Abstract: In accordance with one embodiment, a radar system with auto-alignment suitable for use in an automated vehicle is provided. The system includes a radar-sensor, a speed-sensor, and a controller. The radar-sensor is used to detect objects present in a field-of-view proximate to a host-vehicle on which the radar-sensor is mounted. The radar-sensor is operable to determine a measured-range-rate (dRm), a measured-azimuth-angle (Am), and a measured-elevation-angle (Em) to each of at least three objects present in the field-of-view. The speed-sensor is used to determine a measured-speed (Sm) of the host-vehicle. The controller is in communication with the radar-sensor and the speed-sensor.

Abstract: A radar system suitable for an automated vehicle includes a radar sensor and a controller. The radar-sensor is mounted on a host-vehicle. The radar-sensor is operable to detect radar-signals reflected by scattering-points of a target-vehicle located proximate to the host-vehicle. The controller is in communication with the radar-sensor. The controller is configured to determine a present-range-rate, a present-azimuth, and optionally a present-range, of each of the scattering-points at a present-time. The controller is also configured to recall a prior-range-rate, a prior-azimuth, and optionally a prior-range, of each of the scattering-points at a prior-time. The controller is also configured to calculate a yaw-rate of the target-vehicle at the present-time based on the present-range-rate, the present-azimuth, the prior-range-rate, and the prior-azimuth, and optionally the present-range and the prior-range, of each of the scattering-points.

Abstract: A road-model-definition system suitable for an automated-vehicle includes a camera, a lidar-unit, and a controller. The camera used is to provide an image of an area proximate to a host-vehicle. The lidar-unit is used to provide a point-cloud descriptive of the area. The controller is in communication with the camera and the lidar-unit. The controller is configured to determine an image-position of a lane-marking in the image, select ground-points from the point-cloud indicative of a travel-surface, determine coefficients of a three-dimensional (3D) road-model based on the ground-points, and determine a transformation to map the lane-marking in the image onto the travel-surface based on the image-position of a lane-marking and the 3D road-model and thereby obtain a 3D marking-model.

Abstract: In accordance with one embodiment, a radar system with auto-alignment suitable for use in an automated vehicle is provided. The system includes a radar-sensor, a speed-sensor, and a controller. The radar-sensor is used to detect objects present in a field-of-view proximate to a host-vehicle on which the radar-sensor is mounted. The radar-sensor is operable to determine a measured-range-rate (dRm), a measured-azimuth-angle (Am), and a measured-elevation-angle (Em) to each of at least three objects present in the field-of-view. The speed-sensor is used to determine a measured-speed (Sm) of the host-vehicle. The controller is in communication with the radar-sensor and the speed-sensor.

Abstract: A radar system suitable for use on a vehicle and configured to detect a false radar-track arising from a reflection of a radar return from a target includes a first sensor, a second sensor, and a controller. The first sensor outputs a first signal indicative of a first target in a first area proximate to a vehicle. The second sensor outputs a second signal indicative of a second target in a second area proximate to the vehicle and different from the first area. The controller receives the first signal and the second signal. The controller determines that the second target is a reflection of the first target when a reflection-line that bisects and extends orthogonally from a line-segment extending between the first target and the second target intersects with a reflection surface detected by the second sensor.

Abstract: An object tracking system suitable for use on an automated vehicle includes a camera, a radar-sensor and a controller. The controller is configured to assign a vision-identification to each vision-track associated with an instance of an object detected using the camera, and assign a radar-identification to each radar-glob associated with an instance of grouped-tracklets indicated detected using the radar-sensor. The controller is further configured to determine probabilities that a vision-track and a radar-glob indicate the same object. If the combination has a reasonable chance of matching it is includes in a further screening of the data to determine a combination of pairings of each vision-track to a radar-track that has the greatest probability of being the correct combination.

