Vehicle safety system implementing an integrated active-passive frontal crash control algorithm

Abstract

A vehicle safety system for helping to protect vehicle occupants in the event of a frontal crash includes a controller, one or more crash sensors for sensing a frontal crash, and an active sensor for detecting objects in the path of the vehicle. The controller is configured to implement a crash discrimination metric that detects the occurrence of a frontal crash in response to signals received from the crash sensors. The crash discrimination metric implements a threshold for determining whether the signals received from the crash sensors are indicative of the occurrence of a frontal crash. The controller is configured to implement an algorithm that uses information obtained from the active sensor to detect objects in the path of the vehicle and, in response to detecting an object, selects the threshold implemented in the crash discrimination metric.

Description

Background Technology

[0001] Modern vehicles include various systems designed to help ensure occupant safety. These vehicle safety systems can include passive safety systems and / or active safety systems. Generally, passive safety systems are reactive systems that provide occupant protection in response to the detection of events requiring occupant protection, such as a vehicle collision. On the other hand, active safety systems attempt to anticipate events requiring occupant protection and take proactive avoidance measures.

[0002] Passive safety systems include one or more passive restraint devices, such as airbags and seatbelt retractors, which can be actuated to help protect vehicle occupants. These vehicle safety systems utilize an airbag control unit operatively connected to the airbag and various collision sensors, such as accelerometers and pressure sensors. In response to determining a collision scenario based on information provided by the collision sensors, the airbag control unit can be operated to deploy the airbag by activating an inflator that directs inflation fluid into the airbag. When inflated, the driver's airbag and passenger airbag help protect occupants from impacts with vehicle components such as the dashboard and / or steering wheel.

[0003] Active safety systems utilize sensing devices such as cameras, radar, lidar, and ultrasonic transducers to determine the conditions surrounding the vehicle. In response to the sensed conditions, vehicle warning systems can provide visual, audible, and tactile warnings to the driver. This is the case, for example, in blind spot detection, lane departure warning, front / rear object detection, intersection traffic detection, and pedestrian detection. Active safety systems can also use sensed conditions to proactively actuate vehicle controls, such as adaptive cruise control, active braking, and active steering in response to lane departure detection. Each sensing device used in an active safety system has its own specific advantages.

[0004] Cameras are highly effective at object detection. When positioned to view from several angles, they provide information to the vehicle, which the vehicle's safety system's artificial intelligence algorithms can use to detect external objects along the roadside, such as other vehicles, pedestrians, or objects (like trees or trash cans). Cameras can precisely measure angles, allowing vehicle safety systems to identify early on whether an approaching object will enter the vehicle's path. By combining long-range and short-range zoom with varying degrees of wide and narrow field of view, cameras become crucial tools for safety features such as collision avoidance, adaptive cruise control, automatic braking systems, and lane-keeping assist.

[0005] Radar sensors use an echo system to detect objects, which is advantageous in situations where visibility is poor and camera effectiveness is reduced. Radar sensors emit electromagnetic waves and receive the "echoes" 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 vehicles. Radar sensors function regardless of weather, lighting, or visibility conditions, making them ideal for distance control, collision warnings, blind spot detection, emergency braking, and more.

[0006] LiDAR sensors also employ the echo principle, using laser pulses instead of radio waves. LiDAR sensors record distance and relative speed with accuracy comparable to radar. Furthermore, LiDAR sensors can identify object types and angles between objects with even higher levels of accuracy. Therefore, LiDAR sensors can be used to effectively identify more complex traffic situations, even in darkness. Unlike cameras and radar sensors, the field of view is not critical for LiDAR sensors because they can record a 360-degree environment around a vehicle. High-resolution 3D solid-state LiDAR sensors can even render pedestrians and smaller objects in three dimensions. Summary of the Invention

[0007] This invention relates to a vehicle safety system comprising both active and passive components. In this specification, "active safety" refers to technologies that help prevent collisions (i.e., "collision avoidance"), and "passive safety" refers to vehicle components (such as airbags, seat belts, and the vehicle's physical structure (e.g., crash buffers) that help protect occupants in response to the detection of a collision.

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

[0009] Active safety systems, such as collision avoidance systems, are designed to prevent or reduce the severity of vehicle collisions by using radar (all-weather), laser (LIDAR), cameras (using image recognition), or a combination thereof to detect impending collisions. In response to the detection of an impending collision, a collision avoidance system can generate operator warnings (visual, audible, tactile) and can also actuate 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 can implement automatic emergency braking to detect a potential forward collision and activate the vehicle's braking system to slow the vehicle down in order to avoid or mitigate the collision. Once an impending collision is detected, the collision avoidance system provides a warning to the driver. When a collision is imminent, the collision avoidance system takes action automatically and autonomously by applying emergency braking, without any driver input.

[0011] Active safety systems can be standalone systems or subsystems that utilize components of another system, such as a driver assistance system (DAS). This DAS uses camera, radar, and LiDAR data to provide driver assistance functions such as adaptive cruise control, lane departure warning, blind spot monitoring, and parking assist. These components can even be used to provide autonomous driving capabilities.

[0012] According to the present invention, information obtained from active safety systems is used to enhance frontal collision detection performed by passive safety systems in order to improve the responsiveness of vehicle safety systems.

[0013] According to one aspect, a vehicle safety system for assisting in protecting vehicle occupants in the event of a frontal collision includes: a controller, one or more collision sensors for sensing a frontal collision, and active sensors for detecting objects in the path of the vehicle. The controller is configured to implement a collision discrimination metric for detecting the occurrence of a frontal collision in response to signals received from the collision sensors. The collision discrimination metric implements a threshold 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 information obtained from the active sensors to detect objects in the path of the vehicle and selects a threshold implemented in the collision discrimination metric 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 can be further configured to select misuse boxes associated with a selected threshold implemented by the collision discrimination metric based on information obtained from the active sensor.

[0015] According to another aspect, alone or in combination with any other aspect, the algorithm implemented by the controller can be further configured to: determine the object type of the object; determine the estimated severity of the collision with the object; and further select a threshold implemented in the collision discrimination metric 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 a car, truck, obstacle, or pole.

