VEHICLE SAFETY SYSTEM WITH INTEGRATED ACTIVE-PASSIVE FRONT IMPACT CONTROL ALGORITHM

Abstract

A vehicle safety system (10) that helps protect a vehicle occupant in the event of a frontal collision, comprising: a control device; one or more collision sensors (50, 60, 62) for detecting a frontal collision; and an active sensor (110, 120, 130) for detecting objects in the path of the vehicle (12); wherein the control device is designed to generate metrics for to implement collision discrimination that detects the occurrence of a frontal collision in response to signals received from the collision sensors, characterized in that the collision discrimination metrics implement thresholds for determining whether the signals received from the collision sensors indicate the occurrence of a frontal collision; and in that the control device is configured to implement an algorithm that uses the information obtained from the active sensor (110, 120, 130) to detect an object in the path of travel of the vehicle (12) and to select the thresholds implemented in the collision discrimination metrics in response to detecting the object.

Description

[0001] Modern vehicles incorporate various systems that contribute to occupant safety. These vehicle safety systems can include passive safety systems and / or active safety systems. Generally speaking, passive safety systems are reactive systems that provide occupant protection in response to detecting the occurrence of an event for which occupant protection is desired, such as a vehicle collision. Active safety systems, on the other hand, seek to anticipate the occurrence of events for which occupant protection is desired and execute active avoidance measures.

[0002] Passive safety systems include one or more passive restraint devices, such as airbags and seatbelt retractors, that are operable to help protect an occupant of a vehicle. These vehicle safety systems utilize an airbag control unit that is operatively connected to the airbags and to a variety of crash sensors, such as accelerometers and pressure sensors. In response to determining a crash scenario based on information provided by the crash sensors, the airbag control unit is operable to deploy the airbags by activating an inflator that directs inflation fluid into the airbags. When inflated, the driver and passenger airbags help protect the occupant from striking parts of the vehicle, such as the instrument panel and / or a steering wheel.

[0003] Active safety systems use sensor devices such as cameras, radar, lidar, and ultrasonic transducers to determine the conditions in the vehicle's environment. Based on the detected conditions, vehicle warning systems can provide visual, audible, and tactile alerts to the driver. This can include, for example, blind spot detection, lane departure detection, front and rear object detection, cross-traffic detection, and pedestrian detection. Active safety systems can also use the detected conditions to actively activate vehicle controls, such as active cruise control, active braking, active steering in response to lane departure detection, and so on. The sensing devices used in active safety systems each have specific advantages.

[0004] Cameras are very effective at object detection. When positioned to "view" the surroundings from multiple angles, cameras provide the vehicle with information that can be used by vehicle safety systems' artificial intelligence algorithms to detect external objects, such as other vehicles, pedestrians, or roadside objects like trees or trash cans. Cameras can precisely measure angles, allowing the vehicle safety system to predict early whether an approaching object will enter the vehicle's path. By utilizing both a long-focal-length and a short-focal-length zoom range with varying widths and narrow fields of view, cameras become critical tools for safety functions such as collision avoidance, adaptive cruise control, automated braking systems, and lane-keeping assistance.

[0005] Radar sensors use an echo system to detect objects, which is advantageous in poor visibility conditions that can impair the effectiveness of the camera. Radar sensors emit electromagnetic waves and receive the "echo" reflected back from surrounding objects. Radar sensors are particularly effective at determining the distance and speed of objects—such as vehicles and pedestrians—relative to the vehicle. Because they operate independently of weather, light, and visibility conditions, radar sensors are ideal for following distance, issuing collision warnings, blind-spot detection, emergency braking, etc.

[0006] Lidar sensors also use the echo principle, using laser pulses instead of radio waves. Lidar sensors measure distances and relative speeds with an accuracy comparable to radar. In addition, Lidar sensors can also detect object types and angles between objects with much greater accuracy. Lidar sensors can therefore be used to accurately detect more complex traffic situations, even in the dark. Unlike cameras and radar sensors, the viewing angle is not critical, as Lidar sensors can record the vehicle's 360-degree surroundings. High-resolution 3D solid-state Lidar sensors can even render pedestrians and smaller objects in three dimensions.

[0007] The invention relates to a vehicle safety system that includes both active and passive components. In this specification, "active safety" is used to refer to technology that contributes to the prevention of a collision, i.e., "crash avoidance," and "passive safety" is used to refer to vehicle components such as airbags, seatbelts, and the physical structure of the vehicle (e.g., crumple zones) that contribute to protecting occupants in response to detecting the occurrence of a collision.

[0008] Passive safety systems include one or more sensors, such as accelerometers and / or pressure sensors, configured to detect the occurrence of a collision event. A controller is configured to receive signals from the sensors, determine or discriminate the occurrence of a collision based on the signals, and deploy one or more actuatable restraint devices, such as airbags and / or seatbelt pretensioners / retractors, in response to the detected collision.

[0009] Active safety systems, such as collision avoidance systems, are designed to prevent or mitigate the severity of a vehicle collision by using radar (all-weather), laser (LIDAR), cameras (using image recognition), or a combination of these to detect an impending collision. In response to detecting an impending collision, collision avoidance systems can provide warnings (visual, audible, tactile) to an operator and also initiate active safety measures, such as automatic emergency braking and / or automatic emergency steering, to help avoid or mitigate the collision.

[0010] For example, a collision avoidance system may perform automatic emergency braking to detect a potential forward collision and activate the vehicle's braking system to decelerate the vehicle with the purpose of avoiding or mitigating a collision. Once an impending collision is detected, the collision avoidance system issues a warning to the driver. If the collision is imminent, the collision avoidance system intervenes automatically and autonomously, without any driver input, by performing emergency braking.

[0011] The active safety system can be a standalone system or it can be a subsystem that utilizes components of another system, such as a driver assistance system (DAS), which uses camera, radar, and LIDAR data to provide driver assistance functions such as active cruise control, lane keeping, blind spot monitoring, parking assistance, etc. These components can even be used to provide automated driving functionality.

[0012] According to the invention, information obtained from an active safety system is used to improve the discrimination of a frontal collision performed by the passive safety system and thus the responsiveness of the vehicle safety system.

[0013] According to one aspect, a vehicle safety system that helps protect a vehicle occupant in the event of a frontal collision includes a controller, one or more collision sensors for sensing a frontal collision, and an active sensor for detecting objects in the path of the vehicle. The controller is configured to implement collision discrimination metrics that detect the occurrence of a frontal collision in response to signals received from the collision sensors. The collision discrimination metrics implement thresholds for determining whether the signals received from the collision sensors indicate the occurrence of a frontal collision.The controller is configured to implement an algorithm that uses the information obtained from the active sensor to detect an object in the path of travel of the vehicle and to select the thresholds implemented in the collision discrimination metrics in response to detecting the object.

