Patent Publication Number: US-11383705-B2

Title: Enhanced collision avoidance

Description:
BACKGROUND 
     Vehicle collisions can occur at intersections. Collision avoidance systems use sensors to detect a target that can collide with a host vehicle in the intersection. The systems can detect a target object position and speed to determine a probability of a collision with the host vehicle. Collision mitigation may be difficult and expensive to implement, e.g. assessing a target may require data from a plurality of sensors. 
    
    
     
       BRIEF DESCRIPTION OF THE DRAWINGS 
         FIG. 1  is a block diagram of an example system for collision mitigation. 
         FIG. 2  is a plan view of a host vehicle and a target vehicle. 
         FIG. 3  is a block diagram of an example process for collision mitigation. 
     
    
    
     DETAILED DESCRIPTION 
     A computer includes a processor and a memory, the memory storing instructions executable by the processor to determine at least one of a brake threat number of a host vehicle, a brake threat number of a target vehicle, a steering threat number, or an acceleration threat number. The brake threat number of the host vehicle is based on a predicted lateral distance between the host vehicle and the target vehicle. The brake threat number of the target vehicle is based on a velocity of the target vehicle adjusted by an acceleration of the target vehicle and an actuation time of a brake. The steering threat number is a lateral acceleration based on a predicted lateral distance adjusted by an actuation time of a steering component. The acceleration threat number is based on a predicted lateral offset adjusted by a predicted heading angle of the host vehicle. The instructions include instructions to actuate the host vehicle to change at least one of direction or speed based on at least one of the brake threat number of the host vehicle, the brake threat number of the target vehicle, the steering threat number, or the acceleration threat number. 
     The instructions can further include instructions to determine the brake threat number of the host vehicle when the host vehicle is turning across a path of the target vehicle. 
     The instructions can further include instructions to determine the brake threat number of the target vehicle based on a brake-delayed speed that is a measure of a speed of the target vehicle adjusted by the actuation time of the brake. 
     The actuation time of the brake can be a time to actuate a brake pump to charge a hydraulic brake. 
     The instructions can further include instructions to determine the steering threat number based on a lateral acceleration to steer the host vehicle away from a path of the target vehicle in a direction opposite to a turning direction of the host vehicle. 
     The actuation time of the steering component can be a time to actuate a steering motor. 
     The instructions can further include instructions to determine the acceleration threat number based on an actuation time of a propulsion of the host vehicle to accelerate the host vehicle. 
     The instructions can further include instructions to set the acceleration threat number to a predetermined value when the host vehicle is not turning across a path of the target vehicle. 
     The instructions can further include instructions to determine the acceleration threat number when a predicted lateral position of the host vehicle exceeds a threshold. 
     The instructions can further include instructions to determine an overall threat number that is a minimum of the brake threat number of the host vehicle, the brake threat number of the target vehicle, the steering threat number, and the acceleration threat number. 
     The instructions can further include instructions to actuate the host vehicle based on the overall threat number. 
     When the overall threat number is the acceleration threat number, the instructions can further include instructions to actuate a propulsion to accelerate the host vehicle until the host vehicle clears a path of the target vehicle. 
     A method includes determining at least one of a brake threat number of a host vehicle, a brake threat number of a target vehicle, a steering threat number, or an acceleration threat number. The brake threat number of the host vehicle is based on a predicted lateral distance between the host vehicle and the target vehicle. The brake threat number of the target vehicle is based on a velocity of the target vehicle adjusted by an acceleration of the target vehicle and an actuation time of a brake. The steering threat number is a lateral acceleration based on a predicted lateral distance adjusted by an actuation time of a steering component. The acceleration threat number is based on a predicted lateral offset adjusted by a predicted heading angle of the host vehicle. The method further includes actuating the host vehicle to change at least one of direction or speed based on at least one of the brake threat number of the host vehicle, the brake threat number of the target vehicle, the steering threat number, or the acceleration threat number. 
     The method can further include determining the brake threat number of the host vehicle when the host vehicle is turning across a path of the target vehicle. 
     The method can further include determining the brake threat number of the target vehicle based on a brake-delayed speed that is a measure of a speed of the target vehicle adjusted by the actuation time of the brake. 
     The method can further include determining the steering threat number based on a lateral acceleration to steer the host vehicle away from a path of the target vehicle in a direction opposite to a turning direction of the host vehicle. 
     The method can further include determining the acceleration threat number based on an actuation time of a propulsion of the host vehicle to accelerate the host vehicle. 