[0017] According to another aspect, alone or in combination with any other aspect, the algorithm implemented by the controller can be configured to determine the estimated severity by implementing a metric based on the 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 further include at least one actuable safety device, the safety device comprising an airbag with two-stage inflation devices and a seatbelt pretensioner. An algorithm implemented by the controller may be configured to determine, in response to the estimated severity, one of the following actions to be taken: neither actuating the seatbelt pretensioner nor the inflation device; actuating only the seatbelt pretensioner; actuating the seatbelt pretensioner and the first stage of the inflation device; or actuating the seatbelt pretensioner, the first stage of the inflation device, and the second stage of the inflation device.

[0019] According to another aspect, alone or in combination with any other aspect, the algorithm implemented by the controller can be further configured to select a misuse box associated with a selected threshold implemented by the collision discrimination metric 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 can further be configured to determine the estimated severity by evaluating an estimated severity discriminant metric that compares the relative velocity of the object with the displacement of the vehicle.

[0021] According to another aspect, alone or in combination with any other aspect, the collision sensor may include a front crumple zone sensor (CZS). The collision discrimination metric may include a CZS switching discrimination metric for evaluating CZS acceleration values ​​to determine whether a CZS switching threshold is exceeded. The collision discrimination metric may include a separate metric associated with each object type determined by the algorithm. Each separate metric may include a normal threshold and a misuse box, and a CZS switching threshold and a misuse box. When the CZS switching discrimination metric determines that the CZS switching threshold is exceeded, the collision discrimination metric may implement the CZS switching threshold and the misuse box. The normal threshold and the misuse box may be implemented if the CZS switching threshold is not exceeded. The collision discrimination metric may further include a normal preset threshold and a misuse box, and a CZS switching preset. When 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 collision discrimination metric implements the CZS switching preset threshold and the misuse box. The normal preset threshold and misuse box can be implemented if the CZS switching threshold is not exceeded and the algorithm detects an object in the vehicle's path.

[0022] According to another aspect, alone or in combination with any other aspect, the algorithm implemented by the controller can be configured to detect objects in the path of the vehicle by: identifying the object; determining whether the lateral position of the object relative to the vehicle is within a predetermined threshold; evaluating a time-of-collision (TTC) discriminant metric to determine whether the relative velocity between the object and the vehicle exceeds a predetermined threshold indicating an impending collision; and evaluating a longitudinal distance collision discriminant metric to determine whether the longitudinal position of the object relative to the vehicle exceeds a predetermined threshold indicating an impending collision.

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

[0024] According to another aspect, alone or in combination with any other aspect, the collision sensor may be a component of a passive safety system, which further includes at least one actuable safety device. The passive safety system is 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, which is configured to predict the occurrence of the vehicle collision.

[0025] According to another aspect, alone or in combination with any other aspect, the collision sensor may include at least one of a crush zone sensor and an airbag control unit (ACU) sensor, the sensor being in the form of an accelerometer for measuring vehicle acceleration along the vehicle's longitudinal axis.

[0026] According to another aspect, alone or in combination with any other aspect, the collision discrimination metric may have a value determined by comparing acceleration with displacement, or velocity with displacement, as measured by the collision sensor, and the collision discrimination metric determines that a frontal collision has occurred in response to the value exceeding the threshold.

[0027] According to another aspect, alone or in combination with any other aspect, the active sensor may include a camera, and wherein the information obtained from the active sensor may include the object type, the object's lateral position, the time of impact (TTC) with the object, the relative velocity of the object to the vehicle, and / or the object's longitudinal position relative to the vehicle.

[0028] According to another aspect, alone or in combination with any other aspect, an algorithm for detecting objects in the path of the vehicle can be configured to detect the object by: determining whether the object type is an identified object type; determining whether the lateral position of the object is within a predetermined threshold range; determining whether the TTC with the object exceeds a predetermined threshold by evaluating a TTC collision discrimination metric, thereby determining whether the TTC is within a threshold indicating an impending collision, the threshold being determined with respect to the relative speed of the object and the vehicle; and determining whether the longitudinal distance between the object and the vehicle exceeds a predetermined threshold by evaluating a longitudinal distance collision discrimination metric, thereby determining whether the longitudinal position of the object relative to the vehicle is within a threshold indicating an impending collision, the threshold being 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 the object state, the probability of collision, the time to collision (TTC) with the object, and the relative speed between the object and the vehicle.

[0030] According to another aspect, alone or in combination with any other aspect, an algorithm for detecting objects in the vehicle's path is configured to detect the object by: determining whether the object's state is an identified object state; determining whether the collision probability is greater than a predetermined threshold probability; and determining whether the TTC with the object exceeds a predetermined threshold by evaluating a TTC collision discrimination metric, thereby determining whether the TTC is within a threshold indicating an impending collision, the threshold being determined with respect to the relative speed between the object and the vehicle.

[0031] According to another aspect, alone or in combination with any other aspect, the identified object state can be a forward state, a backward state, a lateral 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 lidar (LIDAR) sensor.

[0033] According to another aspect, alone or in combination with any other aspect, the controller may be an airbag controller unit (ACU). Attached Figure Description

[0034] Figure 1 This is a schematic illustration of a vehicle including a vehicle safety system, based on an example configuration.

[0035] Figures 2 to 5 It is a schematic diagram illustrating the active safety control algorithm implemented in a vehicle safety system.

[0036] Figure 6 It is a schematic diagram illustrating the modulation of collision signals implemented in a vehicle safety system.

[0037] Figures 7A to 7B This is a schematic diagram illustrating known collision discrimination metrics implemented in a vehicle safety system.

[0038] Figures 8A to 8B It is a schematic diagram illustrating a collision discrimination metric that includes active switching features implemented in a vehicle safety system. Detailed Implementation

[0039] In this manual, the left and right sides of the vehicle are sometimes referred to. These references should be understood as referring to the forward direction of the vehicle's movement. Therefore, a reference to the "left" side of the vehicle corresponds to the driver's side ("DS"). A reference to the "right" side of the vehicle corresponds to the passenger side ("PS").