[0014] According to another aspect, alone or in combination with any other aspect, the algorithm implemented by the controller may be further configured to select misuse boxes associated with the selected thresholds implemented by the crash discrimination metrics based on the information obtained from the active sensor.

[0015] According to a further aspect, alone or in combination with any other aspect, the algorithm implemented by the controller may be configured to determine an object type for the object, determine an estimated severity of a collision with the object, and further select the thresholds implemented in the collision discrimination metrics in response to at least one of the object type and the estimated severity.

[0016] According to another aspect, alone or in combination with any other aspect, the object type may be an automobile, a truck, an obstacle, or a pole.

[0017] According to a further aspect, alone or in combination with any other aspect, the algorithm implemented by the controller may be configured to determine the estimated severity by implementing a metric that determines the estimated severity based on a relative speed between the object and the vehicle.

[0018] According to another aspect—alone or in combination with any other aspect—the vehicle safety system may also include at least one actuatable safety device comprising an airbag with a two-stage inflator and a seatbelt pretensioner. The algorithm implemented by the controller may be configured to determine that, in response to the estimated severity, one of the following actions should be performed: actuation of neither the seatbelt pretensioner nor the inflator, actuation of only the seatbelt pretensioner, actuation of both the seatbelt pretensioner and the first stage of the inflator; actuation of the seatbelt pretensioner, the first stage of the inflator, and the second stage of the inflator.

[0019] According to another aspect, alone or in combination with any other aspect, the algorithm implemented by the controller may be further configured to select misuse boxes associated with the selected thresholds implemented by the crash discrimination metrics in response to at least one of the object type and the estimated severity.

[0020] According to another aspect, alone or in combination with any other aspect, the algorithm implemented by the controller may be further configured to determine the estimated severity by evaluating an estimated severity discrimination metric that compares a relative velocity of the object relative to the vehicle and the displacement.

[0021] According to another aspect—alone or in combination with any other aspect—the crash sensors may include front crush zone sensors (CZS). The crash discrimination metrics may include a CZS switching discrimination metric for evaluating CZS acceleration values ​​to determine whether a CZS switching threshold is exceeded. The crash discrimination metrics may include individual metrics associated with each object type determined by the algorithm. Each individual metric may include a normal threshold and a normal misapplication box, as well as a CZS switching threshold and a CZS switching misapplication box. The CZS switching threshold and the CZS switching misapplication box may be implemented by the crash discrimination metrics when the CZS switching discrimination metric determines that the CZS switching threshold is exceeded.The normal threshold and the misapplication box can be implemented if the CZS switching threshold is not exceeded. The collision discrimination metrics can further include a normal preset threshold and a normal preset misapplication box, as well as a preset CZS switching threshold. The preset CZS switching threshold and the preset misapplication box can be implemented by the collision discrimination metrics if the CZS switching discrimination metric determines that the CZS switching threshold is exceeded and the algorithm detects the object in the vehicle's path. The normal preset threshold and the normal preset misapplication box can be implemented if the CZS switching threshold is not exceeded and the algorithm detects the object in the vehicle's path.

[0022] According to a further aspect - alone or in combination with any other aspect - the algorithm implemented by the controller may be configured to detect objects in the path of travel of the vehicle by identifying the object, determining whether a lateral position of the object relative to the vehicle is within a predetermined threshold, evaluating a time-to-collision (TTC) discrimination metric to determine whether a relative speed between the object and the vehicle exceeds a predetermined threshold determining that a collision is imminent, evaluating a longitudinal distance from collision discrimination metric to determine whether the longitudinal position of the object relative to the vehicle exceeds a predetermined threshold determining that a collision is imminent.

[0023] According to a further aspect - alone or in combination with any other aspect - the algorithm implemented by the controller may be configured to detect objects in the path of travel of the vehicle by identifying the state of the object, determining whether a calculated collision probability is greater than a predetermined collision probability, and evaluating a time-to-collision (TTC) discrimination metric to determine whether the relative speed between the object and the vehicle exceeds a predetermined threshold determining that a collision is imminent.

[0024] According to a further aspect—alone or in combination with any other aspect—the collision sensors may be components of a passive safety system that further includes at least one actuatable safety device. The passive safety system may be configured to respond to the occurrence of a vehicle collision by actuating the safety device. The active sensor may be a component of an active safety system configured to anticipate the occurrence of the vehicle collision.

[0025] According to a further aspect - alone or in combination with any other aspect - the collision sensors may be at least one of crumple zone sensors and sensors of an airbag control unit (ACU) in the form of accelerometers for measuring the vehicle acceleration along a vehicle longitudinal axis.

[0026] According to another aspect, alone or in combination with any other aspect, the collision discrimination metrics may have a value determined by comparing acceleration versus displacement or velocity versus displacement as measured by the collision sensors, and the collision discrimination metrics may determine the occurrence of a frontal collision in response to the value exceeding the threshold.

[0027] According to a further aspect - alone or in combination with any other aspect - the active sensor may be a camera, wherein the information obtained from the active sensor may be the object type, a lateral position of the object, a time to collision (TTC) with the object, a relative speed of the object and the vehicle, and / or a longitudinal position of the object relative to the vehicle.

[0028] According to a further aspect - alone or in combination with any other aspect - the algorithm for detecting the object in the path of travel of the vehicle may be configured to detect the object by determining whether the object type is a recognized object type; by determining whether the lateral position of the object is within a predetermined threshold range; by determining whether the TTC with the object is within a threshold indicating an impending collision; by evaluating a TTC collision discrimination metric to determine whether the TTC exceeds a predetermined threshold, wherein the threshold is determined with respect to the relative speed of the object and the vehicle;and by determining whether the longitudinal position of the object relative to the vehicle is within a threshold indicating an impending collision, by evaluating a discrimination metric of the longitudinal distance from a collision to determine whether the longitudinal distance between the object and the vehicle exceeds a predetermined threshold, wherein the threshold is determined with respect to the relative speed of the object and the vehicle;

[0029] According to another aspect—alone or in combination with any other aspect—the active sensor may be a radar sensor. The information obtained from the active sensor may include an object state, a collision probability, a time to collision (TTC) with the object, and a relative speed of the object and the vehicle.

