by a camera) using a deep fusion neural network as illustrated in  FIG. 3 . 
       FIG. 3  shows an example of a neural network  300  for modeling an environment of a vehicle. 
     The neural network  300  receives a LIDAR bird view  301  of the vehicle&#39;s surroundings, a LIDAR front view  302  (from the point of view of the vehicle) and a front photo image  303  (from the point of view of the vehicle as input. The vehicle controller  201  may for example generate the LIDAR bird view  301  and the LIDAR front view  302  from LIDAR data collected by one of the sensors  204 . 
     The inputs  301 ,  302 ,  303  are each processed by respective sets of convolutional layers  304 ,  305 ,  306  of a 3D proposal network  307 . Based on the output of the convolutional layers  304  processing the LIDAR bird view, the 3D proposal network  307  generates 3D proposals  308 , using deconvolution, object classification and 3D box regression  309 . 
     The outputs of the sets of convolutional layers  304 ,  305 ,  306  are further processed by respective deconvolution  310 ,  311 ,  312  and are supplied, along with the 3D proposals, to a region-based fusion network  308 . The 3D proposals  308  give rise to bird view proposals  313 , front view proposals  314  and image proposals  315 . 
     The region-based fusion network  308  processes the outputs of the deconvolutions  310 ,  311 ,  312 , together with the bird view proposals  313 , front view proposals  314  and image proposals  315 , respectively, by means of respective ROI (region of interest) pooling  316 ,  317 ,  318 . The region-based fusion network  308  then performs deep fusion  319  of the outputs of the pooling  316 ,  317 ,  318 . Based on the output of the deep fusion, it generates the model of the environment (e.g. using multiclass classification and 3D box regression). 
     For a given path of a vehicle, given velocities of a vehicle along the path and a given environment of the vehicle, a certain risk arises. Risk can be defined as ‘the probability of something happening multiplied by the resulting cost or benefit if it does’. In the following, examples for risk calculation based on risk functions is described. 
     It is in the following assumed that the road map based on which risk is calculated is correct. During driving of a vehicle it can be approximated that the vehicle is in the lane center. For the particular road participants some prototypical paths can be generated as hypothesis. This is illustrated in  FIGS. 4A and 4B . 
       FIG. 4A  shows possible paths  403 ,  404  of a first car  401  and a second car  402 . 
       FIG. 4B  shows corresponding prototypical paths  405 ,  406  for the first car  401  and the second car  402 , respectively. 
       FIG. 4A  can be seen to include all possible driving scenarios while  FIG. 4B  only shows prototypical hypothesis. 
     For automated driving, each vehicle  401 ,  402  should take care of the maneuvers of the other vehicle because their paths may intersect. 
     For risk calculation for a vehicle based on a risk function according to various examples, it is assumed that there are n objects in the environment of a vehicle and the ith vehicle has a state x t   i  such that there is a state set:
 
 x   t   =x{   t   0   ,x   t   1   , . . . ,x   t   n }  (1)
 
     Because the risk is probability that the event happens multiplied with cost damage the future risk in the time t+s can be defined as:
 
 r ( t+s )=∫ c   t+s   P ( c   t+s   |x   t ) dc   t+s   (2)
 
In the above P(c t+s |x t ) is the probability of damage (cost) c t+s  and the integration is done over the set of all costs.
 
     When the vehicle path (or trajectory) is exchanged with a prototypical path (or trajectory) fixed given state vectors can be used and calculation becomes much simpler. 
     The critical events are usually triggered by simple causes. In the following, it is described how the risk may be calculated for various risk sources. 
     For the vehicle to vehicle collision risk case an instantaneous event rate depends on the distance of an ego vehicle (i.e. the vehicle for which the risk is calculated) to the traffic participants. This even rate is larger for small distances and decreases. For a situation h t  it can therefore be modeled as
 
τ d   −1 ( {circumflex over (x)}   t+s ( x   t   ,h   t ), s )=τ d,0   −1   e   −β     d     (s)·max({circumflex over (d)}     t+s     (x     t     ,h     t     )−d     min     ,0)   (3)
 
In the above {circumflex over (d)} t+s (x t ,h) is the predicted distance between the ego vehicle and the other traffic participant after a time s and d min  is the minimal distance depending on the sizes of the objects limiting the possible physical overlap.
 