     The method can further include setting the acceleration threat number to a predetermined value when the host vehicle is not turning across a path of the target vehicle. 
     The method can further include determining the acceleration threat number when a predicted lateral position of the host vehicle exceeds a threshold. 
     The method can further include determining an overall threat number that is a minimum of the brake threat number of the host vehicle, the brake threat number of the target vehicle, the steering threat number, and the acceleration threat number. 
     The method can further include actuating the host vehicle based on the overall threat number. 
     When the overall threat number is the acceleration threat number, the method can further include actuating a propulsion to accelerate the host vehicle until the host vehicle clears a path of the target vehicle. 
     A system includes a host vehicle including a brake, a steering component, means for determining at least one of: a brake threat number of a host vehicle based on a predicted lateral distance between the host vehicle and a target vehicle, a brake threat number of the target vehicle based on a velocity of the target vehicle adjusted by an acceleration of the target vehicle and an actuation time of a brake, a steering threat number that is based on a lateral acceleration being a predicted lateral distance adjusted by an actuation time of a steering component, or an acceleration threat number based on a predicted lateral offset adjusted by a predicted heading angle of the host vehicle, and means for actuating at least one of the brake or the steering component to change at least one of direction or speed based on at least one of the brake threat number of the host vehicle, the brake threat number of the target vehicle, the steering threat number, or the acceleration threat number. 
     The system can further include means for determining an overall threat number that is a minimum of the brake threat number of the host vehicle, the brake threat number of the target vehicle, the steering threat number, and the acceleration threat number. 
     The system can further include means for actuating at least one of the brake or the steering component based on the overall threat number. 
     The system can further include means for actuating a propulsion, vehicle when the overall threat number is the acceleration threat number, to accelerate the host vehicle until the host vehicle clears a path of the target. 
     Further disclosed is a computing device programmed to execute any of the above method steps. Yet further disclosed is a vehicle comprising the computing device. Yet further disclosed is a computer program product, comprising a computer readable medium storing instructions executable by a computer processor, to execute any of the above method steps. 
     A host vehicle can determine a plurality of threat numbers based on actuation of specific components to avoid or mitigate a collision with a target vehicle. For example, the host vehicle can determine threat numbers based on an ability of the host vehicle to brake before crossing a path of the target vehicle. The host vehicle can determine the threat numbers based on respective predicted times for different points on the host vehicle to reach the path of the target vehicle. By determining multiple threat numbers based on different components, a computer in the host vehicle can actuate fewer components to avoid the collision. That is, the computer can actuate only the components required to avoid the collision with the target vehicle based on the threat numbers associated with the components. 
       FIG. 1  illustrates an example system  100  for collision mitigation. A computer  105  in the vehicle  101  is programmed to receive collected data  115  from one or more sensors  110 . For example, vehicle  101  data  115  may include a location of the vehicle  101 , data about an environment around a vehicle, data about an object outside the vehicle such as another vehicle, etc. A vehicle  101  location is typically provided in a conventional form, e.g., geo-coordinates such as latitude and longitude coordinates obtained via a navigation system that uses the Global Positioning System (GPS). Further examples of data  115  can include measurements of vehicle  101  systems and components, e.g., a vehicle  101  velocity, a vehicle  101  trajectory, etc. 
     The computer  105  is generally programmed for communications on a vehicle  101  network, e.g., including a conventional vehicle  101  communications bus. Via the network, bus, and/or other wired or wireless mechanisms (e.g., a wired or wireless local area network in the vehicle  101 ), the computer  105  may transmit messages to various devices in a vehicle  101  and/or receive messages from the various devices, e.g., controllers, actuators, sensors, etc., including sensors  110 . Alternatively or additionally, in cases where the computer  105  actually comprises multiple devices, the vehicle network may be used for communications between devices represented as the computer  105  in this disclosure. In addition, the computer  105  may be programmed for communicating with the network  125 , which, as described below, may include various wired and/or wireless networking technologies, e.g., cellular, Bluetooth®, Bluetooth® Low Energy (BLE), wired and/or wireless packet networks, etc. 
     The data store  106  can be of any type, e.g., hard disk drives, solid state drives, servers, or any volatile or non-volatile media. The data store  106  can store the collected data  115  sent from the sensors  110 . 
     Sensors  110  can include a variety of devices. For example, various controllers in a vehicle  101  may operate as sensors  110  to provide data  115  via the vehicle  101  network or bus, e.g., data  115  relating to vehicle speed, acceleration, position, subsystem and/or component status, etc. Further, other sensors  110  could include cameras, motion detectors, etc., i.e., sensors  110  to provide data  115  for evaluating a position of a component, evaluating a slope of a roadway, etc. The sensors  110  could, without limitation, also include short range radar, long range radar, LIDAR, and/or ultrasonic transducers. 