[0030] According to a further aspect - alone or in combination with any other aspect - the algorithm for detecting the object in the path of travel of the vehicle is configured to detect the object by determining whether the object state is a detected object state; by determining whether the collision probability is greater than a predetermined threshold probability; and by determining whether the TTC with the object is within a threshold indicating an impending collision by evaluating a TTC collision discrimination metric to determine whether the TTC exceeds a predetermined threshold, wherein the threshold is determined with respect to the relative speed of the object and the vehicle.

[0031] According to a further aspect - alone or in combination with any other aspect - the detected object state can be a forward state, a backward state, a sideways state, a stationary state or a moving state.

[0032] According to another aspect, alone or in combination with any other aspect, the active sensor may be at least one of a camera, a radar sensor, and a laser radar (LIDAR) sensor.

[0033] According to a further aspect - alone or in combination with any other aspect - the control device may be an airbag controller unit (ACU).

[0034] The Fig. 1 is a schematic diagram of a vehicle having a vehicle safety system according to an example configuration.

[0035] The Fig. Figures 2 to 5 are schematic representations of the active safety control algorithms implemented in the vehicle safety system.

[0036] The Fig. Figure 6 is a schematic representation of the collision signal processing implemented in the vehicle safety system.

[0037] The Fig. 7A and Fig. 7B are schematic diagrams showing known crash discrimination metrics implemented in vehicle safety systems.

[0038] The Fig. 8A and Fig. 8B are schematic diagrams showing crash discrimination metrics having active switching features implemented in the vehicle safety system.

[0039] In this description, references are sometimes made to the left and right sides of a vehicle. These references should be understood as referring to the vehicle's forward direction of travel. Therefore, when reference is made to the "left" side of a vehicle, this refers to the driver side ("DS") of the vehicle. When reference is made to the "right" side of the vehicle, this refers to the passenger side ("PS") of the vehicle.

[0040] In addition, certain descriptions in this specification are made with reference to vehicle axes, specifically the X-axis, Y-axis, and Z-axis of the vehicle. The X-axis is a central, longitudinal axis of the vehicle. The Y-axis is a lateral axis of the vehicle that is perpendicular to the X-axis. The Z-axis is a vertical axis of the vehicle that is perpendicular to both the X-axis and the Y-axis. The X-axis, Y-axis, and Z-axis intersect at or near a center of gravity (“COG”) of the vehicle.

[0041] Referring to the Fig. 1, for example, a vehicle 12 includes a vehicle safety system 10 that includes a passive safety system 20 and an active safety system 100. The passive safety system 20 includes actuatable vehicle occupant protection devices, schematically illustrated at 14. The protection devices 14 may include any actuatable vehicle occupant protection devices, such as front airbags, side airbags, side curtain airbags, knee airbags, curtain airbags, actuatable seatbelt pretensioners and / or retractors. The passive safety system 20 also includes an airbag electronic control unit 50 (referred to herein as an airbag control unit or "ACU") operatively connected to the protection devices 14. The ACU 50 is operable to control the actuation of the protection devices 14 in response to vehicle conditions sensed via one or more sensors with which the ACU is operatively connected.

[0042] The passive safety system 20 includes a plurality of sensors, such as accelerometers and / or pressure sensors, to measure certain conditions of the vehicle 12, which are used to determine whether the vehicle occupant protection devices 14 should be activated. These sensors may be mounted throughout the vehicle 12 at various locations selected to enable sensing of the specific vehicle condition for which the sensor is intended. In this specification, the vehicle safety system 10 is described as including a plurality of crash sensors of different types and at different locations within the vehicle 12. The crash sensors described herein are not necessarily a complete list of the sensors included in the vehicle safety system 10.The sensors described herein are merely those used within the scope of the invention to detect the occurrence of a frontal impact. Those skilled in the art will therefore recognize that vehicle safety system 100 may include one or more other collision sensors of any type, in any number, and at any location within vehicle 12.

[0043] The passive safety system 80 is implemented in the ACU 50 and is used to detect the occurrence of a frontal vehicle impact. For this purpose, the vehicle safety system 10 includes a left crumple zone sensor 60 and a right crumple zone sensor 62. The left crumple zone sensor 60 and the right crumple zone sensor 62 are accelerometers configured to sense vehicle accelerations and transmit signals indicative of these accelerations to the ACU 50. The ACU 50 is configured to determine whether the magnitude of the sensed accelerations meets or exceeds a threshold sufficient to indicate that a collision event has occurred and to actuate the safety devices in response to this determination.

[0044] In the Fig. 1, the crumple zone sensors 60, 62 are single-axis accelerometers designed to measure accelerations parallel to the longitudinal axis X FZ to detect directions generally indicated in the schematic representation of the sensors by the arrows LT_CZS and RT_CZS, respectively. The left and right crumple zone sensors 60, 62 are located at or near the left (DS) front corner and the right (PS) front corner of the vehicle 12. The left and right crumple zone sensors 60, 62 may be mounted at these front corner locations, for example, behind a front bumper 16 of the vehicle. The ACU 50 includes an integrated 2-axis accelerometer 52 for detecting vehicle accelerations along the X-axis and the Y-axis. These accelerations are displayed at CCU_X and CCU_Y, respectively.

[0045] The vehicle safety system 10 is implemented and configured to interact with other vehicle systems. The ACU 50 may, for example, be operatively connected to a vehicle body control module (BCM) 30 via a vehicle controller area network (CAN) bus. The BCM 30 may communicate with other vehicle systems via the CAN bus, such as chassis control, stability control, traction / skid control, anti-lock braking system (ABS), tire pressure monitoring (TPMS), navigation systems, gauges (speed, throttle position, brake pedal position, etc.), information and entertainment systems (infotainment systems), and other systems. The ACU 50 may communicate with any of these external systems via the CAN bus interface to provide and / or receive data.

[0046] With further reference to the Fig. 1, the active safety system 100 may have a known configuration, including one or more active safety system components configured to provide active safety functionality in a known manner. The active safety system 100 may utilize components of a driver assist system (DAS), which, as the name implies, provides assistance to the vehicle driver while driving. These components may assist in providing DAS functionality such as active cruise control, lane keeping, blind spot monitoring, parking assistance, etc. These components may even be those employed to provide automated driving functionality and may therefore provide large amounts of information about the vehicle's surroundings using artificial intelligence (AI) and other machine learning techniques.For collision avoidance functionality, the active safety system can provide collision warnings (audible, visual, tactile), automatic emergency braking, and automatic emergency steering.

[0047] The active safety system 100 includes components such as camera sensors, radar sensors, and laser radar (LIDAR) sensors. A camera sensor 110 is mounted forward-facing at the top of the windshield 18, for example, behind or in the area of ​​a rearview mirror. The radar sensor 120(s) may be mounted at the front, in the area of ​​the bumper 16, for example, in the grille. A laser radar (LIDAR) sensor 130 may be mounted on or near the vehicle roof 22.