[0040] Similarly, this specification provides some description of the vehicle's axes, specifically the X-axis, Y-axis, and Z-axis. The X-axis is the longitudinally extending central axis of the vehicle. The Y-axis is the laterally extending axis of the vehicle, perpendicular to the X-axis. The Z-axis is the vertically extending axis of the vehicle, perpendicular to both the X-axis and Y-axis. The X-axis, Y-axis, and Z-axis intersect at or near the vehicle's center of gravity ("COG").

[0041] Vehicle safety systems

[0042] refer to Figure 1 For example, vehicle 12 includes vehicle safety system 10, which includes passive safety system 20 and active safety system 100. Passive safety system 20 includes actuable vehicle occupant protection devices, which are schematically shown at 14. Protection device 14 may include any actuable vehicle occupant protection devices, such as front airbags, side airbags, curtain airbags, knee pad airbags, actuable seat belt pretensioners and / or retractors. Passive safety system 20 also includes an airbag electronic control unit (referred to herein as an airbag controller unit or “ACU”) 50 operatively connected to protection device 14. ACU 50 is operable to control the actuation of protection device 14 in response to vehicle conditions sensed via one or more sensors operatively connected to ACU.

[0043] Passive safety system 20 includes several sensors (such as accelerometers and / or pressure sensors) for measuring certain conditions of vehicle 12, and determining whether to actuate vehicle occupant protection device 14 based on these conditions. These sensors may be installed at various locations throughout vehicle 12, selected to allow sensing of specific vehicle conditions for which the sensors are intended to operate. In this specification, vehicle safety system 10 is described as including several different types and locations of collision sensors in vehicle 12. The collision sensors described herein are not necessarily a complete list of sensors included in vehicle safety system 10. The sensors described herein are only those used by the present invention to detect the occurrence of a frontal impact. Therefore, those skilled in the art will understand that vehicle safety system 100 may include one or more other collision sensors of any type, any number, and any location in vehicle 12.

[0044] Passive safety system 20 is implemented in ACU 50 and is used to detect the occurrence of a frontal vehicle collision. For this purpose, vehicle safety system 10 includes a left crush zone sensor 60 and a right crush zone sensor 62. The left crush zone sensor 60 and the right crush zone sensor 62 are accelerometers configured to sense vehicle acceleration and transmit signals indicating those accelerations to ACU 50. ACU 50 is configured to determine whether the magnitude of the sensed acceleration meets or exceeds a threshold sufficient to indicate that a collision event has occurred, and actuates safety devices in response to this determination.

[0045] exist Figure 1 In the compression zone, sensors 60 and 62 are uniaxial accelerometers, which are configured to detect compression in a region parallel to the longitudinal axis X. VEH The accelerations in the directions indicated are generally indicated by arrows LT_CZS and RT_CZS, respectively, shown in the schematic representation of the sensors. The left compression zone sensor 60 and the right compression zone sensor 62 are located at or near the left (DS) and right (PS) front corners of the vehicle 12, respectively. The left compression zone sensor 60 and the right compression zone sensor 62 may, for example, be mounted behind the front bumper 16 of the vehicle at these front corner locations. The ACU 50 includes an integrated 2-axis accelerometer 52 for sensing vehicle accelerations along the X and Y axes. These accelerations are indicated by CCU_X and CCU_Y, respectively.

[0046] Vehicle safety system 10 is implemented and configured to cooperate with other vehicle systems. ACU 50 can be operably connected to Body Control Module (BCM) 30, for example, via a Vehicle Controller Area Network (CAN) bus. BCM 30 can communicate with other vehicle systems via the CAN bus, such as chassis control, stability control, traction / slip control, anti-lock braking system (ABS), tire pressure monitoring system (TPMS), navigation system, instrument clusters (speed, throttle position, brake pedal position, etc.), infotainment (“infotainment”) systems, and other systems. Through the CAN bus interface, ACU 50 can communicate with any of these external systems to provide and / or receive data.

[0047] Still referencing Figure 1The active safety system 100 may have a known configuration, including one or more active safety system components configured to provide active safety functions in a known manner. The active safety system 100 may utilize components of driver assistance systems (DAS), which, as the name suggests, provide assistance to the vehicle operator while driving. These components can help provide DAS functions such as the aforementioned active cruise control, lane departure warning, blind spot monitoring, parking assist, etc. These components can even be components used to provide autonomous driving functions, and therefore can use artificial intelligence (AI) and other machine learning techniques to provide a wealth of information about the vehicle's surroundings. For collision avoidance functions, the active safety system can provide collision warnings (auditory, visual, tactile), automatic emergency braking, and automatic emergency steering.

[0048] The active safety system 100 includes components in the form of, for example, camera sensors, radar sensors, and lidar (LIDAR) sensors. Camera sensors 110 are mounted high on the windshield 18 in a forward-facing manner, for example, behind or in the area of ​​a rearview mirror. Multiple radar sensors 120 may be mounted forward, in the area of ​​the bumper 16, for example, in the grille. Lidar (LIDAR) sensors 130 may be mounted on or near the vehicle roof 22.

[0049] Camera sensor 110 is effective in providing a wide field of view and has the ability to identify various objects / obstacles with high accuracy. The camera can also determine whether an object / obstacle is in the path of vehicle 12. The camera also requires good visibility and deteriorates in dark conditions, fog, rain, snow, etc. Radar sensor 120 does not deteriorate in poor visibility conditions and does provide accurate indications of time-of-collision (TTC). However, radar sensor 120 is less capable of distinguishing different types of objects / obstacles and is not as good as the camera in determining whether an object / obstacle is in the path of vehicle 12. LiDAR sensor 130 provides 3D sensing capabilities for TTC and vehicle path determination, offers good object / obstacle recognition, and is robust in both good and poor visibility conditions.

[0050] Camera 110, radar sensor 120, and LIDAR sensor 130 can be connected to a separate controller, such as DAS controller 140, which can communicate with ACU 50 via a CAN bus. Alternatively, both active and passive safety functions can be handled by a single controller, such as ACU 50, in which case camera 110, radar sensor 120, and LIDAR sensor 130 can be directly connected to ACU 50. These sensors monitor the area in front of vehicle 12 within the vehicle's predetermined field of vision and range.

