Abstract: The disclosed system identifies the images of particular objects or organisms (“segmented image” or “target image”) from images that include the segmented image and the surrounding area (collectively, the “ambient image”). Instead of attempting to merely segment the target image from the ambient image, the system purposely “over-segments” the ambient image into various image regions. Those image regions are then selectively combined into the segmented image using a predefined heuristic that incorporates logic relating to the particular context of the processed image. In some embodiments, different combinations of image regions are evaluated on the basis of probability-weighted classifications.

Abstract: Improved methods and apparatus for classifying occupancy of a position use wavelet transforms, such as Gabor filters, for processing images obtained in conjunction therewith. For example, a computer system comprises an algorithm that utilizes a wavelet transform for processing of imagery associated with a position in order to classify occupancy of that position. A method comprises steps of: obtaining an image of the position; optionally segmenting the image at the position; optionally dividing the image into multiple key regions for further analysis; analyzing texture of the image using one or more wavelet transforms; and classifying occupancy of the position based on the texture of the image.

Abstract: The disclosure describes systems and methods that pertain to interactions between a vehicle and an occupant within the vehicle. More specifically, various systems and methods for enhancing the decisions of automated vehicle applications (collectively “decision enhancement system”) are disclosed. In a safety restraint embodiment, a sensor is used to capture various sensor readings. Sensor readings are typically in the form of images. Occupant information, such as location attributes, motion attributes, and occupant category attributes can be obtained from the sensor readings. Such information can then used by the system to enhance the decisions made by various automated applications.

Abstract: The disclosure describes systems and methods that pertain to interactions between a vehicle and an occupant within the vehicle. More specifically, various systems and methods for enhancing the decisions of automated vehicle applications (collectively “decision enhancement system”) are disclosed. In a safety restraint embodiment, a sensor is used to capture various sensor readings. Sensor readings are typically in the form of images. Occupant information, such as location attributes, motion attributes, and occupant category attributes can be obtained from the sensor readings. If the system concludes that the deployment of a safety restraint is potentially justified, an at-risk-zone detector can be used to determine whether or not the occupant will be too close to the deploying safety restraint at the time of deployment for the safety restraint to be safely deployed.

Abstract: An invention is disclosed to divide a segmented image of an occupant into an upper torso image and a lower torso image. An occupant labeling heuristic can identify each pixel within the segmented image as an upper torso pixel or a lower torso pixel. A k-means module can provide an initial pixel classification by comparing the distance between the particular pixel and an estimated midpoint on the upper torso with the distance between the particular pixel and an estimated midpoint on the lower torso. The iterative parameters estimator can update the mean values for the upper torso and lower torso by performing a conditional likelihood heuristic. Pixels can then be classified as either upper or lower torso pixels by comparing a Mahalonobis distance for each torso. Airbag-related applications can then use the upper torso image to generate occupant characteristics relevant to airbag-related applications.

Abstract: The disclosure describes systems and methods that pertain to interactions between a vehicle and an occupant within the vehicle. More specifically, various systems and methods for enhancing the decisions of automated vehicle applications (collectively “decision enhancement system”) are disclosed. In a safety restraint embodiment, a sensor is used to capture various sensor readings. Sensor readings are typically in the form of images. Occupant information, such as location attributes, motion attributes, and occupant category attributes can be obtained from the sensor readings. If the system concludes that the deployment of a safety restraint is potentially justified, an at-risk-zone detector can be used to determine whether or not the occupant will be too close to the deploying safety restraint at the time of deployment for the safety restraint to be safely deployed.

Abstract: A segmentation system is disclosed that allows a segmented image of a vehicle occupant to be identified within an overall image (the “ambient image”) of the area that includes the image of the occupant. The segmented image from a past sensor measurement within can help determine a region of interest within the most recently captured ambient image. To further reduce processing time, the system can be configured to assume that the bottom of segmented image does not move. Differences between the various ambient images captured by the sensor can be used to identify movement by the occupant, and thus the boundary of the segmented image. A template image is then fitted to the boundary of the segmented image for an entire range of predetermined angles. The validity of each fit within the range of angles can be evaluated. The template image can also be modified for future ambient images.