[0048] The camera sensors 110 provide a wide field of view and can identify various objects / obstacles with high accuracy. Cameras can also determine whether an object / obstacle is in the path of the vehicle 12. Cameras also require good visibility and are impaired in darkness, fog, rain, snow, etc. The radar sensors 120 are unaffected in poor visibility conditions and provide accurate time-to-collision (TTC) information. However, the radar sensors 120 are less able to distinguish between different types of objects / obstacles and cannot determine whether an object / obstacle is in the path of the vehicle 12 as well as cameras. The LIDAR sensors 130 provide 3D sensing capability for determining the vehicle's TTC and path of travel, provide good object / obstacle detection, and are robust in both good and poor visibility situations.

[0049] The camera 110, the radar sensor 120, and the LIDAR sensor 130 may be connected to a separate controller, such as a DAS controller 140, and the controller may communicate with the ACU 50 via the CAN bus. Alternatively, both the active and passive safety functionality may be handled by a single controller, such as the ACU 50, in which case the camera 110, the radar sensor 120, and the LIDAR sensor 130 may be directly connected to the ACU 50. These sensors monitor an area within a predetermined field of view and range in front of the vehicle 12.

[0050] The active safety system's sensors provide information (signals, data, etc.) that a controller, such as the ACU 50, the DAS controller 140, or another controller, can use to detect the presence of objects in the vehicle's path of travel. By implementing known techniques such as artificial intelligence (AI) and other algorithms, the controller can determine information related to the detected object, such as the object type, the longitudinal distance from the vehicle, the lateral position in the vehicle's path of travel, the time to collision with the vehicle, the relative speed to the vehicle, the state of the object (e.g., forward-facing, rearward-facing, sideways-facing, moving, stationary, etc.), and the probability of a collision occurring.

[0051] The Fig. 2 through 8 illustrate control algorithms implemented by the vehicle safety system 10 to help protect the vehicle occupant(s) in the event of a frontal impact, referred to herein as a frontal collision, with the vehicle 12. The algorithms are implemented in a vehicle controller, such as the ACU 50, which is operatively connected to the safety devices 14 and is configured to actuate the safety devices in response to detecting the occurrence of a frontal collision. According to the invention, the control algorithms implemented in the vehicle safety system 10 are configured such that the passive safety system 20 adapts or adjusts its response to a frontal collision based on the information received from the active safety system 100.

[0052] The Fig. Figure 2 illustrates an overview of the control algorithm 150 implemented by the vehicle safety system 10 to contribute to the protection of the vehicle occupant(s) in response to detecting the occurrence of a frontal collision. As shown in the Fig. 2, active safety signals 152 are provided by the active safety system 100 to preset algorithms 170. The preset algorithms 170 include collision discrimination algorithms 180, severity estimation algorithms 190, and object identification algorithms 200. As shown in the Fig. 2, the collision discrimination algorithms 180 generate a default detection flag 182. The severity estimation algorithms 190 generate a default severity flag 192. The object identification algorithms 200 generate a default object type flag 202.

[0053] The control algorithm 150 also includes a front-end algorithm 210, which receives the preset detection flag 182, the preset severity flag 192, and the preset object type flag 202 from the preset algorithms 170. The front-end algorithm 210 is the passive control algorithm that implements the metrics used to determine whether to deploy the safety devices 14 based on the signals received from the sensors. The front-end control algorithm 210 adjusts these metrics based on the flags 182, 192, and 202 generated by the preset algorithm 170 in response to the active safety signals 152. The front-end control algorithm 210 generates individual misuse boxes for each object type, individual thresholds for each object type, and individual thresholds for each severity level.

[0054] In the Fig. 3 and Fig. 4, collision discrimination algorithms 180 are shown, which can be implemented in the preset algorithm part 170 of the control algorithm 150. The collision discrimination algorithms 180 of the Fig. 3 and Fig. 4 differ in the type of active safety sensor and the corresponding inputs provided to the algorithms. The algorithms 180 for collision discrimination of the Fig. 3 and Fig. 4 can be implemented individually, in which case the individual algorithm determines the default detection flag. The algorithms 180 for collision discrimination of the Fig. 3 and Fig. 4 may also be implemented in combination, in which case one or both algorithms determine the preset detection flag.

[0055] The algorithm 180 for collision discrimination of the Fig. 3 uses the active safety signals 152 as inputs. In the Fig. 3, the active safety signals 152 are those received from an active safety sensor in the form of a camera (see, for example, the camera 110 in the Fig. 1). The active safety signals 152 include: • Object type, 154, • lateral position of the object, 156, • Time to collision (TTC), 158, • Relative speed of the object, 160, • Longitudinal position of the object, 162.

[0056] The collision discrimination algorithm 180 uses the active safety signals 152 to determine whether a preset condition exists. If a preset condition exists, the collision discrimination algorithm 180 has identified an object type and has also determined that a collision is imminent. The collision discrimination algorithm 180 generates a preset detection flag 182 (see also Fig. 2) in response to ALL of the following conditions being true (see AND gate 236): • Object is identified (Block 220) as automobile, truck, obstacle, pole or “other”. • Lateral position 154 of the object is greater than the minimum threshold and less than the maximum threshold (block 224). • TTC collision discrimination metric indicates an imminent collision (metric 228). • Discrimination metric of a longitudinal distance from a collision indicates an imminent collision (metric 232).

[0057] *Note that blocks 220, 224 and metrics 228, 232 are cached in blocks 222, 226, 230, and 234, respectively. Therefore, once these conditions are met or TRUE, they are cached as TRUE for a predetermined period of time. These cached values ​​can be seen in AND block 236.

[0058] The AND gate 236 receives the latched values ​​from blocks 222, 226, 230, and 234. Once the AND gate 236 is true (TRUE), the preset detection flag 182 is triggered (TRUE). The preset detection flag 182 is latched at block 238. Therefore, once the AND block 236 is true, the preset detection flag 182 is held at TRUE for a predetermined period of time.

[0059] The algorithm 180 for collision discrimination of the Fig. 4 also uses the active safety signals 152 as inputs. In the Fig. 4, the active safety signals 152 are those received from an active safety sensor in the form of a radar sensor (see, for example, the radar sensors 120 in the Fig. 1).