     Collected data  115  can include a variety of data collected in a vehicle  101 . Examples of collected data  115  are provided above, and moreover, data  115  are generally collected using one or more sensors  110 , and may additionally include data calculated therefrom in the computer  105 , and/or at the server  130 . In general, collected data  115  may include any data that may be gathered by the sensors  110  and/or computed from such data. 
     The vehicle  101  can include a plurality of vehicle components  120 . In this context, each vehicle component  120  includes one or more hardware components adapted to perform a mechanical function or operation—such as moving the vehicle  101 , slowing or stopping the vehicle  101 , steering the vehicle  101 , etc. Non-limiting examples of components  120  include a propulsion component (that includes, e.g., an internal combustion engine and/or an electric motor, etc.), a transmission component, a steering component (e.g., that may include one or more of a steering wheel, a steering rack, etc.), a brake component, a park assist component, an adaptive cruise control component, an adaptive steering component, a movable seat, and the like. 
     When the computer  105  operates the vehicle  101 , the vehicle  101  is an “autonomous” vehicle  101 . For purposes of this disclosure, the term “autonomous vehicle” is used to refer to a vehicle  101  operating in a fully autonomous mode. A fully autonomous mode is defined as one in which each of vehicle  101  propulsion (typically via a powertrain including an electric motor and/or internal combustion engine), braking, and steering are controlled by the computer  105 . A semi-autonomous mode is one in which at least one of vehicle  101  propulsion (typically via a powertrain including an electric motor and/or internal combustion engine), braking, and steering are controlled at least partly by the computer  105  as opposed to a human operator. In a non-autonomous mode, i.e., a manual mode, the vehicle  101  propulsion, braking, and steering are controlled by the human operator. 
     The system  100  can further include a network  125  connected to a server  130  and a data store  135 . The computer  105  can further be programmed to communicate with one or more remote sites such as the server  130 , via the network  125 , such remote site possibly including a data store  135 . The network  125  represents one or more mechanisms by which a vehicle computer  105  may communicate with a remote server  130 . Accordingly, the network  125  can be one or more of various wired or wireless communication mechanisms, including any desired combination of wired (e.g., cable and fiber) and/or wireless (e.g., cellular, wireless, satellite, microwave, and radio frequency) communication mechanisms and any desired network topology (or topologies when multiple communication mechanisms are utilized). Exemplary communication networks include wireless communication networks (e.g., using Bluetooth®, Bluetooth® Low Energy (BLE), IEEE 802.11, vehicle-to-vehicle (V2V) such as Dedicated Short Range Communications (DSRC), etc.), local area networks (LAN) and/or wide area networks (WAN), including the Internet, providing data communication services. 
       FIG. 2  is a plan view of a host vehicle  101  and a target vehicle  200  at an intersection. The host vehicle  101  defines a coordinate system, e.g., a two-dimensional rectangular coordinate system. The coordinate system defines a lateral direction x and a longitudinal direction y extending from an origin at a center point O of the host vehicle  101 . The longitudinal direction y is a vehicle-forward direction, i.e., the direction in which a propulsion  120  moves the vehicle  101  when a steering component  120  is at a neutral position. The lateral direction x is perpendicular to the longitudinal direction y. 
     The computer  105  can determine a brake threat number BTN h  for the host vehicle  101 . The brake threat number BTN h  is a measure of a needed longitudinal deceleration to allow the host vehicle  101  to stop before colliding with the target vehicle  200 . The computer  105  determines the brake threat number BTN h  based on a predicted time to collision TTC long  between the host vehicle  101  and the target vehicle  200  and a velocity of the host vehicle  101 . The time to collision TTC long  is a measure of a time for a longitudinal distance between the host vehicle  101  and the target vehicle  200  to reach zero, i.e., for the longitudinal position of the host vehicle  101  to be substantially the same as the longitudinal position of the target vehicle  200 . 
     The computer  105  can determine an adjusted time to collision TTC adj  that is based on a predicted lateral distance between the host vehicle  101  and the target vehicle  200 . The computer  105  can determine the adjusted time to collision TTC adj  based on a brake threat number modification factor BTN mod  that modifies the brake threat number BTN h  based on the target vehicle speed V tg . Values for the modification factor BTN mod  can be determined based on empirical testing of host vehicles  101  and target vehicles  200  in collision scenarios. The modification factor BTN mod  can be stored in a lookup table or the like in the data store  106 . For example, the modification factor BTN mod  can be values such as those shown in Table 1: 
                                           V   tg     (     m   s     )           BTN mod                     0   0.0       13   0.3       30   0.3                    
Thus, the computer  105  can determine a modified brake threat number BTN h +BTN mod .
 