Abstract: A radar system suitable for an automated vehicle includes a radar sensor and a controller. The radar-sensor is mounted on a host-vehicle. The radar-sensor is operable to detect radar-signals reflected by scattering-points of a target-vehicle located proximate to the host-vehicle. The controller is in communication with the radar-sensor. The controller is configured to determine a present-range-rate, a present-azimuth, and optionally a present-range, of each of the scattering-points at a present-time. The controller is also configured to recall a prior-range-rate, a prior-azimuth, and optionally a prior-range, of each of the scattering-points at a prior-time. The controller is also configured to calculate a yaw-rate of the target-vehicle at the present-time based on the present-range-rate, the present-azimuth, the prior-range-rate, and the prior-azimuth, and optionally the present-range and the prior-range, of each of the scattering-points.

Abstract: A system for automated operation of a host-vehicle includes an object-sensor, a global-positioning-system (GPS) receiver, and a controller. The object-sensor is used to determine a first-polynomial indicative of a preferred-steering-path based on an object detected proximate to a host-vehicle. The GPS-receiver is used to determine a second-polynomial indicative of an alternative-steering-path based on a GPS-map. The controller is configured to steer the host-vehicle in accordance with the first-polynomial when the object is detected, and steer the host-vehicle in accordance with the second-polynomial when the object is not detected. The improvement allows the system to make use of a less expensive/less accurate version of the GPS-receiver, and a less complicated GPS-map than would be anticipated as necessary for automated steering of the host-vehicle using only the GPS-receiver and the GPS-map.

Abstract: A radar system suitable for use on a vehicle and configured to detect a false radar-track arising from a reflection of a radar return from a target includes a first sensor, a second sensor, and a controller. The first sensor outputs a first signal indicative of a first target in a first area proximate to a vehicle. The second sensor outputs a second signal indicative of a second target in a second area proximate to the vehicle and different from the first area. The controller receives the first signal and the second signal. The controller determines that the second target is a reflection of the first target when a reflection-line that bisects and extends orthogonally from a line-segment extending between the first target and the second target intersects with a reflection surface detected by the second sensor.

Abstract: An improved probabilistic lane assignment method for detected objects in the scene forward of a host vehicle. Road/lane model parameters, preferably including an angular orientation of the host vehicle in its lane, are estimated from host vehicle sensor systems, taking into account measurement uncertainty in each of the constituent parameters. A probabilistic assignment of the object's lane is then assessed based on the road/lane model parameters and object measurements, again taking into account measurement uncertainty in both the road/lane model and object measurements. According to a first embodiment, the probabilistic assignment is discrete in nature, indicating a confidence or degree-of-belief that the detected object resides in each of a number of lanes. According to a second embodiment, the probabilistic assignment is continuous in nature, providing a lateral separation distance between the host vehicle and the object, and a confidence or degree-of-belief in the lateral separation distance.

Abstract: A method of lane marker detection and detection fitting is provided for lane tracking. A lane marker is modeled and split into left and right steps. A filter response is calculated from a cumulative row sum, and normalized for filter pixel size, lane marker brightness and road brightness. A lane marker response is peak detected for positive and negative peaks and checked for having a magnitude above a threshold and being a local peak in a five point neighborhood. A Hough transform is extended to multiple planes to use lane marker features to determine a best line. Lane marker features include a mean and variance of lane marker brightness, lane marker width, lane marker parallelism to a host vehicle direction of travel, and consistence with a predicted lane marker characteristic. A closest lane marker line to a host vehicle is identified, and refitted to account for any curvature.

Abstract: A collision detection system and method of estimating a crossing location are provided. The system includes a first sensor for sensing an object in a field of view and sensing a first range defined as the distance between the object and the first sensor. The system also includes a second sensor for sensing the object in the field of view and sensing a second range defined by the distance between the object and the second sensor. The system further includes a controller for processing the first and second range measurements and estimating a crossing location of the object as a function of the first and second range measurements. The crossing location is estimated using range and range rate in a W-plane in one embodiment and using a time domain approach in another embodiment.