[0051] Active safety system sensors provide information (signals, data, etc.) that a controller (such as the ACU 50, DAS controller 140, or other controllers) can use to detect the presence of objects in the vehicle's path. By implementing known methods (such as artificial intelligence (AI) and other algorithms), the controller can determine information related to the detected object, such as the object's type, longitudinal distance from the vehicle, lateral position in the vehicle's path, time of impact with the vehicle, relative speed to the vehicle, the object's state (e.g., facing forward, facing backward, facing sideways, moving, stationary, etc.), and the probability of a collision.

[0052] Figure 2 Figures 8 through 8 illustrate control algorithms implemented by the vehicle safety system 10 to aid in the protection of vehicle occupants in the event of a frontal collision (referred to herein as a frontal collision) with vehicle 12. These algorithms are implemented in a vehicle controller (such as ACU 50), which is operatively connected to the safety device 14 and configured to actuate the safety device in response to the detection of a frontal collision. According to the invention, the control algorithms implemented in the vehicle safety system 10 are configured to cause the passive safety system 20 to adjust or regulate its response to a frontal collision based on information obtained from the active safety system 100.

[0053] Overview of Control Algorithms

[0054] Figure 2 An overview of a control algorithm 150 implemented by the vehicle safety system 10 in response to the detection of a frontal collision is presented. (See also:) Figure 2 As shown, the active safety signal 152 from the active safety system 100 is provided to the preset algorithm 170. The preset algorithm 170 includes a collision detection algorithm 180, a severity estimation algorithm 190, and an object recognition algorithm 200. Figure 2 As shown, collision detection algorithm 180 generates a preset detection flag 182. Severity estimation algorithm 190 generates a preset severity flag 192. Object recognition algorithm 200 generates a preset object type flag 202.

[0055] The control algorithm 150 also includes a positive algorithm 210, which receives a preset detection flag 182, a preset severity flag 192, and a preset object type flag 202 from a preset algorithm 170. The positive algorithm 210 is a passive control algorithm that implements metrics for determining whether to deploy the security device 14 based on signals received from sensors. The positive control algorithm 210 adjusts these metrics based on the flags 182, 192, and 202 generated from the preset algorithm 170 in response to the active security signal 152. The positive control algorithm 210 generates separate misuse boxes for each object type, separate thresholds for each object type, and separate thresholds for each severity level.

[0056] Collision detection

[0057] Figure 3 and Figure 4 A collision detection algorithm 180 is shown that can be implemented in the preset algorithm 170 section of the control algorithm 150. Figure 3 and Figure 4 The difference between the collision discrimination algorithm 180 and the other lies in the type of active safety sensor and the corresponding input provided to the algorithm. Figure 3 and Figure 4 The collision detection algorithm 180 can be implemented independently, in which case the independent algorithm determines a preset detection flag. Figure 3 and Figure 4 The collision detection algorithm 180 can also be implemented in combination, in which case one or both of the two algorithms determine the preset detection flag.

[0058] Figure 3 The collision detection algorithm 180 uses the active safety signal 152 as input. Figure 3 In the middle, the active safety signal 152 is from the presenting camera (see example). Figure 1 The signal is obtained by an active safety sensor in the form of a camera 110. Active safety signal 152 includes:

[0059] ·Object type 154

[0060] • Object's horizontal position 156

[0061] • Time to collision (TTC) 158

[0062] The relative velocity of the object is 160.

[0063] • Object's vertical position 162

[0064] Collision detection algorithm 180 uses active safety signal 152 to determine whether a preset condition exists. When the preset condition exists, collision detection algorithm 180 has identified the object type and also determined that a collision is imminent. In response to all of the following conditions being true (see AND gate 236), collision detection algorithm 180 generates a preset detection flag 182 (see also...). Figure 2 ):

[0065] • The object is identified (box 220) as a car, truck, obstacle, pole, or "other".

[0066] • The object's horizontal position 154 is greater than the minimum threshold and less than the maximum threshold (box 224).

[0067] • The TTC collision discrimination metric indicates an impending collision (metric 228).

[0068] • The longitudinal distance collision discrimination metric indicates an impending collision (metric 232).

[0069] *Note that boxes 220 and 224, and measures 228 and 232 are time-locked at boxes 222, 226, 230, and 234, respectively. Therefore, once these conditions are met or become true, they are time-locked for a predetermined time period. These time-locked values ​​can be seen at box 236.

[0070] AND gate 236 receives time-locked values ​​from boxes 222, 226, 230, and 234. Once AND gate 236 is satisfied (true), preset detection flag 182 is triggered (true). Preset detection flag 182 is time-locked at box 238. Therefore, once AND gate 236 is satisfied, preset detection flag 182 remains true for a predetermined time period.

[0071] Figure 4 The collision detection algorithm 180 also uses the active safety signal 152 as input. Figure 4 In this context, the active safety signal 152 originates from a radar sensor (see example...). Figure 1 The signal is obtained by an active safety sensor in the form of a radar sensor 120. The active safety signal 152 includes:

[0072] ·Object State 250

[0073] Collision probability 252

[0074] Impact Time (TTC) 254

[0075] The relative velocity of the object is 256.

[0076] Collision detection algorithm 180 uses active safety signal 152 to determine whether a preset condition exists. When the preset condition exists, collision detection algorithm 180 has identified the object type and also determined that a collision is imminent. In response to all of the following conditions being true (see AND gate 270), collision detection algorithm 180 generates a preset detection flag 182 (see also...). Figure 2 ):

[0077] • The object's state is identified (box 258) as moving forward, moving backward, or stationary.

[0078] • Collision probability > collision threshold probability (box 262).

[0079] • The TTC collision discrimination metric indicates an impending collision (metric 266).

[0080] *Note that boxes 258, 262, and metric 266 are time-locked at boxes 260, 264, and 268, respectively. Therefore, once these conditions are met or become true, they are time-locked for a predetermined time period. These time-locked values ​​can be seen at box 270.

[0081] AND gate 270 receives time-locked values ​​from frames 260, 264, and 268. Once AND gate 270 is satisfied (true), a preset detection flag 182 (see also...) is activated. Figure 2 The condition will be triggered (true). The preset detection flag 182 is time-locked at box 272. Therefore, once box 270 is satisfied, the preset detection flag 182 will remain true for a predetermined time period.