     For the risks of drifting off a curve the instantaneous event rate for a situation h t  can be modeled as
 
τ c   −1 ( {circumflex over (x)}   t+s ( x   t   ,h   t ), s )=τ c,0   −1   e   −β     c     (s)·max(v     c max     −{circumflex over (v)}     t+s     (x     t     ,h     t     ),0)   (4)
 
In the above {circumflex over (v)} t+s (x t ,h) is the predicted vehicle velocity and v c max  is the maximal allowed velocity in the curve. v c max =√{square root over (a c,max R)} depends on the maximum centrifugal acceleration of the object and radius of the curve.
 
     For the risk of losing control due to heavy breaking the instantaneous event rate for a situation h t  can be modeled as
 
τ b   −1 ( {circumflex over (x)}   t+s ( x   t   ,h   t ), s )=τ b,0   −1   e   −β     b     (s)·max(b     b max     −{circumflex over (b)}     t+s     (x     t     ,h     t     ),0)   (5)
 
Here, {circumflex over (b)} t+s (x t ,h) is the predicted acceleration and b b max  is the maximum acceleration allowed.
 
     In the above equations (3) to (5), the parameters τ d,0   −1 , τ c,0   −1 , τ b,0   −1 , define the respective event rate at minimal distance. β d (s), β c (s) and β b (s) define the steepness of the respective event rates and can be used to model state uncertainties. 
     For standard events, the cost function as used in equation (2) can be calculated analytically. 
     For vehicle to vehicle collision the cost function can be estimated as the energy. The energy depends on the masses and velocities of the participants. The cost for a situation h t  and an event e t+s  at time t+s can therefore be modelled as 
     
       
         
           
             
               
                 
                   
                     
                       
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     m 0  is the ego vehicle mass 
     m i  is another vehicle mass 
     {circumflex over (v)} t+s   0 (x t ,h t ) is the ego vehicle vector velocity 
     {circumflex over (v)} t+s   i (x t ,h t ) is the other vehicle velocity 
     Equation (6) can be seen to reflect the human natural behavior as well: we try to avoid the collision with bigger and high velocity vehicles more as compared with light ones. 
     For the risk of drifting of a curve or due to the heavy acceleration (lose control) the cost for a situation h t  and an event e t+s  at time t+s can be calculated with a similar equation but assuming velocity of the other participant to be zero and the mass to be infinite:
 
 ĉ   t+s ( e   t+s   ,{circumflex over (x)}   t+s ( x   t   ,h   t ))= w   c ½ m   0   ∥{circumflex over (v)}   t+s   0 ( x   t   ,h   t )∥ 2   (7)
 
     With the above equations and approximations it is possible to analytical calculate the driving risk of a vehicle in complex driving scenarios. 
     For example, the driving risk may be calculated according to (2) wherein the above formulas for the cost are used for calculating the cost in equation (2) and the above formulas for the event rate are used for calculating the probability. For example, assuming an event rate τ e     t+s     −1 ({circumflex over (x)} t+s ) depending on the predicted future state vector {circumflex over (x)} t+s  at future time t+s, an instantaneous event probability for small time intervals [t+s, t+s+δt] can be derived as
 
 P ( e   t+s   |{circumflex over (x)}   t+s ( x   t   ,h   t ))=τ e     t+s     −1 ( {circumflex over (x)}   t+s )δ t   (8)
 