     The computer  105  can determine a modified time to collision TTC mod  based on the modified brake threat number BTN h +BTN mod : 
                     TTC   mod     =       V   h         (       BTN   h     +     BTN   mod       )     ·     a     h   ,   max                   (   1   )               
where V h  is the host vehicle  101  speed and a h,max  is a maximum deceleration of the host vehicle  101 . The modified time to collision TTC mod  represents modifications to the time to collision from the target vehicle  200  speed.
 
     The computer  105  can predict a lateral offset x off  between the host vehicle  101  and a corner point  205  of the target vehicle  200 . The lateral offset x off  is the predicted lateral distance between the host vehicle  101  and the target vehicle  200  at the modified time to collision TTC mod . That is, because the modified time to collision TTC mod  is based on a longitudinal time to collision TTC, at the longitudinal time to collision TTC, the distance between the host vehicle  101  and the target vehicle  200  is only in the lateral direction x. As an example, the computer  105  can predict the lateral offset x off  based on a lateral and longitudinal distance determining algorithm: 
                       x   off     ⁡     (   T   )       =             a   h     ⁢   ω     3     *     T   3       +             v   h     ⁢   ω     +         x   ¨     ~     tg       2     *     T   2       +           x   .     ~     tg     *   T     +       x   ~     tg               (   2   )               
where T is a specified time, e.g., the modified time to collision TTC mod , a h  is the acceleration of the host vehicle  101 , v h  is the speed of the host vehicle  101 , ω is the yaw rate of the host vehicle  101 , {umlaut over ({tilde over (x)})} tg  is the lateral acceleration of the target vehicle  200 , {dot over ({tilde over (x)})} tg  is the lateral velocity of the target vehicle  200 , and {tilde over (x)} tg  is the lateral position of the target vehicle  200  e.g., as described in U.S. Patent Application Publication No. 2018/0204460, application Ser. No. 15/409,641, which is incorporated herein by reference in its entirety. The computer  105  can use the distance determining algorithm to determine the lateral position of the host vehicle  101  at the modified time to collision TTC mod  to predict the lateral offset x off .
 
     The computer  105  can determine a brake threat number BTN tg  for the target vehicle  200 . The brake threat number BTN tg  is a measure of the ability of the target vehicle  200  to decelerate to avoid the collision with the host vehicle  101 . The brake threat number BTN tg  can be based on a velocity of the target vehicle  200  v tg , an acceleration of the target vehicle  200  a tg , and an actuation time of a brake  120  T BrakeDelay : 
                     BTN   tg     =       (         v   tg     -       a   tg     *     T   BrakeDelay           TTC   -     T   BrakeDelay         )       (       a     tg   ,   max       -     a   tg       )               (   3   )               
where a tg,max  is the maximum deceleration of the target vehicle  200 . That is, the brake threat number BTN tg  is based on a brake-delayed speed v tg −a tg *T BrakeDelay  that is a measure of a speed of the target vehicle adjusted by the actuation time T BrakeDelay  of the brake  120 . The computer  105  can determine the maximum deceleration of the target vehicle  200  a tg,max  based on manufacturer specifications and/or empirical brake testing of vehicles  101 ,  200 . The maximum deceleration a tg,max  is stored in the data store  106  and/or the server  130 . The time to collision TTC can be determined as described above. Alternatively, the time to collision TTC can be determined as a range divided by a range rate, i.e.
 