[0082] Severity assessment

[0083] Figure 5 The severity estimation algorithm 190 portion of the preset algorithm 170 of the control algorithm 150 is shown. The severity estimation algorithm 190 implements a severity metric 310, which evaluates (obtained from the active safety signal 152) the relative velocity 300 of an object to determine whether a severity threshold has been exceeded. The severity metric 310 is determined by a preset detection flag 182 (see [link to relevant documentation]). Figure 3 and Figure 4 Enabled (box 306). Severity metric 310 can include any number of severity levels. Figure 5 In the example configuration, the severity metric 310 includes four thresholds:

[0084] • Minimum severity (L0)

[0085] • Severity Level 1 (L1)

[0086] • Severity Level 2 (L2)

[0087] • Maximum Severity (L3)

[0088] Severity metric 310 outputs a severity level, which is time-latched at box 312 and output as a preset severity flag 192 (see also...). Figure 2 ).like Figure 5 As shown in the table, the severity level of the preset severity flag 192 can be associated with the corresponding response of the passive safety system 20 (i.e., the deployment scheme of the security device 14). For example, as Figure 5 As shown, these responses can be:

[0089] • Minimum severity (L0) = No action.

[0090] • Severity Level 1 (L1) = Seatbelt pretensioner only.

[0091] • Severity Level 2 (L2) = Level 1 only for seat belt pretensioners and airbag inflators.

[0092] • Maximum severity (L3) = Level 1 and Level 2 of seat belt pretensioners and airbag inflators.

[0093] Object recognition

[0094] Figure 5 The object recognition algorithm 200 portion of the preset algorithm 170 portion of the control algorithm 150 is also shown. The object recognition algorithm 200 utilizes object type 304 (obtained from active safety signal 152) and preset detection flag 182 (see [link to relevant documentation]). Figure 3 and 4 The object type flag 202 is output in response to AND gate 318. This object type flag is true when preset detection flag 182 is true and time latch 316 latches the detected object type 314. Object type 314 can be a car, truck, obstacle, pole, or "other" if none of these types are detected.

[0095] The object recognition algorithm 200 outputs an object type flag 202 indicating the type of the identified object. These object types are... Figure 5 The table shows:

[0096] • Object type 0 = Car.

[0097] • Object type 1 = Truck.

[0098] • Object type 2 = Obstacle.

[0099] Object type 3 = Rod.

[0100] • Object type 4 = Other.

[0101] Collision signal modulation

[0102] The collision signals CCU_1X, CZS_3X, and CZS_4X generated by collision sensors 50, 60, and 62 are modulated in a known manner for use by vehicle safety system 10. For example, Figure 6 This demonstrates how collision signals can be modulated for use in control algorithm 150, particularly in frontal algorithm 210 (see [link]). Figure 2 The collision signals CCU_1X, CZS_3X, and CZS_4X are initially modulated using hardware low-pass filters (LPFs) (as shown in 320, 322, and 324, respectively), and these signals are then transmitted to ACU 50 for further modulation.

[0103] As shown in box 330, the low-pass filtered CCU_1X undergoes analog-to-digital (ADC) conversion at a predetermined frequency / sampling rate. Other modulations, such as track checking and bias adjustment, can also be performed. The conditioned CCU_1X from box 330 can be further conditioned using a high-pass filter (HPF) 332 and a low-pass filter (LPF) 334. The conditioned CCU_1X is fed to a damped spring mass (MSD) model 336, which uses the damped spring mass to model and generate modeled values ​​for the relative velocity (V_REL) and relative displacement (X_REL) resulting from an impact that produces CCU_1X acceleration. This can be accomplished using known modeling methods, based on a specific vehicle architecture and on occupants with specified characteristics. Examples of such signal conditioning and modeling are described in detail in U.S. Patent Nos. 5,935,182 and 6,036,225 to Foo et al., the disclosures of which are incorporated herein by reference in their entirety.

[0104] As shown in box 340, the low-pass filtered CZS_3X undergoes analog-to-digital (ADC) conversion at a predetermined frequency / sampling rate. Other modulations, such as track checking and bias adjustment, may also be performed. At box 342, a moving average of the adjusted CZS_3X from box 340 is calculated to produce CZS_3X_AMA. Similarly, as shown in box 344, the low-pass filtered CZS_4X undergoes analog-to-digital (ADC) conversion at a predetermined frequency / sampling rate. Other modulations, such as track checking and bias adjustment, may also be performed. At box 346, a moving average of the adjusted CZS_4X from box 344 is calculated to produce CZS_4X_AMA.

[0105] Collision detection after normal switching

[0106] Figure 7A and Figure 7B This demonstrates a conventional frontal collision detection scheme, which utilizes... Figure 6 The modified collision signals V_REL, X_REL, CZS_3X_AMA, and CZS_4X_AMA are determined to identify various frontal collision types. Figure 7A The ACU_X collision discrimination metric is demonstrated. This metric utilizes only ACU_X, i.e., it uses the metric that evaluates V_REL and X_REL to determine whether a frontal collision has occurred. When the collision discrimination metric 350 exceeds a normal threshold, a frontal collision is detected and safety equipment (airbags, seatbelt pretensioners) is deployed. The collision discrimination metric also implements a misuse box to filter out vehicle misuse scenarios (e.g., off-road use, reckless driving) from being identified as collisions. Therefore, for a frontal collision to be detected, the metric must leave the misuse box and exceed the normal threshold in any order. Figure 7B CZS switching metrics 352 are shown, which are used to switch the collision threshold and misuse box implemented in collision discrimination metric 350.

[0107] Figure 7A and Figure 7B The thresholds and misuse boxes shown in connection with this problem in any other figures in this specification are for illustrative purposes only and are examples only. Those skilled in the art will understand that the characteristics of the thresholds and misuse boxes (e.g., shape, limitations, range, number, etc.) can vary widely depending on various factors, such as the specific vehicle platform on which the vehicle safety system 10 is implemented and the safety standards (e.g., NHTSA) that the vehicle safety system is designed to meet.