     Based on the risk, a target velocity profile (assuming a given path) can be generated: it is the velocity profile which minimizes the overall risk during the driving along the path of the vehicle. This may be done by calculating the gradient decent on the risk function with respect to velocity of the vehicle. 
     According to various embodiments, the vehicle controller uses a convolutional neural network for determining a target velocity or a plurality of target velocities along the (target) path of the vehicle, i.e. a target velocity profile for the vehicle path. 
     The convolutional neural network may be trained using labeled data (i.e. training data) generated by analytical calculation of the optimal vehicle profile (minimizing risk), e.g. based on the above formulas. The training may be performed before the vehicle controller is deployed in a vehicle or a program code implementing a convolutional neural network trained in this way may be loaded into the memory to be executed by the processor  202 . 
       FIG. 5  shows an example of a convolutional neural network  500 . 
     The convolutional neural network  500  receives its input in the form of a plurality of images  501 . The images  501  do not have to correspond to actual images (e.g. photographs) but each image may just be seen as a 2-dimensional array of values (corresponding to pixel values of an image). The values may be real values (such as a greyscale value of an image) but the 2-dimensional array may also include a plurality of values, e.g. a pair or triple of value at each position (i.e. “pixel”) of the array (such as RGB components for each pixel of an image). 
     For the input, the convolutional neural network  500  may include an input node for each pixel value of each image. The images  501  input in this way are then processed by a first convolution and non-linearity  502 , formed by a first pooling  503 , by a second convolution and non-linearity  504  and a second pooling  505  (and possibly one or more further convolution and non-linearity stages and pooling stages) and lastly by a fully-connected neural network (with one or more layers)  506 . The output is given by output nodes  507 . 
     In the example of  FIG. 5 , the number of nodes is reduced by the pooling and a relatively low number of output nodes are shown. However, the number of output nodes may be higher and may for example be such that the output again corresponds to an image (possibly with lower resolution) as the input images  501 . 
     For the application as described above the output may for example be an output image showing the target velocity profile (i.e. showing a graph specifying a target velocity for each point (according to the x-resolution of the image) along the path of the vehicle). 
       FIG. 6  illustrates the inputs  601 ,  602 ,  603  and the output  604  of a convolutional neural network for determining a target velocity profile for a vehicle. 
     As described above, the output is an image showing a velocity profile in terms of a graph. 
     As input, the convolutional neural network receives an image representing, for the surroundings of the vehicle, a first velocity map  601  for velocities in a first direction (x direction). This means that for each pixel of the image, the first velocity map  601  specifies (by means of the pixel value for the pixel) the velocity in x-direction of an object located at the position in the surroundings of the vehicle to which the pixel corresponds. It should be noted that since an image (or map) corresponds to the surroundings of the vehicle (i.e. represents the geographical area around the vehicle), each pixel corresponds to a location and thus possibly to an object, namely the object located at the position of the surroundings of the vehicle to which the pixel corresponds (or which the pixel “shows”). The object may be a movable object but may also be immovable object. In the latter case, the velocity is zero. Also for non-moving areas like the street or the lawn the velocity is zero. 
     Similarly, the convolutional neural network receives an image representing, for the surroundings of the vehicle, a second velocity map  602  for velocities in a first direction (x direction). 
     Further, the convolutional neural network receives an image representing, for the surroundings of the vehicle, a priority map  603 . This means that for each pixel of the image, the priority map  603  specifies (by means of the pixel value for the pixel) the priority of an object located at the position in the surroundings of the vehicle to which the pixel corresponds. The priority of an object may be the importance level at which a collision with the object should be avoided. This may for example be higher for a pedestrian than for a garbage box (and may be zero for areas where the vehicle can drive or is even supposed to drive (like available parts of the street). The object priority map may be used to differentiate the objects sizes, types and masses. A pedestrian may for example have highest probability. An object with high mass will cause more problems during a collision and therefore have higher cost function and may be assigned a higher priority to model this. The trucks with petrol or some dangerous materials will also have high cost functions and may be assigned a higher priority to model this. 
     Additionally, the convolutional neural network receives an image showing the location of objects and prototypical driving paths (e.g. as shown in  FIG. 4B ). 
     The vehicle controller  201  may generate the inputs based on sensor information like radar sensor data, LIDAR mono camera image data provided by the one or more sensors  204 , for example using another neural network for environment modelling as described with reference to  FIG. 3 . Such sensor-based inputs may also be used for training the convolutional neural network. 
     Instead of giving the target velocity profile in form of a graph, the convolutional neural network may also be configured (e.g. by means of corresponding training) to give a risk as two dimensional function (i.e. as an image) where each pixel value specifies a risk depending on the driving distance and velocity. The vehicle controller  201  may then determine the target velocity profile using this function, e.g. by selecting the target velocity profile which minimizes the overall risk. 