Abstract: An invention is disclosed to divide a segmented image of an occupant into an upper torso image and a lower torso image. An occupant labeling heuristic can identify each pixel within the segmented image as an upper torso pixel or a lower torso pixel. A k-means module can provide an initial pixel classification by comparing the distance between the particular pixel and an estimated midpoint on the upper torso with the distance between the particular pixel and an estimated midpoint on the lower torso. The iterative parameters estimator can update the mean values for the upper torso and lower torso by performing a conditional likelihood heuristic. Pixels can then be classified as either upper or lower torso pixels by comparing a Mahalonobis distance for each torso. Airbag-related applications can then use the upper torso image to generate occupant characteristics relevant to airbag-related applications.

Abstract: A segmentation system is disclosed that allows a segmented image of a vehicle occupant to be identified within an overall image (the “ambient image”) of the area that includes the image of the occupant. The segmented image from a past sensor measurement within can help determine a region of interest within the most recently captured ambient image. To further reduce processing time, the system can be configured to assume that the bottom of segmented image does not move. Differences between the various ambient images captured by the sensor can be used to identify movement by the occupant, and thus the boundary of the segmented image. A template image is then fitted to the boundary of the segmented image for an entire range of predetermined angles. The validity of each fit within the range of angles can be evaluated. The template image can also be modified for future ambient images.

Abstract: A neural network radar processor (10) comprises a multilayer perceptron neural network (100.1) comprising an input layer (102), a second layer (122), and at least a third layer (124), wherein each layer has a plurality of nodes (108), and respective subsets of nodes (108) of the second (122) and third (124) layers are interconnected so as to form mutually exclusive subnetworks (120). In-phase and quadrature phase time series from a sampled down-converted FMCW radar signal (19) are applied to the input layer, and the neural network (100) is trained so that the nodes of the output layer (106) are responsive to targets in corresponding range cells, and different subnetworks (120) are responsive to respectively different non-overlapping sets of target ranges. The neural network is trained with signals that are germane to an FMCW radar, including a wide range of target scenarios as well as leakage signals, DC bias signals, and background clutter signals.

Abstract: An occupant discrimination system incorporates a position sensor for measuring the distance from a fixed structure within the vehicle to a first surface of an occupant or object on a vehicle seat, and a seat weight sensor for measuring the weight of the occupant or object. The position sensor generates a plurality of signal components that are combined with the weight and vehicle speed related measurements so as to form a signal space, from which a plurality of measures are calculated. A plurality of seat occupancy scenarios are defined and the likelihood of each seat occupancy scenario is functionally related to the plurality of measures. For a given measure state, the likelihood of each of the seat occupancy scenarios is calculated, and the most likely scenario is used to govern the control of a safety restraint system.

Abstract: A method and system (10) for detecting vehicle occupant type and position utilizes a single camera unit (12) positioned, for example at the driver or passenger side A-pillar, to generate image data of the front seating area of the vehicle. The present invention distinguishes between objects, forwardly or rearwardly facing child seats, and occupants, by periodically mapping the image taken of the interior of the vehicle into an image profile (104), and utilizing image profile matching with stored profile data (110) to determine the occupant or object type. The system and method of the present invention track occupant type and position in both parallel and perpendicular directions relative to a fixed structure such as the vehicle instrument panel to optimize both the efficiency and safety in controlling deployment of a occupant safety device, such as an air bag (28).

Abstract: A relatively narrow beam of either RF or optical electromagnetic radiation is scanned over a relatively wide azimuthal range. The return signal is processed to detect the range and velocity of each point of reflection. Individual targets are identified by clustering analysis and are tracked in a Cartesian coordinate system using a Kalman filter. The threat to the vehicle for a given target is assessed from estimates of the relative distance, velocity, and size of each target, and one or more vehicular devices are controlled responsive to the assessment of threat so as to enhance the safety of the vehicle occupant. In a preferred embodiment, a quantized linear frequency modulated continuous wave RF signal is transmitted from and received by a multi-beam antenna having an aziumthal range of at least +/-100 degrees and an individual beam width of approximately 10 degrees.

