Abstract: Methods and vehicles are provided for identifying objects in proximity to the vehicle and controlling active safety functionality for the vehicle. A target object in proximity to the vehicle is detected. A movement of the target object is measured. The target object is classified based at least in part on the movement. The active safety functionality is controlled based at least in part on the classification of the target object.

Abstract: Methods, systems, and vehicles are provided for localizing a vehicle. A sensor is configured to detect an object disposed in a generally upward direction from the vehicle while the vehicle is travelling. A processor is coupled to the sensor. The processor is configured to correlate the object with information from a map database, thereby generating a correlation, and determine a geographic location of the vehicle based on the correlation.

Abstract: Methods and systems are provided for assessing a target proximate a vehicle. A location and a velocity of the target are obtained. The location and the velocity of the target are mapped onto a polar coordinate system via a processor. A likelihood that the vehicle and the target will collide is determined using the mapping.

Abstract: A vehicle system and method that can determine object sensor misalignment while a host vehicle is being driven, and can do so without requiring multiple sensors with overlapping fields-of-view. In an exemplary embodiment where the host vehicle is traveling in generally a straight line, the present method uses an object sensor to track the path of a stationary object as it moves through the sensor's field-of-view and compares the sensed object path to an expected object pat*h. If the sensed and expected paths of the stationary object deviate by more than some amount, then the method determines that the object sensor is skewed or otherwise misaligned.

Abstract: A method of associating targets from at least two object detection systems. An initial prior correspondence matrix is generated based on prior target data from a first object detection system and a second object detection system. Targets are identified in a first field-of-view of the first object detection system based on a current time step. Targets are identified in a second field-of-view of the second object detection system based on the current time step. The prior correspondence matrix is adjusted based on respective targets entering and leaving the respective fields-of-view. A posterior correspondence matrix is generated as a function of the adjusted prior correspondence matrix. A correspondence is identified in the posterior correspondence matrix between a respective target of the first object detection system and a respective target of the second object detection system.

Abstract: A method for selecting an anchor lane for tracking in a vehicle lane tracking system. Digital map data and leading vehicle trajectory data are used to predict lane information ahead of a vehicle. Left and right lane boundary markers are also detected, where available, using a vision system. The lane marker data from the vision system is combined with the lane information from the digital map data and the leading vehicle trajectory data in a lane curvature fusion calculation. The left and right lane marker data from the vision system are also evaluated for conditions such as parallelism and sudden jumps in offsets, while considering the presence of entrance or exit lanes as indicated by the map data. An anchor lane for tracking is selected based on the evaluation of the vision system data, using either the fused curvature calculation or the digital map and leading vehicle trajectory data.

Abstract: A method for selecting an anchor lane for tracking in a vehicle lane tracking system. Digital map data and leading vehicle trajectory data are used to predict lane information ahead of a vehicle. Left and right lane boundary markers are also detected, where available, using a vision system. The lane marker data from the vision system is combined with the lane information from the digital map data and the leading vehicle trajectory data in a lane curvature fusion calculation. The left and right lane marker data from the vision system are also evaluated for conditions such as parallelism and sudden jumps in offsets, while considering the presence of entrance or exit lanes as indicated by the map data. An anchor lane for tracking is selected based on the evaluation of the vision system data, using either the fused curvature calculation or the digital map and leading vehicle trajectory data.

Abstract: A method of associating targets from at least two object detection systems. An initial prior correspondence matrix is generated based on prior target data from a first object detection system and a second object detection system. Targets are identified in a first field-of-view of the first object detection system based on a current time step. Targets are identified in a second field-of-view of the second object detection system based on the current time step. The prior correspondence matrix is adjusted based on respective targets entering and leaving the respective fields-of-view. A posterior correspondence matrix is generated as a function of the adjusted prior correspondence matrix. A correspondence is identified in the posterior correspondence matrix between a respective target of the first object detection system and a respective target of the second object detection system.

Abstract: A vehicle system and method that can determine object sensor misalignment while a host vehicle is being driven, and can do so without requiring multiple sensors with overlapping fields-of-view. In an exemplary embodiment where the host vehicle is traveling in generally a straight line, the present method uses an object sensor to track the path of a stationary object as it moves through the sensor's field-of-view and compares the sensed object path to an expected object path. If the sensed and expected paths of the stationary object deviate by more than some amount, then the method determines that the object sensor is skewed or otherwise misaligned.

Abstract: Methods, systems, and vehicles are provided for localizing a vehicle. A sensor is configured to detect an object disposed in a generally upward direction from the vehicle while the vehicle is travelling. A processor is coupled to the sensor. The processor is configured to correlate the object with information from a map database, thereby generating a correlation, and determine a geographic location of the vehicle based on the correlation.

Abstract: Methods and vehicles are provided for identifying objects in proximity to the vehicle and controlling active safety functionality for the vehicle. A target object in proximity to the vehicle is detected. A movement of the target object is measured. The target object is classified based at least in part on the movement. The active safety functionality is controlled based at least in part on the classification of the target object.

Abstract: Methods and systems are provided for assessing a target proximate a vehicle. A location and a velocity of the target are obtained. The location and the velocity of the target are mapped onto a polar coordinate system via a processor. A likelihood that the vehicle and the target will collide is determined using the mapping.