     In summary, according to various embodiments, a vehicle controller is provided as illustrated in  FIG. 7 . 
       FIG. 7  shows a vehicle controller  700 . 
     The vehicle controller  700  includes a determiner  701  configured to determine information about surroundings of a vehicle, the information about the surroundings including information about velocities of objects in the surroundings of the vehicle. 
     The vehicle controller  700  further includes a velocity controller  702  configured to input the information about the surroundings of the vehicle and a specification of a path of the vehicle to a convolutional neural network  703 , to determine a target velocity of the vehicle along the path based on an output of the convolutional neural network  703  and to control the vehicle according to the determined target velocity. 
     According to various examples, in other words, a vehicle controller is configured to determine a target velocity (or a target velocity profile) along a path of a vehicle by means of a convolutional neural network. The convolutional neural network may be trained (or may have been trained) based on analytically generated labeled data, for example in such a way that it determines the target velocity (or the target velocity profile) to minimize risk along the path of the vehicle. 
     For training, i.e. for generating the training data, a driving risk function may be analytically derived, e.g. as described above. The driving risk function may include the risk of collision in several scenarios like parallel driving, turning in the crossing, etc. As the event cost function several sources may make contributions: vehicle-to-vehicle collisions, losing control because of heavy breaking and accident in curve because of very high velocity. The risk map (or risk function) which can be calculated analytically can be estimated with Deep Learning (e.g. by means of the convolutional neural network). An object velocity map, a map of the road matched to the ego vehicle location and an object priority map may be given as inputs to the Convolutional Neural Network (CNN) as different channels in the training. The priority map may represent object risk factors like size and weight. The deep learning output may be the risk function depending the driving path length and the velocity profile. The minimum of this risk function for the different driving distances gives the searched velocity profile. 
     According to various examples a method as illustrated in  FIG. 8  is performed. 
       FIG. 8  shows a flow diagram  800  illustrating a method for controlling a vehicle, for example performed by a vehicle controller. 
     In  801 , the vehicle controller determines information about surroundings of a vehicle, the information about the surroundings including information about velocities of objects in the surroundings of the vehicle. 
     In  802 , the vehicle controller inputs the information about the surroundings of the vehicle and a specification of a path of the vehicle to a convolutional neural network. 
     In  803 , the vehicle controller determines a target velocity of the vehicle along the path based on an output of the convolutional neural network. 
     In  804 , the vehicle controller controls the vehicle according to the determined target velocity. 
     The control process as described with reference to  FIG. 8  may be repeatedly performed, e.g. periodically, for example one or more times per second, e.g. once every 0.5 seconds. 
     The vehicle controller and its components (e.g. the velocity controller and the determiner) may for example be implemented by one or more processors. A “processor” may be understood as any kind of a logic implementing entity, which may be special purpose circuitry or a processor executing software stored in a memory, firmware, or any combination thereof. Thus a “processor” may be a hard-wired logic processor or a programmable logic processor such as a programmable processor, e.g. a microprocessor. A “processor” may also be a processor executing software, e.g. any kind of computer program. Any other kind of implementation of the respective functions which will be described in more detail below may also be understood as a “processor”. 
     The following examples pertain to further exemplary implementations. 
     Example 1 is a vehicle controller as illustrated in  FIG. 7 . 
     In Example 2, the subject-matter of Example 1 may optionally include the information about the surroundings further including information about priorities of objects. 
     In Example 3, the subject-matter of Example 2 may optionally include a priority of an object specifying an importance level at which a collision with the object should be avoided. 
     In Example 4, the subject-matter of any one of Examples 1-3 may optionally include the determiner being configured to determine the information about the surroundings of the vehicle and the velocity controller being configured to input the information about the surroundings of the vehicle and a specification of a path of the vehicle to the convolutional neural network, to determine a target velocity of the vehicle along the path based on an output of the convolutional neural network and to control the vehicle according to the determined target velocity repeatedly. 
     In Example 5, the subject-matter of any one of Examples 1-4 may optionally include the information about the surroundings including information about the velocity of objects in the surroundings of the vehicle. 
     In Example 6, the subject-matter of any one of Examples 1-5 may optionally include an environment modeler configured to model an environment of the vehicle, wherein the determiner is configured to determine the information about the surroundings of the vehicle at least partially based on the modelled environment. 
     In Example 7, the subject-matter of Example 6 may optionally include the environment modeler modelling the environment by means of a further neural network. 