               TTC   =     R     R   .         ,         
where R is a range between the host vehicle  101  and the target vehicle  200  (i.e., a minimum straight-line distance between the host vehicle  101  and the target vehicle  200 ), {dot over (R)} is the time-rate of change of the range.
 
     The actuation time of the brake  120  T BrakeDelay  is an elapsed time between when the target vehicle  200  determines to decelerate and then begins to decelerate, i.e., the actuation time of a brake of the target vehicle  200 . The actuation time of the brake  120  can be substantially similar between vehicles  101 ,  200 , so the computer  105  can use the actuation time of the brake  120  of the host vehicle  101  to determine the deceleration of the target vehicle  200 . Thus, the computer  105  can determine the actuation time of the brake  120  as the elapsed time between when the computer  105  instructs the brake  120  to decelerate the host vehicle  101  and the host vehicle  101  begins to decelerate. For example, the actuation time T BrakeDelay  can be a time for a brake pump to provide brake fluid to one or more brake pads of a hydraulic brake as specified by a manufacturer or measured from empirical testing of brakes  120  of vehicles  101 ,  200 . The actuation time T BrakeDelay  is stored in the data store  106  and/or the server  130 . 
     The computer  105  can determine a steering threat number STN. The steering threat number STN is a measure of a lateral acceleration to steer the host vehicle  101  away from a path of the target vehicle  200  in a direction opposite the turning direction of the host vehicle  101 . The computer  105  can determine the steering threat number STN based on a sign of the yaw rate ω of the host vehicle  101  and a sign of the predicted lateral offset x off : 
                   STN   =     max   ⁡     (       2   *         x   margin     +       F   sign     ·          x   off                  (       TTC     l   ⁢   o   ⁢   n   ⁢   g       -     T   SteerDelay       )     2       *     1     a     h   ,   lat   ,   max           ,   1     )               (   4   )               
where x margin  is a lateral safety margin based on empirical testing of host vehicle  101  in intersections with target vehicles  200 , TTC long  is a longitudinal time to collision, as described above, T SteerDelay  is an actuation time of a steering component  120 , a h,lat,max  is a maximum lateral acceleration of the host vehicle  101 , and F sign  is a factor that is either 1 or −1 depending on the signs of the yaw rate ω and the predicted lateral offset x off :
 
                     F   sign     =     {                 1   ⁢           ⁢   ω     &gt;   0     ,     ⁢                     x   off     ≥   0                     1   ⁢           ⁢   ω     &lt;   0     ,     ⁢                     x   off     ≤   0                       -   1     ⁢           ⁢   ω     &gt;   0     ,     ⁢                     x   off     &lt;   0                       -   1     ⁢           ⁢   ω     &lt;   0     ,     ⁢                     x   off     &gt;   0                     (   5   )               
That is, the steering threat number STN is a lateral acceleration of the host vehicle  101  based on a predicted lateral distance x off  adjusted by an actuation time T SteerDelay  of a steering component  120 . By including for the actuation time T SteerDelay  of the steering component  120 , the computer  105  more accurately predicts the time necessary to perform steering.
 
     The actuation time T SteerDelay  of the steering component  120  is an elapsed time between when the computer  105  instructs the steering component  120  to steer the host vehicle  101  and the host vehicle  101  begins to turn as specified by a manufacturer and/or measured in empirical testing of steering components  120  of vehicles  101 ,  200 . For example, the actuation time T SteerDelay  can be a time for a steering motor to power up and move a steering rack to steer the host vehicle  101 . The actuation time T SteerDelay  is stored in the data store  106  and/or the server  130 . 
     The computer  105  can determine an acceleration threat number ATN, e.g., as further described below. The acceleration threat number is a measure of a host vehicle  101  ability to accelerate to avoid a collision with a target vehicle  200 . The acceleration threat number ATN is based on a predicted lateral offset x off , as described above, adjusted by a predicted heading angle ϕ of the host vehicle  101 . The computer  105  can use the acceleration threat number ATN when the host vehicle  101  is in a turn across a path of the target vehicle  200 . 
     The computer  105  can determine an acceleration distance d that is a minimum distance in order for the host vehicle  101  to pass the target vehicle  200 , as shown in  FIG. 2 . The acceleration distance d is based on a dimensions of the host vehicle  101 , i.e., the length and width as defined above, and a predicted heading angle ϕ of the host vehicle  101 . The heading angle ϕ is an angle defined between a trajectory of the host vehicle  101  and a longitudinal axis extending from a center pint of a front bumper of the host vehicle  101 . The computer  105  can predict the heading angle ϕ at the longitudinal time to collision TTC long  based on the yaw rate ω of the host vehicle  101 :
 