[0060] The active safety signals 152 include: • Object condition 250, • Collision probability, 252, • Time to collision (TTC), 254, • Relative velocity of the object, 256,

[0061] The collision discrimination algorithm 180 uses the active safety signals 152 to determine whether a preset condition exists. If a preset condition exists, the collision discrimination algorithm 180 has identified an object type and has also determined that a collision is imminent. The collision discrimination algorithm 180 generates a preset detection flag 182 (see also Fig. 2) in response to ALL of the following conditions being true (see AND gate 270): • Object state identified (block 258) as moving forward, moving backward, or stationary. • Collision probability > collision probability threshold (block 262). • TTC collision discrimination metric indicates an imminent collision (metric 266).

[0062] *Note that blocks 258, 262, and metric 266 are cached in blocks 260, 264, and 268, respectively. Therefore, once these conditions are met or TRUE, they are cached as TRUE for a predetermined period of time. These cached values ​​can be seen in AND block 270.

[0063] The AND gate 270 receives the buffered values ​​from blocks 260, 264 and 268. As soon as the AND gate 270 is satisfied (TRUE), the preset detection flag 182 (see also Fig. 2) is triggered (TRUE). The preset detection flag 182 is latched at block 272. Therefore, once the AND block 270 is satisfied, the preset detection flag 182 is held at TRUE for a predetermined period of time.

[0064] The Fig. Figure 5 shows the severity estimation subalgorithm 190 of the preset subalgorithm 170 of the control algorithm 150. The severity estimation algorithm 190 implements a severity metric 310 that evaluates the object relative velocity 300 (obtained from the active safety signals 152) to determine whether severity thresholds are exceeded. The severity metric 300 is activated by the preset detection flag 182 (block 306) (see the Fig. 3 and Fig. 4). The severity metric 300 may include any number of severity levels. In the exemplary embodiment of the Fig. 5, the severity metric 300 includes four thresholds: • Gravity minimum (L0) • Severity level 1 (L1) • Severity level 2 (L2) • Maximum gravity (L3)

[0065] The severity metric 300 outputs the severity level, which is buffered in block 312 and output as the default severity flag 192 (see also the Fig. 2). As shown in the table of Fig. 5, the severity levels of the default severity flag 192 can be associated with a corresponding response (ie, a deployment scheme for the safety devices 14) of the passive safety system 20. As shown in the Fig. As shown in Figure 5, these reactions can be, for example, the following: • Gravity minimum (L0) = No action. • Severity level 1 (L1) = Belt tensioners only. • Severity Level 2 (L2) = Seat belt pretensioners and airbag inflator stage 1 only. • Maximum gravity L3 = seat belt pretensioners and airbag inflator stage 1 and stage 2. Object identification

[0066] The Fig. 5 also shows the object identification sub-algorithm 200 of the preset sub-algorithm 170 of the control algorithm 150. The object identification algorithm 200 uses the object type 304 (obtained from the active safety signals 152) and the preset detection flag 182 (see Fig. 3 and Fig. 4). The object type flag 202 is output in response to the AND gate 318, which is TRUE when the preset detection flag 182 is TRUE and when the hold time latch 316 holds a detected object type 314. The object type 314 may be an automobile, a truck, an obstacle, a pole, or if none of these are detected, "other."

[0067] The object identification algorithm 200 outputs the object type flag 202, which indicates the specific type of the object. These object types are listed in the table of Fig. 5 represented as: • Object type 0 = Automobile. • Object type 1 = truck. • Object type 2 = obstacle. • Object type 3 = post. • Object type 4 = Other.

[0068] The collision signals CCU_1X, CZS_3X and CZS_4X generated by the collision sensors 50, 60, 62 are processed in a known manner for use by the vehicle safety system 10. The Fig. Figure 6 shows by way of example how the collision signals for use in the control algorithm 150, in particular in the front algorithm 210 (see Fig. 2). The collision signals CCU_1X, CZS_3X, and CZS_4X are first conditioned by hardware low-pass filters (LPFs), as shown at 320, 322, and 324, respectively, and these signals are transmitted to the ACU 50 for further conditioning.

[0069] As shown at block 330, the low-pass filtered CCU_1X undergoes an analog-to-digital (ADC) conversion at a predetermined frequency / sampling rate. Other conditioning, such as rail checks and bias adjustments, may also be performed. The conditioned CCU_1X from block 330 may also be further conditioned with high-pass filtering (HPF) 332 and low-pass filtering (LPF) 334. The conditioned CCU_1X is fed to the mass-spring damper (MSD) model 336, which uses the mass-spring damper modeling to generate modeled values ​​for the relative velocity (V_REL) and relative displacement (X_REL) resulting from the impact that generated the CCU_1X acceleration. This can be done using known modeling methods based on specific vehicle architectures and occupants with specific characteristics.Examples of this signal state and modeling are described in detail in U.S. Patent Nos. 5,935,182 to Foo et al. and 6,036,225 to Foo et al. The disclosures of these patents are hereby incorporated by reference in their entirety.

[0070] As shown at block 340, the low-pass filtered CZS_3X is subjected to an analog-to-digital conversion (ADC) at a predetermined frequency / sampling rate. Other conditioning, such as rail checks and bias adjustments, may also be performed. In block 342, a moving average of the conditioned CZS_3X from block 340 is calculated to generate CZS_3X_AMA. Similarly, as shown at block 344, the low-pass filtered CZS_4X is subjected to an analog-to-digital conversion (ADC) at a predetermined frequency / sampling rate. Other conditioning, such as rail checks and bias adjustments, may also be performed. At block 346, a moving average of the conditioned CZS_4X from block 344 is calculated to generate CZS_4X_AMA.

[0071] The Fig. 7A and Fig. 7B illustrate a conventional frontal collision discrimination scheme that uses the Fig. 6 specific processed collision signals V_REL, X_REL, CZS_3X_AMA and CZS_4X_AMA to distinguish between different types of frontal collisions. Fig. Figure 7A shows an ACU_X collision discrimination metric that uses only ACU_X, i.e., a metric that evaluates V_REL against X_REL to discriminate whether a frontal collision has occurred. If the collision discrimination metric 350 exceeds a normal threshold, a frontal collision is detected, and the safety devices (airbags, seatbelt pretensioners) are deployed. Misuse boxes are also implemented in the collision discrimination metric to filter out scenarios of vehicle misuse (e.g., off-road use, reckless driving) from being detected as a collision. Accordingly, the metric must—in any order—both exit the misuse box and exceed the normal threshold for a frontal collision to be detected. Fig. 7B illustrates the CZS switching metrics 352 used to switch the collision thresholds and misapplication boxes implemented in the collision discrimination metric 350.