     In Example 8, the subject-matter of any one of Examples 1-7 may optionally include the velocity controller being configured to input the information about the surroundings of the vehicle and the specification of the path of the vehicle to the convolutional neural network in the form of one or more two-dimensional images. 
     In Example 9, the subject-matter of any one of Examples 1-8 may optionally include the output of the convolutional neural network specifying a target velocity for each of a plurality of positions along the path of the vehicle. 
     In Example 10, the subject-matter of any one of Examples 1-9 may optionally include the convolutional neural network being configured to output a specification of a target velocity for each of a plurality of positions along the path of the vehicle in form of a two-dimensional image. 
     In Example 11, the subject-matter of any one of Examples 1-10 may optionally include the convolutional neural network being configured to output a specification of a risk depending on velocity and travel distance along the path. 
     In Example 12, the subject-matter of any one of Examples 1-11 may optionally include the convolutional neural network being configured to output the specification of the risk depending on velocity and travel distance along the path in form of a two-dimensional image. 
     In Example 13, the subject-matter of any one of Examples 1-12 may optionally include a path controller configured to determine a target vehicle path and to control the vehicle according to the determined target vehicle path. 
     In Example 14, the subject-matter of any one of Examples 1-13 may optionally include the convolutional neural network being trained based on training data generated based on an analytical representation of a driving risk function. 
     Example 15 being a method for controlling a vehicle as illustrated in  FIG. 8 . 
     In Example 16, the subject-matter of Example 15 may optionally include the information about the surroundings further including information about priorities of objects. 
     In Example 17, the subject-matter of Example 16 may optionally include a priority of an object specifying an importance level at which a collision with the object should be avoided. 
     In Example 18, the subject-matter of any one of Examples 15-17 may optionally include determining the information about the surroundings of the vehicle and inputting the information about the surroundings of the vehicle and a specification of a path of the vehicle to the convolutional neural network, determining a target velocity of the vehicle along the path based on an output of the convolutional neural network and controlling the vehicle according to the determined target velocity repeatedly. 
     In Example 19, the subject-matter of any one of Examples 15-18 may optionally include the information about the surroundings including information about the velocity of objects in the surroundings of the vehicle. 
     In Example 20, the subject-matter of any one of Examples 15-19 may optionally include modelling an environment of the vehicle and determining the information about the surroundings of the vehicle at least partially based on the modelled environment. 
     In Example 21, the subject-matter of Example 20 may optionally include modelling the environment by means of a further neural network. 
     In Example 22, the subject-matter of any one of Examples 15-21 may optionally include inputting the information about the surroundings of the vehicle and the specification of the path of the vehicle to the convolutional neural network in the form of one or more two-dimensional images. 
     In Example 23, the subject-matter of any one of Examples 15-22 may optionally include the output of the convolutional neural network specifying a target velocity for each of a plurality of positions along the path of the vehicle. 
     In Example 24, the subject-matter of any one of Examples 15-23 may optionally include the convolutional neural network outputting a specification of a target velocity for each of a plurality of positions along the path of the vehicle in form of a two-dimensional image. 
     In Example 25, the subject-matter of any one of Examples 15-24 may optionally include the convolutional neural network outputting a specification of a risk depending on velocity and travel distance along the path. 
     In Example 26, the subject-matter of any one of Examples 15-25 may optionally include the convolutional neural network outputting the specification of the risk depending on velocity and travel distance along the path in form of a two-dimensional image. 
     In Example 27, the subject-matter of any one of Examples 15-26 may optionally include determining a target vehicle path and controlling the vehicle according to the determined target vehicle path. 
     In Example 28, the subject-matter of any one of Examples 15-27 may optionally include training the convolutional neural network based on training data generated based on an analytical representation of a driving risk function. 
     According to a further example, a vehicle controller (and a corresponding method for controlling a vehicle) are provided wherein the vehicle controller includes (e.g. implements) a convolutional neural network, trained to derive a velocity profile or a risk profile for a path of a vehicle (i.e. a velocity or risk for positions on the vehicle path) from information about surroundings of the vehicle. The vehicle controller is configured to control the vehicle based on be velocity or risk profile. 
     It should be noted that one or more of the features of any of the examples above may be combined with any one of the other examples. 
     While specific aspects have been described, it should be understood by those skilled in the art that various changes in form and detail may be made therein without departing from the spirit and scope of the aspects of this disclosure as defined by the appended claims. The scope is thus indicated by the appended claims and all changes which come within the meaning and range of equivalency of the claims are therefore intended to be embraced.