[0108] A frontal collision discrimination scheme may be needed to detect various types of frontal collisions, such as rigid obstacles, poles, and various sizes of offsets, angles, and asymmetries determined by velocity. For effectiveness, the collision discrimination metric 350 must not only detect the occurrence of a collision, but also complete this detection within a time frame in which safety equipment can be deployed and effective passenger protection can be provided. Each collision type will generate a metric with different characteristics, some of which include... Figure 7A As shown. For some of these collision types, the normal threshold can effectively detect the occurrence of a collision within the required time period (“timely”). For other collision types, the normal threshold may not be able to detect the occurrence of a collision within the required time period (“too late”).

[0109] Figure 7AExample metrics for three types of frontal collisions are presented: high-speed rigid barrier impact, high-speed pole impact, and high-speed offset / angle / asymmetric impact. A high-speed rigid barrier impact can be, for example, a 56 kph, zero-offset rigid barrier impact. A high-speed pole impact can be, for example, a 48 kph, zero-offset pole impact. An offset impact can be, for example, a 56 to 64 kph, 40% offset deformable barrier impact. Vehicle safety systems can be designed to meet various safety standards, such as those established by the National Highway Traffic Safety Administration (“NHTSA”). The goal is for the collision discrimination metric 350 to detect as many of these collisions as possible.

[0110] like Figure 7A As shown, the normal threshold and normal misuse box are effective for identifying high-speed rigid obstacle impact events; that is, the metric is triggered in a timely manner, as indicated by the asterisks. However, for high-speed pole impacts and high-speed offset / angle / asymmetric impacts, the normal threshold and normal misuse box are ineffective, and the metric is triggered too late, as indicated by the asterisks.

[0111] To solve this problem, Figure 7A and Figure 7B The conventional frontal collision detection scheme implements CZS switching metric 352 ( Figure 7B To switch on collision discrimination metric 350 ( Figure 7A The collision threshold and misuse box implemented in ) . For example Figure 7B As shown, CZS switching metric 352 uses the metrics of evaluating CZS_3X_AMA / CZS_4X_AMA and X_REL to determine whether to switch to collision discrimination metric 350. Figure 7A The thresholds and misused boxes implemented in the system are switched from normal thresholds and misused boxes to the switched thresholds and misused boxes. For example... Figure 7B As shown, the threshold is configured to avoid being triggered by misuse events and low-magnitude events such as low-velocity rigid obstacle impacts (e.g., 10 to 16 kph, zero-bias rigid obstacle impacts)

[0112] CZS switching metric 352 indicates CZS switching when this metric exceeds a threshold (in... Figure 7B (Indicated by an asterisk). For events that trigger too late in the collision discrimination metric 350 (i.e., high-speed pole impacts and offset / angle / asymmetric deformable obstacle impacts), the CZS switching metric 352 indicates an earlier switching in time. As a result, CZS switching can be used to switch the thresholds and misused boxes implemented in the collision discrimination metric 350 from normal thresholds and misused boxes to switched thresholds and misused boxes. Figure 7A As shown, the threshold will be triggered promptly upon impact with high-speed poles and upon impact with offset / angle / asymmetric deformable obstacles after switching.

[0113] Active switching

[0114] exist Figure 8A and Figure 8B The diagram illustrates the positive alignment algorithm 210. The positive alignment algorithm 210 implements the conventional post-switch collision discrimination algorithm described above. Advantageously, in addition to the aforementioned CZS switching, the positive alignment algorithm 210 also implements active threshold switching in response to a preset algorithm 170. More specifically, the positive alignment algorithm 170 implements active threshold switching in response to a preset severity flag 192 and an object type flag 202 to adjust the collision discrimination metric and misused bounding box for the impending collision with the identified object and the expected severity of the collision.

[0115] Figure 8A and Figure 8B A frontal collision discrimination scheme or method implemented by frontal algorithm 210 is demonstrated. Frontal algorithm 210 implements active threshold switching in the collision discrimination metric used to detect the occurrence of a frontal collision. The collision discrimination metric utilizes... Figure 6 The modified collision signals V_REL, X_REL, CZS_3X_AMA, and CZS_4X_AMA are determined to identify various frontal collision types. Figure 8A The ACU_X collision discrimination metric 360 is demonstrated. This collision discrimination metric uses only ACU_X, that is, it uses the metric that evaluates V_REL and X_REL to determine whether a frontal collision has occurred. Figure 8B CZS switching metrics 390 are shown; these switching metrics are used to switch the collision threshold and misused boxes implemented in collision discrimination metric 350. For example... Figure 8A and Figure 8B As shown, metrics 360 and 390 actively switch in response to preset severity flag 192 and object type 202.

[0116] It needs to be reiterated here that... Figure 8A and Figure 8B The thresholds and misuse boxes shown are for illustrative purposes only and are examples only. Those skilled in the art will understand that the characteristics of the thresholds and misuse boxes (e.g., shape, limitations, range, number, etc.) can vary widely depending on various factors, such as the specific vehicle platform on which the vehicle safety system 10 is implemented and the safety standards (e.g., NHTSA) that the vehicle safety system is designed to meet.

[0117] According to the present invention, the object type flag 202 is operable to be based on the object recognition algorithm 200 (see [reference]). Figure 5The identified object type is used to switch collision discrimination and switching metrics 360 and 390. Object type flag 202 is operable to select metrics 360 and 390 implemented by the frontal algorithm. In other words, the frontal algorithm 210 can implement multiple versions of the ACU_X collision discrimination metric 360 and CZS switching metric 390 associated with one of the object types identified by object type flag 202. (See reference) Figure 5 These object types can be, for example, cars, trucks, obstacles, poles, or others. Therefore, based on the object type flags, the positive algorithm 210 can select metrics 360, 390, and all associated thresholds based on the flagged object type.

[0118] refer to Figure 8A The ACU_X collision discrimination metric 360 implements a Level 1 / pretensioner normal threshold 362 and a post-switching threshold 364. The ACU_X collision discrimination metric 360 also includes a normal misuse box 374 and a post-switching misuse box 376. These normal / post-switching thresholds and misuse boxes correspond to the above references. Figure 7A The described thresholds and misused boxes. Whether to use normal thresholds and misused boxes or post-switch thresholds and misused boxes depends on the CZS switching metric 390 ( Figure 8B In particular, whether the CZS switching threshold of 392 has been exceeded. This corresponds to the above reference. Figure 7B The described CZS switching threshold. When the CZS switching threshold 392 is exceeded, the collision discrimination metric 360 uses the post-switching threshold 364 and the post-switching misused box 376.