ϕ( TTC   long )=ω· TTC   long   (6)
 
     The computer  105  can determine the acceleration distance d according the Equation below: 
                   d   =     max   (         ATN   marg     +     H   l     -     (       max   ⁡     (              x   off          -       H   w     2       ,   0     )         max   ⁡     (       min   ⁡     (       tan   ⁡     (   φ   )       ,     tan   ⁡     (     φ   max     )         )       ,     tan   ⁡     (     φ   min     )         )         )       ,   0     )             (   7   )               
where ATN marg  is a margin that increases the acceleration distance d based on empirical testing of host vehicles  101  and target vehicles  200  in intersections, H l  is a length of the host vehicle  101  in the longitudinal direction, H w  is a width of the host vehicle  101  is a lateral direction, ϕ max  is a maximum heading angle ϕ, and ϕ min  is a minimum heading angle ϕ. The margin ATN marg  is a value that increases the acceleration distance d to ensure that the host vehicle  101  passes the path of the target vehicle  200 . The margin ATN marg  can be, e.g., 2 meters. The maximum heading angle ϕ max  is a maximum heading angle ϕ beyond which the host vehicle  101  may be turning too quickly to accelerate away from the target vehicle  200 , and the maximum heading angle ϕ max  can be determined with empirical testing of host vehicles  101  and target vehicles  200  in intersections. The minimum heading angle ϕ min  is a minimum heading angle ϕ to perform the turn, i.e., the minimum heading angle ϕ that can cause the host vehicle  101  to turn from a current roadway lane to a transverse roadway lane.
 
     The computer  105  can determine the acceleration threat number ATN based on the acceleration distance d: 
                   ATN   =     d       a     h   ,   max       *       (       TTC   Long     -     (       T   AccelDelay     -       F   ADR     *     T   ADR         )       )     2                 (   8   )               
where a h,max  is a maximum acceleration of the host vehicle  101 , T AccelDelay  is an elapsed time between when the computer  105  instructs a propulsion  120  to accelerate the host vehicle  101  and the host vehicle  101  begins to accelerate as specified by a manufacturer or measured from empirical testing of propulsions  120  of vehicles  101 ,  200 , T ADR  is an acceleration reduction factor that reduces the acceleration time delay T AccelDelay  when the host vehicle  101  is decelerating, and F ADR  is a Boolean value that is 0 when the current acceleration a h  is below or equal to a predetermined threshold and 1 when the current acceleration a h  is above the predetermined threshold. For example, the acceleration delay time T AccelDelay  can include a time for a fuel system to provide fuel to an internal combustion engine and for the engine to turn an axle of the host vehicle  101 . The threshold for the Boolean value F ADR  can be an acceleration beyond which the elapsed time between instruction of the propulsion  120  and acceleration of the host vehicle  101  is below the delay time T AccelDelay , e.g., 1 meter/second. That is, when the host vehicle  101  is already accelerating, the elapsed time between instructing the propulsion  120  to increase the acceleration and when the host vehicle  101  accelerates may be less than when the host vehicle  101  begins acceleration from a constant speed, and the acceleration reduction factor T ADR  is an empirically determined value based on host vehicles  101  turning across paths of target vehicles  200  that accounts for the time delay change during acceleration of the host vehicle  101 . The acceleration time delay T AccelDelay  is stored in the data store  106  and/or the server  130 .
 