[0072] The Fig. 7A and Fig. The thresholds and misuse boxes shown in Figure 7B, as well as in all other figures of this specification, are merely exemplary and are for illustrative purposes only. Those skilled in the art will appreciate that the characteristics of the thresholds and misuse boxes (e.g., shape, limits, range, number, etc.) can vary widely depending on a variety of factors, such as the particular vehicle platform in which the vehicle safety system 10 is implemented and the safety standards (e.g., NHTSA) for which the vehicle safety system is designed.

[0073] The frontal collision discrimination scheme may need to detect the occurrence of a variety of frontal collision types, such as with a rigid obstacle, a pole, offset, at an angle, and asymmetric, of varying severity determined by speed. To be effective, the collision discrimination metric 350 must not only detect the occurrence of the collision, but must do so within a timeframe in which the safety devices can be deployed and provide effective occupant protection. Each collision type generates metrics with different signatures, some of which are in the Fig. 7A. For some of these crash types, the normal threshold may be effective in detecting the occurrence of the crash within the required time period (in time). For other crash types, the normal threshold may not be effective in detecting the occurrence of the crash within the required time period (too late).

[0074] The Fig. Figure 7A illustrates example metrics for three types of frontal collisions: a high-speed rigid obstacle impact, a high-speed pole impact, and a high-speed offset / angle / asymmetry impact. For example, a high-speed rigid obstacle impact may be a 56 km / h rigid obstacle collision with zero degrees of offset. For example, a high-speed pole impact may be a 48 km / h pole collision with zero degrees of offset. For example, an offset impact may be a 56-64 km / h deformable obstacle collision with 40 percent offset. The vehicle safety system may be designed to meet various safety standards, such as those of the National Highway Traffic Safety Administration (“NHTSA”).The goal is for the collision discrimination metric 350 to detect as many of these [impacts] as possible.

[0075] As in the Fig. As shown in Figure 7A, the normal threshold and misapplication box are effective in distinguishing the event of a high-speed rigid obstacle impact, meaning the metric triggers in a timely manner, as indicated by the star. However, for both the high-speed pole impact and the high-speed offset / angle / asymmetry impact, the normal threshold and misapplication box are ineffective, and the metric triggers too late, as indicated by the stars, respectively.

[0076] To take this into account, the conventional frontal collision discrimination scheme of the Fig. 7A and Fig. 7B the CZS switching metrics 352 ( Fig. 7B) to determine the collision discrimination metric 350 ( Fig. 7A) implemented collision thresholds and misuse boxes. As described in the Fig. 7B, the CZS switching metric 352 uses a metric that evaluates CZS_3X_AMA / CZS_4X_AMA against X_REL to determine whether the threshold and misapplication box used in the crash discrimination metric 350 ( Fig. 7A) are to be switched from the normal threshold and the normal misuse box to the switched threshold and the switched misuse box. As in the Fig. 7B, the threshold is designed to avoid triggering misuse events and low severity events, such as low speed rigid obstacle impacts (e.g., a 10 to 16 km / h rigid obstacle impact with zero percent offset).

[0077] The CZS switching metric 352 indicates a CZS switching (indicated by the stars in the Fig. 7B) when the metric exceeds the threshold. For the events where the crash discrimination metric 350 triggers too late (i.e., the high-speed pole impact and the offset / angle / asymmetry deformable obstacle impact), the CZS switching metric 352 indicates switching at an earlier time. As a result, the CZS switching can be used to switch the threshold and misapplication box implemented in the crash discrimination metric 350 from the normal threshold and misapplication box to the switched threshold and misapplication box. As shown in the Fig. As shown in Figure 7A, the switched threshold triggers in a timely manner for both the high-speed impact on a pole and the impact on a deformable obstacle with offset / angle / asymmetry.

[0078] The front algorithm 210 is in the Fig. 8A and Fig. 8B. The frontal algorithm 210 implements the conventional switched collision discrimination algorithms described above. Advantageously, in addition to the CZS switching described above, the frontal algorithm 210 also implements active threshold switching in response to the preset algorithm 170. In particular, the frontal algorithm 170 implements active threshold switching in response to the preset severity flag 192 and the object type flag 202 to tailor the collision discrimination metrics and misapplication boxes to impending collisions with identified objects and the predicted severity with which the collision will occur.

[0079] The Fig. 8A and Fig. 8B illustrate a frontal collision discrimination scheme or method implemented by the frontal algorithm 210. The frontal algorithm 210 implements active threshold switching in collision discrimination metrics used to detect the occurrence of a frontal collision. The collision discrimination metrics utilize the Fig. 6 specific collision signals V_REL, X_REL, CZS_3X_AMA and CZS_4X_AMA to distinguish between different types of frontal collisions. Fig. 8A illustrates an ACU_X collision discrimination metric 360 that uses only ACU_X, ie, that uses a metric that evaluates V_REL against X_REL to distinguish whether a frontal collision has occurred. Fig. Figure 8B illustrates the CZS switching metrics 390 used to switch the collision thresholds and misapplication boxes implemented in the collision discrimination metric 350. As shown in the Fig. 8A and Fig. 8B, the metrics 360, 390 implement active switching in response to the default severity flag 192 and the object type 202.

[0080] At this point it should be pointed out again that the Fig. 8A and Fig. 8B are merely exemplary and are for illustrative purposes only. Those skilled in the art will appreciate that the characteristics of the thresholds and misuse boxes (e.g., shape, limits, range, number, etc.) can vary widely depending on a variety of factors, such as the particular vehicle platform in which the vehicle safety system 10 is implemented and the safety standards (e.g., NHTSA) for which the vehicle safety system is designed.

[0081] According to the invention, the object type flag 202 is operative to switch the collision discrimination and switching metrics 360, 390 based on the type of object identified by the object identification algorithm 200 (see Fig. 5). The object type flag 202 may be effective to select the metrics 360, 390 implemented by the frontal algorithm. In other words, the frontal algorithm 210 may implement multiple versions of the ACU_X collision discrimination metric 360 and the CZS switching metrics 390 associated with one of the object types identified by the object type flag 202. With reference to the Fig. 5, these object types can be, for example, a car, a truck, an obstacle, a pole, or something else. Thus, based on the object type flag, the front algorithm 210 can select the metrics 360, 390, and all associated thresholds based on the marked object type.