Abstract: An automotive radar incorporates a repetitive randomized equivalent LFM sequence of frequencies for improved immunity to jamming from other automotive radars. Each frequency in the sequence is of sufficient duration to travel round trip over the detection range of the radar. The Doppler shift in the received signal is estimated by performing a spectral analysis on similar frequency components of the received signal, and is then removed from the entire received signal. The received signal is then reordered so as to form an equivalent LFM received signal, and is compared with a similarly reordered image of the transmitted signal so as to estimate the range to the target. The randomization sequence, initial start frequency, or initial start time of the repetitive sequence are varied to minimize the effects of jamming by other radars, and this variation can be directionally dependent.

Abstract: A method and system (10) for detecting vehicle occupant type and position utilizes a single camera unit (12) positioned, for example at the driver or passenger side A-pillar, to generate image data of the front seating area of the vehicle. The present invention distinguishes between objects, forwardly or rearwardly facing child seats, and occupants, by periodically mapping the image taken of the interior of the vehicle into an image profile (104), and utilizing image profile matching with stored profile data (110) to determine the occupant or object type. The system and method of the present invention track occupant type and position in both parallel and perpendicular directions relative to a fixed structure such as the vehicle instrument panel to optimize both the efficiency and safety in controlling deployment of a occupant safety device, such as an air bag (28).

Abstract: A vehicle collision warning system converts collision threat messages from a predictive collision sensor into intuitive sounds which are perceived by the occupant to be directed from the direction of the potential collision. The type and volume of the sounds are dependent upon the estimated likelihood, severity, and commencement time of the collision. The types of sound are chosen to evoke the proper corrective action by the driver as necessary to avoid the collision or mitigate the effects thereof. Examples of the sounds include a horn, screeching tires, a siren, sounds of various types of vehicles and object, and voice commands. The sounds are stored monaurally and are converted to directional sounds using known techniques.

Abstract: A leakage calibration and removal system and method estimates the complex in-phase and quadrature phase (I/Q) components of a leakage signal for each beam location in the sampled down-converted radar signal in a radar system (10). In a digital embodiment, the stored leakage calibration signal (264) is subtracted (206) from the sampled radar signal, and the resultant signal is processed (208, 210, 212) to detect targets. A leakage calibration process (250) is activated if a leakage signal test (214) indicates a problem for a sufficient number of consecutive scans (216), wherein for each beam location, M consecutive I/Q waveforms are averaged (252), known targets are removed (254, 256, 258), and the resulting signal is scaled (262) and stored (264) as a new leakage calibration signal if the variance is within acceptable limits (262).

Abstract: An automotive radar incorporates a repetitive randomized equivalent LFM sequence of frequencies for improved immunity to jamming from other automotive radars. Each frequency in the sequence is of sufficient duration to travel round trip over the detection range of the radar. The Doppler shift in the received signal is estimated by performing a spectral analysis on similar frequency components of the received signal, and is then removed from the entire received signal. The received signal is then reordered so as to form an equivalent LFM received signal, and is compared with a similarly reordered image of the transmitted signal so as to estimate the range to the target. The randomization sequence, initial start frequency, or initial start time of the repetitive sequence are varied to minimize the effects of jamming by other radars, and this variation can be directionally dependent.

Abstract: A smart weed recognition and identification system comprises a chlorophyll sensor for detecting green vegetation and memory map means for storing images which contain different forms of green vegetation. The memory maps stored in memory are processed to eliminate the background information and leave a memory map containing only green vegetation. The enhanced memory map is further processed by an operation of segmentation into identifiable regions and the identifiable green vegetation regions are processed to identify unique attributes for each of the regions.

Abstract: A pseudo-code representation and a C language representation of a scan converter system whereby radar amplitude data specified in polar coordinates may be displayed on a computer monitor display controlled by rectangular coordinates is provided. The invention utilizes a look-up table that is built using a two-phase algorithm. The look-up table is set into an initial state after which a mapping process takes place in which all of the (x,y) coordinate values covering the display area are inversely projected to the nearest (r,.theta.) coordinate values using trigonometric and approximation procedures. Since more than one (x,y) value may map to the same (r,.theta.) value, these values are linked together to form a patch. All of the (r,.theta.) coordinates will not be hit in this mapping process. Therefore, a second phase of projection occurs. Each (r,.theta.