[0119] It should be noted here that, Figure 8B The CZS switching metric 390 shown actually represents two metrics—one using CZS_3X_AMA and the other using CZS_4X_AMA, as indicated by the vertical axis of the metrics. Either metric can result in a CZS switching in the described ACU_X collision discrimination metric 360.

[0120] According to the present invention, in response to a preset severity flag 192, the thresholds implemented in the ACU_X collision discrimination metric 360 and the CZS-switched metric 390 can also be switched to preset thresholds and corresponding misused boxes. Which preset thresholds and misused boxes are switched to in metrics 360 and 390 depends on the severity estimation algorithm 190 (see [link to relevant documentation]). Figure 5 The preset severity flag is set at 192. After switching, the threshold and misuse box are... Figure 8A and Figure 8B The middle is indicated by a dotted line. (See reference.) Figure 8AThe ACU_X collision discrimination metric 360 includes a Level 1 / pretensioner normal preset threshold 366 and a corresponding normal preset misuse box 378, as well as a switched preset threshold 368 and a corresponding switched preset misuse box 380. Similarly, the CZS switching metric 390 includes a CZS switching preset threshold 394.

[0121] The thresholds implemented in the ACU_X collision discrimination metric 360 and the CZS switching metric 390 are either preset thresholds 366, 368, 394, or preset misuse boxes 378, 380, depending on the preset severity flag 192. The preset severity flag can be configured to indicate two or more severity levels. Figure 5 In the example configuration shown, there can be four severity levels: 0 = no action, 1 = pretensioner only, 2 = pretensioner and inflation device level 1, and 3 = pretensioner and inflation device levels 1 and 2. Metrics 360 and 390 can be individually configured to switch to a preset threshold and misuse box in response to any severity level indicated in preset severity flag 192.

[0122] For example, referring to ACU_X collision discrimination metric 360, the switched threshold 364 can be switched to the switched preset threshold 368 and the switched preset misuse box 380 in response to a preset severity flag ≥1. The Level 1 / Pretensioner normal threshold 362 can be switched to the Level 1 / Pretensioner normal preset threshold 366 and the normal preset misuse box 378 in response to a preset severity flag ≥1. Referring to CZS switching metric 390, the CZS switching threshold 392 can be switched to the CZS switching preset threshold 394 in response to a preset severity flag ≥1. Figure 8A and Figure 8B As shown, the threshold after switching can be smaller or larger than its corresponding normal threshold, although a decrease in threshold size is more likely after switching. Similarly, the misused box after switching can be larger or smaller than its corresponding normal misused box.

[0123] Based on the above, it should be understood that the frontal algorithm 210 can not only implement CZS switching based on passive sensing to help consider different frontal collision types, but also implement active switching based on active sensing to consider object type and estimate collision severity.

[0124] Reference Figure 8AThe ACU_X collision discrimination metric 360 may also include a second-level threshold 370, which is for a safety system where the airbag inflator is a two-stage inflator. The second stage of the airbag inflator is for more severe collisions, indicating that the magnitude of the second-level threshold 370 is relatively high. In this configuration, a first-level / pretensioner normal threshold 362 is implemented to trigger the pretensioner and the first stage of the airbag inflator. The second-level threshold 370 is implemented to trigger the second stage of the airbag inflator after a predetermined delay (which could be, for example, 5 ms, 20 ms, 100 ms, etc.). Figure 8A As shown, the ACU_X collision discrimination metric 360 may also include a 2-level preset threshold 372, which can be switched to in response to a preset severity flag being a predetermined value such as ≥2, thus reducing the threshold for triggering the 2nd level in response to a flag indicating a higher collision severity level.

[0125] Although not shown, the ACU_X collision discrimination metric 360 can implement misuse boxes corresponding to the Level 2 threshold 370 and the Level 2 preset threshold 372. However, these misuse boxes may not be necessary due to the higher magnitude required to trigger Level 2 events.

[0126] Alternatively, refer to Figure 8B The CZS switching metric 390 may also include a CZS specific threshold 396, which is for safety systems where the airbag inflator is a two-stage inflator. In this configuration, the CZS specific threshold 396 can be implemented to adjust or regulate the trigger delay between the first and second stages of the inflator. The delay can be increased or decreased in response to exceeding the CZS specific threshold 396. Figure 8B As shown, the CZS switching metric 390 may also include a CZS special preset threshold 398, which can be switched to in response to a preset severity flag being a predetermined value such as ≥2, thus reducing the threshold used to adjust the level 2 trigger delay in response to a flag indicating a higher collision severity level.

[0127] From the above description of the invention, those skilled in the art will recognize improvements, changes, and modifications. The appended claims are intended to cover such improvements, changes, and / or modifications that fall within the scope of the art.

Claims

1. A vehicle safety system for assisting in protecting vehicle occupants in the event of a frontal collision, the vehicle safety system comprising: Controller; One or more collision sensors, the one or more collision sensors being used to sense frontal impacts; as well as An active sensor, the active sensor being used to detect objects in the path of the vehicle; The controller is configured to implement a collision discrimination metric to detect the occurrence of a frontal collision in response to a signal received from the collision sensor. The collision discrimination metric implements a threshold for determining whether the signal received from the collision sensor indicates that a frontal collision has occurred. The controller is configured to implement an algorithm that uses information obtained from the active sensors to detect objects in the vehicle's path, and selects a threshold to be applied in the collision discrimination metric in response to the detection of the object. The algorithm implemented by the controller is further configured to select misused boxes based on information obtained from the active sensors, the misused boxes being associated with a selected threshold implemented by the collision discrimination metric.

2. The vehicle safety system as claimed in claim 1, wherein, The algorithm implemented by the controller is further configured to: Determine the object type of the object; Determine the estimated severity of the impact with the object; and The threshold implemented in the collision discrimination metric is further selected in response to at least one of the object type and the estimated severity.

3. The vehicle safety system as described in claim 2, wherein, The object types include one of the following: car, truck, obstacle, and pole.

4. The vehicle safety system as described in claim 2, wherein, The algorithm implemented by the controller is configured to determine the estimated severity by implementing a metric based on the relative speed between the object and the vehicle.