[0082] Referring to the Fig. 8A, the ACU_X crash discrimination metric 360 implements a normal threshold 362 for level 1 / pretensioner and a switched threshold 364. The ACU_X crash discrimination metric 360 also includes a normal misapplication box 374 and a switched misapplication box 376. These normal / switched thresholds and misapplication boxes correspond to those described above with reference to the Fig. 7A. Whether the normal or switched thresholds and misuse boxes are used depends on the CZS switching metric 390 ( Fig. 8B) and in particular whether the CZS switching threshold 392 has been exceeded. This corresponds to the above with reference to the Fig. 7B. When the CZS switching threshold 392 is exceeded, the crash discrimination metric 360 uses the switched threshold 364 and the switched misapplication box 376. It should be noted that the Fig. 8B actually represents two metrics—one using CZS_3X_AMA and one using CZS_4X_AMA, as shown on the metric's vertical axis. Either metric can cause the described CZS switching in the ACU_X crash discrimination metric 360.

[0083] According to the invention, the thresholds implemented in the ACU_X collision discrimination metric 360 and the CZS switching metric 390 can also be switched to preset thresholds and corresponding misapplication boxes in response to the default severity flag 192. The predetermined thresholds and misapplication boxes to which the metrics 360, 390 are switched depend on the default severity flag 192, which is determined by the severity estimation algorithm 190 (see Fig. 5). The switched thresholds and misuse boxes are in the Fig. 8A and Fig. 8B is indicated by dashed lines. With reference to the Fig. 8A, the ACU_X crash discrimination metric 360 includes a normal threshold 366 for Level 1 / pretensioner with a corresponding normal preset misapplication box 378, and a switched preset threshold 368 with a corresponding switched preset misapplication box 380. Similarly, the CZS switch metric 390 includes a CZS switch preset threshold 394.

[0084] Whether the thresholds implemented in the ACU_X collision discrimination metric 360 and the CZS switching metric 390 are the preset thresholds 366, 368, 394 and the preset misapplication boxes 378, 380 depends on the default severity flag 192. The default severity flag may be configured to indicate two or more severity levels. In the Fig. 5, there may be four severity levels: 0 = no action, 1 = pretensioner only, 2 = pretensioner and inflator level 1, and 3 = pretensioner and inflator levels 1 and 2. The metrics 360, 390 may be individually configured to switch to the preset thresholds and misuse boxes in response to any of the severity levels indicated in the preset severity flag 192.

[0085] For example, referring to the ACU_X crash discrimination metric 360, in response to the preset severity flag being ≥ 1, the switched threshold 364 may be switched to the switched preset threshold 368 and the switched preset misapplication box 380. The normal level 1 / pretensioner threshold 362 may be switched to the normal level 1 / pretensioner preset threshold 366 and the normal preset misapplication box 378 in response to the preset severity flag being ≥ 1. Referring to the CZS switching metric 390, in response to the preset severity flag being ≥ 1, the CZS switching threshold 392 may be switched to the CZS switching preset threshold 394. As described in the Fig. 8A and Fig. As shown in Figure 8B, the switched thresholds may have magnitudes smaller or larger than their corresponding normal thresholds, although smaller switched thresholds are the more likely scenario. Similarly, the switched misuse bins may be larger or smaller than their corresponding normal misuse bins.

[0086] From the foregoing, it will be appreciated that the frontal algorithm 210 may not only implement CZS switching to help account for various frontal collisions based on passive sensing, but the frontal algorithm may also implement active switching to account for object types and estimated collision severity based on active sensing.

[0087] With reference to the Fig. 8A, the ACU_X crash discrimination metric 360 may also include a Level 2 threshold 370 for safety systems where the airbag inflator is a two-stage inflator. The second stage of the airbag inflator is used in more severe crashes, which explains the comparatively significant size of the Level 2 threshold 370. In this embodiment, the normal Level 1 / pretensioner threshold 362 is implemented to trigger the pretensioner and the Stage 1 airbag inflator. The Level 2 threshold 370 is implemented such that the Stage 2 airbag inflator is triggered after a predetermined time delay, which may be, for example, 5 ms, 20 ms, 100 ms, etc. As shown in the Fig. 8A, the ACU_X crash discrimination metric 360 may also include a preset Level 2 threshold 372 that may be switched in response to the preset severity flag having a predetermined value, such as ≥ 2, thereby lowering the threshold for triggering Level 2 in response to a high flagged crash severity.

[0088] Although not shown, the ACU_X crash discrimination metric 360 may implement misapplication boxes corresponding to the Level 2 threshold 370 and the Level 2 preset threshold 372. However, due to the significant magnitudes required to trigger a Level 2 event, these misapplication boxes may not be necessary.

[0089] With reference to the Fig. 8B, the CZS switching metric 390 may also include a special CZS threshold 396 for safety systems where the airbag inflator is a two-stage inflator. In this embodiment, the special CZS threshold 396 may be implemented to adjust or tune the deployment delay between stage 1 and stage 2 of the inflator. The delay may be increased or decreased in response to the special CZS threshold 396 being exceeded. As shown in the Fig. 8B, the CZS switching metric 390 may also include a special preset CZS threshold 398 that may be switched in response to the preset severity flag having a predetermined value, such as ≥ 2, thereby lowering the threshold for adjusting the Level 2 deployment delay in response to a high flagged crash severity.