     The computer  105  can determine the acceleration threat number ATN when the host vehicle  101  is turning across a path of a target vehicle  200 . That is, the computer  105  can predict a path of the target vehicle  200  and, if a predicted path of the host vehicle  101  turns across the path of the target vehicle  200 , the computer  105  can determine the acceleration threat number ATN. The computer  105  can predict the path of the target vehicle  200  by inputting target vehicle  200  speed, position, and acceleration from data  115  from one or more sensors to a conventional path-planning algorithm. The computer  105  can determine that the host vehicle  101  is turning across the path of the target vehicle  200  when a lateral position x of the host vehicle  101  exceeds a threshold, e.g., a width of a roadway lane. 
     If the computer  105  determines that the host vehicle  101  is not turning across the path of the target vehicle  200 , the computer  105  can determine to set the acceleration threat number ATN to a predetermined value (e.g., 1) instead of calculating the acceleration threat number ATN according to the Equations above. That is, the computer  105  can reduce the computations performed by reducing the number of threat numbers to determine based on the predicted paths of the host vehicle  101  and the target vehicle  200 , only determining the acceleration threat number ATN when the host vehicle  101  is turning across the path of the target vehicle  200 . 
     The computer  105  can determine an overall threat number TN. The overall threat number TN is the minimum of the brake threat number of the host vehicle BTN h , the brake threat number of the target vehicle BTN tg , the steering threat number STN, and the acceleration threat number ATN. That is, lower threat numbers indicate less actuation of components  120  to avoid the target vehicle  200 . For example, if the brake threat number BTN h  is less than the steering threat number STN, the computer  105  can actuate the brake  120  to provide braking force that is less than the steering force required by the steering component  120  to steer the host vehicle  101  away from the target vehicle  200 . That is, the computer  105  advantageously performs fewer actuations of components  120  to provide the same collision avoidance according to the minimum threat number TN. 
       FIG. 3  is a block diagram of an example process  300  for collision mitigation. The process  300  begins in a block  305 , in which a computer  105  of a host vehicle  101  actuates one or more sensors  110  to collect data  115  about a target vehicle  200 . The computer  105  can actuate, e.g., a camera  110  to collect image data  115 , a radar  110  to collect radar data  115 , etc. 
     Next, in a block  310 , the computer  105  determines whether the host vehicle  101  is passing in front of a path of the target vehicle  200 . As described above, the computer  105  can predict a path of the host vehicle  101  and a path of the target vehicle  200 . When the path of the host vehicle  101  turns across a path of the target vehicle  200 , the computer  105  can perform threat assessments with specific threat numbers. If the host vehicle  101  is passing in front of the path of the target vehicle  200 , the process  300  continues in a block  325 . Otherwise, the process  300  continues in a block  315 . 
     In the block  315 , the computer  105  determines brake threat numbers BTN h , BTN tg  and a steering threat number STN. The brake threat numbers BTN h , BTN tg , as described above, are a measure of an ability for the host vehicle  101  and the target vehicle  200  to brake prior to a potential collision. The steering threat number STN, as described above, is a measure of an acceleration for the host vehicle  101  to turn away from the target vehicle  200  is a direction opposite to a turn direction of the host vehicle  101 . The computer  105  determines the threat numbers BTN h , BTN tg , STN based on the collected data  115  about the target vehicle  200 . 
     Next, in a block  320 , the computer  105  sets an acceleration threat number ATN to a predetermined value. Because the host vehicle  101  is not turning across the path of the target vehicle  200 , the acceleration threat number ATN may not be needed, and the computer  105  sets the acceleration threat number ATN to a value to prevent further collision mitigation and avoidance based on the acceleration threat number ATN. For example, the computer  105  can set the acceleration threat number ATN to 1. 
     In the block  325 , the computer  105  determines brake threat numbers BTN h , BTN tg , the steering threat number STN, and the acceleration threat number ATN. As described above, the acceleration threat number ATN is a measure of an acceleration required to move the host vehicle  101  past the path of the target vehicle  200 . Because the host vehicle  101  is turning across the path of the target vehicle  200 , the computer  105  can determine all four threat numbers BTN h , BTN tg , STN, ATN. 
     Next, in a block  330 , the computer  105  determines an overall threat number TN. As described above, the overall threat number TN is the minimum of the threat numbers BTN h , BTN tg , STN, ATN. By taking the minimum of the threat numbers as the overall threat number TN, the computer  105  can perform collision mitigation and avoidance with fewer components  120  to avoid the target vehicle  200 , as described above. 