5. The vehicle safety system of claim 4, further comprising at least one actuable safety device, said safety device including an airbag with a two-stage inflation mechanism and a seatbelt pretensioner, wherein, The algorithm implemented by the controller is configured to determine one of the following actions to be taken in response to the estimated severity: Neither the seatbelt pretensioner nor the inflation device will be activated; Only the seatbelt pretensioner is actuated; Actuate the first stage of the seatbelt pretensioner and inflation device; Actuate the seat belt pretensioner, the first stage of the inflation device, and the second stage of the inflation device.

6. The vehicle safety system as claimed in claim 2, wherein, The algorithm implemented by the controller is further configured to select a misused box in response to at least one of the object type and the estimated severity, the misused box being associated with a selected threshold implemented by the collision discrimination metric.

7. The vehicle safety system as claimed in claim 2, wherein, The algorithm implemented by the controller is further configured to determine the estimated severity by evaluating an estimated severity discriminant metric that compares the relative velocity and displacement of the object relative to the vehicle.

8. The vehicle safety system as described in claim 1: in, The collision sensor includes a front compression zone sensor CZS; The collision discrimination metric includes a CZS switching discrimination metric used to evaluate CZS acceleration values ​​to determine whether the CZS switching threshold is exceeded. The collision discrimination metric includes a separate metric associated with each object type determined by the algorithm. Each separate metric includes a normal threshold and a misused bounding box, as well as a CZS switching threshold and a misused bounding box. When the CZS switching discrimination metric determines that the CZS switching threshold is exceeded, the collision discrimination metric implements the CZS switching threshold and the misused bounding box; otherwise, it implements the normal threshold and the misused bounding box. The collision discrimination metric further includes a normal preset threshold and a misused bounding box, as well as a preset threshold after CZS switching. When 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 collision discrimination metric implements the preset threshold after CZS switching and the misused bounding box. When the CZS switching threshold is not exceeded and the algorithm detects the object in the vehicle's path, the normal preset threshold and the misused bounding box are implemented.

9. The vehicle safety system as claimed in claim 1, wherein, The algorithm implemented by the controller is configured to detect objects in the vehicle's path in the following manner: Identify the object; Determine whether the lateral position of the object relative to the vehicle is within a predetermined threshold. The Time-of-Collision (TTC) discriminant metric is used to determine whether the relative velocity between the object and the vehicle exceeds a predetermined threshold indicating that a collision is imminent. as well as The longitudinal distance collision discrimination metric is evaluated to determine whether the longitudinal position of the object relative to the vehicle exceeds a predetermined threshold indicating that a collision is imminent.

10. The vehicle safety system as claimed in claim 1, wherein, The algorithm implemented by the controller is configured to detect objects in the vehicle's path in the following manner: Identify the state of the object; Determine whether the calculated collision probability is greater than the predetermined collision probability; as well as The Time-of-Impact (TTC) discriminant metric is evaluated to determine whether the relative velocity between the object and the vehicle exceeds a predetermined threshold indicating that a collision is imminent.

11. The vehicle safety system as claimed in claim 1, wherein, The collision sensor is a component of a passive safety system, which further includes at least one actuable safety device configured to respond to a vehicle collision by actuating the safety device, and wherein the active sensor is a component of an active safety system configured to predict the occurrence of the vehicle collision.

12. The vehicle safety system as claimed in claim 1, wherein, The collision sensor includes at least one of a crush zone sensor and an airbag controller unit (ACU) sensor, wherein the sensor is in the form of an accelerometer for measuring vehicle acceleration along the longitudinal axis of the vehicle.

13. The vehicle safety system of claim 12, wherein, The collision discrimination metric has a value determined by comparing acceleration with displacement, or velocity with displacement, as measured by the collision sensor, and the collision discrimination metric determines that a frontal collision has occurred in response to the value exceeding the threshold.

14. The vehicle safety system as claimed in claim 2, wherein, The active sensor includes a camera, and the information obtained from the active sensor includes the object type, the object's lateral position, the time of impact (TTC) with the object, the relative velocity of the object to the vehicle, and the object's longitudinal position relative to the vehicle.

15. The vehicle safety system as claimed in claim 14, wherein, The algorithm for detecting objects in the path of the vehicle is configured to detect the objects in the following manner: Determine whether the object type is an identified object type; Determine whether the lateral position of the object is within a predetermined threshold range; The impact time-to-critical (TTC) collision discrimination metric is used to determine whether the TTC exceeds a predetermined threshold, thereby determining whether the object's TTC is within a threshold indicating an impending collision, the threshold being determined with respect to the relative speed between the object and the vehicle. as well as The longitudinal distance between the object and the vehicle is determined by evaluating a longitudinal distance collision discrimination metric to determine whether the longitudinal distance between the object and the vehicle exceeds a predetermined threshold, thereby determining whether the longitudinal position of the object relative to the vehicle is within a threshold indicating an impending collision, the threshold being determined with respect to the relative velocity between the object and the vehicle.

16. The vehicle safety system of claim 15, wherein, The identified object types include cars, trucks, obstacles, or poles.

17. The vehicle safety system as claimed in claim 1, wherein, The active sensor includes a radar sensor, and the information obtained from the active sensor includes the object state, the probability of collision, the time to collision (TTC) with the object, and the relative speed between the object and the vehicle.

18. The vehicle safety system of claim 17, wherein, The algorithm for detecting objects in the vehicle path is configured to detect the objects in the following manner: Determine whether the object state is an identified object state; Determine whether the collision probability is greater than a predetermined threshold probability; as well as The impact time-to-critical (TTC) collision discrimination metric is used to determine whether the TTC exceeds a predetermined threshold, thereby determining whether the object's TTC is within a threshold indicating an impending collision, the threshold being determined with respect to the relative speed between the object and the vehicle.

19. The vehicle safety system of claim 18, wherein, The identified object states include forward state, backward state, lateral state, stationary state, or moving state.

20. The vehicle safety system as claimed in claim 1, wherein, The active sensor includes at least one of a camera and a radar sensor.

21. The vehicle safety system as claimed in claim 1, wherein, The controller includes an airbag controller unit (ACU).

Images