Claims

[1] A vehicle safety system (10) which helps to protect a vehicle occupant in the event of a frontal collision, comprising: a control device; one or more collision sensors (50, 60, 62) for detecting a frontal collision; and an active sensor (110, 120, 130) for detecting objects in the path of the vehicle (12); wherein the control device is designed to generate metrics for to implement collision discrimination that detects the occurrence of a frontal collision in response to signals received from the collision sensors, characterized by that the collision discrimination metrics implement thresholds for determining whether the signals received from the collision sensors indicate the occurrence of a frontal collision; and in that the control device is configured to implement an algorithm that uses the information obtained from the active sensor (110, 120, 130) to detect an object in the path of travel of the vehicle (12) and to select the thresholds implemented in the collision discrimination metrics in response to detecting the object. [2] The vehicle safety system (10) of claim 1, wherein the algorithm implemented by the controller is further configured to select misuse boxes associated with the selected thresholds implemented by the crash discrimination metrics based on the information obtained from the active sensor (110, 120, 130). [3] The vehicle safety system (10) of claim 1, wherein the algorithm implemented by the control device is further configured to: Determining an object type for the object; Determining an estimated severity of a collision with the object; and further in response to at least one of the object type and the estimated severity: selecting the thresholds included in the metrics for Collision discrimination is implemented. [4] The vehicle security system (10) of claim 3, wherein the object type includes one of an automobile, a truck, an obstacle, and a pole. [5] The vehicle safety system (10) of claim 3, wherein the algorithm implemented by the controller is configured to determine the estimated severity by implementing a metric that determines the estimated severity based on a relative speed between the object and the vehicle (12). [6] The vehicle safety system (10) of claim 5, further including at least one actuatable safety device comprising an airbag with a two-stage inflator and a seatbelt pretensioner, and wherein the algorithm implemented by the controller is configured to determine that in response to the estimated severity, one of the following actions is to be performed: Do not operate the seat belt pretensioner or the inflator; Operate only the belt tensioner; Activating the seat belt pretensioner and the first stage of the inflator; Operating the seat belt pretensioner, first stage inflator, and second stage inflator. [7] The vehicle safety system (10) of claim 3, wherein the algorithm implemented by the controller is further configured to select misuse boxes associated with the selected thresholds implemented by the crash discrimination metrics in response to at least one of the object type and the estimated severity. [8] The vehicle safety system (10) of claim 3, wherein the algorithm implemented by the controller is further configured to determine the estimated severity by evaluating an estimated severity discrimination metric that compares a relative velocity of the object relative to the vehicle (12) and the displacement. [9] Vehicle safety system (10) according to claim 1: wherein the collision sensors include front crush zone sensors (CZS) (60, 62); wherein the collision discrimination metrics comprise a CZS shift discrimination metric for evaluating CZS acceleration values ​​to determine whether a CZS shift threshold is exceeded; where the collision discrimination metrics include individual metrics, assigned to each object type determined by the algorithm, with each individual metric having a normal threshold and a normal misuse box, and a CZS switching threshold and a CZS switching misapplication box, wherein the CZS switching threshold and the CZS switching misapplication box are implemented by the crash discrimination metrics when the CZS switching discrimination metric determines that the CZS switching threshold is exceeded, wherein the normal threshold and the normal Misuse box should be implemented if the CZS switching threshold is not exceeded; and wherein the collision discrimination metrics further comprise a normal preset threshold and a normal preset misapplication box, and a preset CZS switching threshold, wherein the preset CZS switching threshold and the preset misapplication box are implemented by the crash discrimination metrics when the CZS switching discrimination metric determines that the CZS switching threshold is exceeded and the algorithm detects the object in the path of the vehicle (12), wherein the normal preset threshold and the preset misapplication box are implemented when the CZS switching threshold is not exceeded and the algorithm detects the object in the path of the vehicle (12). [10] Vehicle safety system (10) according to claim 1, wherein the algorithm implemented by the control device is designed to detect objects in the path of travel of the vehicle (12) by: Identifying the object; Determining whether a lateral position of the object relative to the vehicle is within a predetermined threshold; Evaluating a time-to-collision (TTC) discrimination metric to determine whether a relative speed between the object and the vehicle (12) exceeds a predetermined threshold determining that a collision is imminent; and Evaluating a discrimination metric of a longitudinal distance from a collision to determine whether the longitudinal position of the object relative to the vehicle (12) exceeds a predetermined threshold determining that a collision is imminent. [11] Vehicle safety system (10) according to claim 1, wherein the algorithm implemented by the control device is adapted to detect objects in the path of travel of the vehicle (12) by: Identifying a state of the object; Determining whether a calculated collision probability is greater than a predetermined collision probability; and Evaluating a time-to-collision (TTC) discrimination metric to determine whether a relative speed between the object and the vehicle (12) exceeds a predetermined threshold determining that a collision is imminent. [12] The vehicle safety system (10) of claim 1, wherein the collision sensors are components of a passive safety system further including at least one actuatable safety device, the passive safety system being configured to respond to the occurrence of a vehicle collision by actuating the safety device, and the active sensor (110, 120, 130) being a component of an active safety system configured to anticipate the occurrence of the vehicle collision. [13] The vehicle safety system (10) of claim 1, wherein the crash sensors include at least one of crumple zone sensors (60, 62) and airbag control unit (ACU) sensors in the form of accelerometers for measuring vehicle acceleration along a vehicle longitudinal axis. [14] The vehicle safety system (10) of claim 13, wherein the crash discrimination metrics comprise a value determined by comparing acceleration versus displacement or velocity versus displacement as measured by the crash sensors, and the crash discrimination metrics determine the occurrence of a frontal collision in response to the value exceeding the threshold. [15] The vehicle safety system (10) of claim 1, wherein the active sensor (110, 120, 130) includes a camera, and wherein the information obtained from the active sensor (110, 120, 130) includes the object type, a lateral position of the object, a time to collision (TTC) with the object, a relative speed of the object and the vehicle (12), and a longitudinal position of the object relative to the vehicle (12). [16] The vehicle safety system (10) of claim 5, wherein the algorithm for detecting the object in the path of travel of the vehicle (12) is configured to detect the object by: Determine whether the object type is a recognized object type; Determining whether the lateral position of the object is within a predetermined threshold range; Determining whether the TTC with the object is within a threshold indicating an impending collision by evaluating a TTC collision discrimination metric to determine whether the TTC exceeds a predetermined threshold, wherein the threshold is determined with respect to the relative speed of the object and the vehicle (12); and Determining whether the longitudinal position of the object relative to the vehicle (12) is within a threshold indicating an impending collision by evaluating a discrimination metric of the longitudinal distance from a collision to determine whether the longitudinal distance between the object and the vehicle (12) exceeds a predetermined threshold, wherein the threshold is determined with respect to the relative speed of the object and the vehicle (12). [17] The vehicle safety system (10) of claim 16, wherein the detected object type includes an automobile, a truck, an obstacle, or a pole. [18] The vehicle safety system (10) of claim 1, wherein the active sensor (110, 120, 130) comprises a radar sensor, and wherein the information obtained from the active sensor (110, 120, 130) includes an object state, a collision probability, a time to collision (TTC) with the object, and a relative speed of the object and the vehicle (12). [19] The vehicle safety system (10) of claim 18, wherein the algorithm for detecting the object in the path of travel of the vehicle (12) is configured to detect the object by: Determine whether the object state is a recognized object state; Determining whether the collision probability is greater than a predetermined threshold probability; and Determining whether the TTC with the object is within a threshold indicating an impending collision by evaluating a TTC collision discrimination metric to determine whether the TTC exceeds a predetermined threshold, wherein the threshold is determined with respect to the relative speed of the object and the vehicle (12). [20] The vehicle safety system (10) of claim 19, wherein the detected object state includes a forward state, a backward state, a sideways state, a stationary state, or a moving state. [21] The vehicle safety system (10) of claim 1, wherein the active sensor (110, 120, 130) includes at least one of a camera, a radar sensor, and a laser radar sensor (LIDAR). [22] The vehicle safety system (10) of claim 1, wherein the control device comprises an airbag controller unit (ACU).

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