     Next, in a block  335 , the computer  105  actuates one or more components  120  according to the overall threat number TN. For example, if the overall threat number TN is the steering threat number STN, the computer  105  can actuate a steering motor  120  to provide the lateral acceleration to steer the host vehicle  101  away from the target vehicle  200 . In another example, if the overall threat number TN is the acceleration threat number ATN, the computer  105  can actuate a propulsion to accelerate the host vehicle  101  past the target vehicle  200 . In another example, if the overall threat number is the brake threat number BTN h  of the host vehicle  101 , the computer  105  can actuate a brake to slow or stop the host vehicle  101  until the target vehicle  200  passes the host vehicle  101 . 
     Next, in a block  340 , the computer  105  determines whether to continue the process  300 . For example, the computer  105  can determine to continue the process  300  upon approaching another intersection. If the computer  105  determines to continue, the process  300  returns to the block  305 . Otherwise, the process  300  ends. 
     As used herein, the adverb “substantially” modifying an adjective means that a shape, structure, measurement, value, calculation, etc. may deviate from an exact described geometry, distance, measurement, value, calculation, etc., because of imperfections in materials, machining, manufacturing, data collector measurements, computations, processing time, communications time, etc. 
     Computing devices discussed herein, including the computer  105  and server  130 , include processors and memories, the memories generally each including instructions executable by one or more computing devices such as those identified above, and for carrying out blocks or steps of processes described above. Computer executable instructions may be compiled or interpreted from computer programs created using a variety of programming languages and/or technologies, including, without limitation, and either alone or in combination, Java™, C, C++, Visual Basic, Java Script, Python, Perl, HTML, etc. In general, a processor (e.g., a microprocessor) receives instructions, e.g., from a memory, a computer readable medium, etc., and executes these instructions, thereby performing one or more processes, including one or more of the processes described herein. Such instructions and other data may be stored and transmitted using a variety of computer readable media. A file in the computer  105  is generally a collection of data stored on a computer readable medium, such as a storage medium, a random access memory, etc. 
     A computer readable medium includes any medium that participates in providing data (e.g., instructions), which may be read by a computer. Such a medium may take many forms, including, but not limited to, non volatile media, volatile media, etc. Non volatile media include, for example, optical or magnetic disks and other persistent memory. Volatile media include dynamic random access memory (DRAM), which typically constitutes a main memory. Common forms of computer readable media include, for example, a floppy disk, a flexible disk, hard disk, magnetic tape, any other magnetic medium, a CD ROM, DVD, any other optical medium, punch cards, paper tape, any other physical medium with patterns of holes, a RAM, a PROM, an EPROM, a FLASH EEPROM, any other memory chip or cartridge, or any other medium from which a computer can read. 
     With regard to the media, processes, systems, methods, etc. described herein, it should be understood that, although the steps of such processes, etc. have been described as occurring according to a certain ordered sequence, such processes could be practiced with the described steps performed in an order other than the order described herein. It further should be understood that certain steps could be performed simultaneously, that other steps could be added, or that certain steps described herein could be omitted. For example, in the process  300 , one or more of the steps could be omitted, or the steps could be executed in a different order than shown in  FIG. 3 . In other words, the descriptions of systems and/or processes herein are provided for the purpose of illustrating certain embodiments, and should in no way be construed so as to limit the disclosed subject matter. 
     Accordingly, it is to be understood that the present disclosure, including the above description and the accompanying figures and below claims, is intended to be illustrative and not restrictive. Many embodiments and applications other than the examples provided would be apparent to those of skill in the art upon reading the above description. The scope of the invention should be determined, not with reference to the above description, but should instead be determined with reference to claims appended hereto and/or included in a non provisional patent application based hereon, along with the full scope of equivalents to which such claims are entitled. It is anticipated and intended that future developments will occur in the arts discussed herein, and that the disclosed systems and methods will be incorporated into such future embodiments. In sum, it should be understood that the disclosed subject matter is capable of modification and variation. 
     The article “a” modifying a noun should be understood as meaning one or more unless stated otherwise, or context requires otherwise. The phrase “based on” encompasses being partly or entirely based on.