A forward-facing vision system for a vehicle includes a forward-facing camera disposed in a windshield electronics module attached at a windshield of the vehicle and viewing through the windshield. A control includes a processor that, responsive to processing of captured image data, detects taillights of leading vehicles during nighttime conditions and, responsive to processing of captured image data, detects lane markers on a road being traveled by the vehicle. The control, responsive to lane marker detection and a determination that the vehicle is drifting out of a traffic lane, may control a steering system of the vehicle to mitigate such drifting, with the steering system manually controllable by a driver of the vehicle irrespective of control by the control. The processor, based at least in part on detection of lane markers via processing of captured image data, determines curvature of the road being traveled by the vehicle.

Vision system for vehicle

A vehicular vision system includes a camera having a lens and a CMOS photosensor array having a plurality of photosensor elements. The camera is disposed at an interior portion of a vehicle equipped with the vehicular vision system. The camera one of (i) views exterior of the equipped vehicle through the windshield of the equipped vehicle and forward of the equipped vehicle and (ii) views from the windshield of the equipped vehicle into the interior cabin of the equipped vehicle. A control includes an image processor that processes image data captured by the photosensor array. The image processor processes captured image data to detect an object viewed by the camera. The photosensor array is operable at a plurality of exposure periods and at least one exposure period of the plurality of exposure periods is dynamically variable.

Vehicular vision system

A driver assistance system for a vehicle includes an imaging device having a field of view forward of a vehicle and in a direction of travel of the equipped vehicle, an image processor operable to process image data captured by the imaging device, and a global positioning system operable to determine a geographical location of the vehicle. The equipped vehicle includes an adaptive speed control system for controlling the speed of the equipped vehicle. The adaptive speed control system may reduce the speed of the equipped vehicle responsive at least in part to a detection of a curve in the road ahead of the equipped vehicle via processing by the image processor of image data captured by the imaging device.

Driver assistance system for vehicle

A driver assistance system for a vehicle includes an imager disposed in a housing and a control. The imager includes a CMOS photosensor array of photosensor elements and a lens. With the housing disposed in a vehicle, the imager views forwardly to the exterior of the vehicle through the vehicle windshield at a region of the windshield that is swept by a windshield wiper of the vehicle. The CMOS photosensor array is operable to capture image data. The control includes an image processor disposed in the housing. The driver assistance system identifies objects viewed by the imager via processing by the image processor of captured image data. At least in part responsive to processing of captured image data by the image processor, streetlights present exterior of the vehicle and viewed by imager are discriminated from other objects present exterior the vehicle and viewed by imager.

Driver assistance system for a vehicle

A vision system for a vehicle includes a first imaging sensor having a first forward field of view and a second imaging sensor spaced from the first imaging sensor and having a second forward field of view, which at least partially overlaps with the first forward field of view. A control processes image data captured by at least one of the first and second imaging sensors to determine an object present in the first and/or second forward fields of view. The control is operable to modulate a headlamp of the vehicle responsive to the processing of image data captured by the at least one of the first and second imaging sensors. The control may process image data captured by both of the imaging sensors to determine a distance between the equipped vehicle and an object present in the overlap of the first and second forward fields of view.

Vehicle vision system

An algorithm that discriminates moving objects from their shadows is presented. Starting from the change mask of an image sequence, first of all the changed area is divided into subregions consisting of pixels with similar colour properties. This is done using the mean shift algorithm, which is very powerful in non-parametric clustering of data. In a second step a significance test is performed to classify each image pixel inside the change mask into one of the classes foreground or shadow. To do this a straightforward image model is used where the grey level of a foreground pixel covered by a shadow is given by the product of the corresponding background pixels' grey-level and a constant value. Assuming that fore- and background images are corrupted by Gaussian white noise, a significance test is derived which classifies all pixels inside the change mask. In the third step global and local information from the first and second steps are combined. For each region inside the change mask it is examined if the majority of pixels survived the second step. If this is the case, the whole region is kept for the final moving object mask, if not the region is set to zero.

D. Toth

Papers

Detection of moving shadows using mean shift clustering and a significance test