Abstract: A method for estimating unknown parameters (pan angle (?), instantaneous tilt angle (?) and road geometry of an upcoming road segment) for a vehicle object detection system. The vehicle object detection system is preferably a forward looking, radar-cued vision system having a camera, a radar sensor and an processing unit. The method first estimates the pan angle (?), then corrects the coordinates from a radar track so that pan angle (?) can be treated as zero, and finally solves a least squares problem that determines best estimates for instantaneous tilt angle (?) and road geometry. Estimating these parameters enables the vehicle object detection system to identify, interpret and locate objects in a more accurate and efficient manner.

Abstract: An improved probabilistic lane assignment method for detected objects in the scene forward of a host vehicle. Road/lane model parameters, preferably including an angular orientation of the host vehicle in its lane, are estimated from host vehicle sensor systems, taking into account measurement uncertainty in each of the constituent parameters. A probabilistic assignment of the object's lane is then assessed based on the road/lane model parameters and object measurements, again taking into account measurement uncertainty in both the road/lane model and object measurements. According to a first embodiment, the probabilistic assignment is discrete in nature, indicating a confidence or degree-of-belief that the detected object resides in each of a number of lanes. According to a second embodiment, the probabilistic assignment is continuous in nature, providing a lateral separation distance between the host vehicle and the object, and a confidence or degree-of-belief in the lateral separation distance.

Abstract: A collision detection system and method of estimating a crossing location are provided. The system includes a first sensor for sensing an object in a field of view and sensing a first range defined as the distance between the object and the first sensor. The system also includes a second sensor for sensing the object in the field of view and sensing a second range defined by the distance between the object and the second sensor. The system further includes a controller for processing the first and second range measurements and estimating a crossing location of the object as a function of the first and second range measurements. The crossing location is estimated using range and range rate in a W-plane in one embodiment and using a time domain approach in another embodiment.

Abstract: A method of lane marker detection and lane fitting is provided for lane tracking. A lane marker is modeled and split into left and right steps. A filter response is calculated from a cumulative row sum, and normalized for filter pixel size, lane marker brightness and road brightness. A lane marker response is peak detected for positive and negative peaks and checked for having a magnitude above a threshold and being a local peak in a five point neighborhood. A Hough transform is extended to multiple planes to use lane marker features to determine a best line. Lane marker features include a mean and variance of lane marker brightness, lane marker width, lane marker parallelism to a host vehicle direction of travel, and consistence with a predicted lane marker characteristic. A closest lane marker line to a host vehicle is identified, and refitted to account for any curvature.

Abstract: A collision detection system and method of estimating a crossing location are provided. The system includes a first sensor for sensing an object in a field of view and sensing a first range defined as the distance between the object and the first sensor. The system also includes a second sensor for sensing the object in the field of view and sensing a second range defined by the distance between the object and the second sensor. The system further includes a controller for processing the first and second range measurements and estimating a crossing location of the object as a function of the first and second range measurements. The crossing location is estimated using range and range rate in a W-plane in one embodiment and using a time domain approach in another embodiment.

Abstract: A rollover detection apparatus and method are provided for anticipating a potential vehicle rollover event. The apparatus includes an input for receiving a plurality of input signals including sensed parameters of the vehicle. A first memory buffer stores data representative of one or more predetermined driving scenarios that represent possible rollover scenarios. A second memory buffer stores data representative of a history of recent conditions of the vehicle based on the plurality of sensed vehicle parameters. The apparatus further includes a processor for comparing the data representative of a history of recent driving events to the data representative of one or more predetermined driving scenarios. The processor further determines a possible rollover event of the vehicle based on the comparison and generates an output signal indicative thereof.

Abstract: A collision detection system and method of estimating a miss distance are provided. The collision detection system includes a sensor for sensing an object in a field of view. The sensor measures range and range rate of the object. The collision detection system further includes a controller for estimating a miss distance as a function of the measured range and range rate, without requiring a measured azimuth angle measurement.

Abstract: A tracking system and method of estimating position and velocity of an object are provided. The tracking system includes first and second sensors for sensing an object in first and second fields of view, respectively. The first and second fields of view partially overlap to provide an overlapping coverage zone. Each of the sensors measures range and range rate of the object. The system further includes a controller for estimating position and velocity of the object as a function of the measured range and range rate signals, without requiring sensing of the azimuth angle of the object.