A system and method for alerting a driver of a motor vehicle or a person walking along a road or hiking on a trail of potentially dangerous hazards in their path. Hazards may be deep water, ice, oil slicks or other hazards. In the case of a motor vehicle, the system uses cameras mounted on or within the vehicle to detect potential hazards and then analyzes the images combined with the known topography of the location to evaluate the ability of the vehicle to safely traverse the hazard. In the case of a person walking or hiking, the person may use the camera on a personal mobile device to capture images of the hazard and to combine the images with the known topography at the location to evaluate the danger presented by the hazard.

A small-object perception system, for use in a vehicle, includes a stereo vision system that captures stereo images and outputs information identifying an object having a dimension in a range of ˜20 cm to about ˜100 cm in a perception range of ˜3 meters to ˜150 meters from the vehicle, and a system controller configured to receive output signals from the stereo vision system and to provide control signals to control a path of movement of the vehicle. The stereo vision system includes cameras separated by a baseline of ˜1 meter to ˜4 meters. The stereo vision system includes a stereo matching module configured to perform stereo matching on left and right initial images and to output a final disparity map based on a plurality of preliminary disparity maps generated from the left and right initial images, with the preliminary disparity maps having different resolutions from each other.

In a driving assistance apparatus, an image acquiring unit acquires a captured image captured by an onboard camera. Based on the captured image acquired by the image acquiring unit, a boundary line recognizing unit recognizes a boundary line that demarcates a traffic lane in which an own vehicle is driving. A road information acquiring unit acquires road information related to a road on which the own vehicle is driving. Based on the road information acquired by the road information acquiring unit, a degree-of-reliability setting unit sets a degree of reliability of the boundary line recognized by the boundary line recognizing unit. Based on the boundary line recognized by the boundary line recognizing unit, a driving assisting unit performs driving assistance of the own vehicle and varies control content of the driving assistance based on the degree of reliability.

A system and method operate an autonomous vehicle. A sensor senses a road and an object. A processor determines, in a Cartesian reference frame, a representation of the road and a source point representative of the object, samples a first waypoint and a second waypoint from the representation of the road, determines a linear projection of the source point to a line connecting the first waypoint and the second waypoint, determines a first estimate of a longitudinal component of the source point in a road-based reference frame based on the linear projection, the first estimate being on a curve representing the road between the first waypoint and the second waypoint, determines a second estimate of the longitudinal component from the first estimate, determines a coordinate of the source point in the road-based reference frame from the second estimate and operates the vehicle with respect to the object using the coordinate.

There is provided an automatic correction method for an onboard camera and an onboard camera device. The automatic correction method includes the following steps: obtaining a lane image with the onboard camera and a current extrinsic parameter matrix, and identifying two lane lines in the lane image; converting the lane image into a top-view lane image, and obtaining two projected lane lines in the top-view lane image for the two lane lines; calculating a plurality of correction parameter matrices corresponding to the current extrinsic parameter matrix according to the two projected lane lines; and correcting the current extrinsic parameter matrix according to the plurality of correction parameter matrices. This can be applied in situations where the vehicle is stationary or travelling for automatic correction on the extrinsic parameter matrix of the onboard camera.

Systems and methods for updating navigational maps based using at least one sensor are provided. In one aspect, a control system for an autonomous vehicle, includes a processor and a computer-readable memory configured to cause the processor to: receive output from at least one sensor located on the autonomous vehicle indicative of a driving environment of the autonomous vehicle, retrieve a navigational map used for driving the autonomous vehicle, and detect one or more inconsistencies between the output of the at least one sensor and the navigational map. The computer-readable memory is further configured to cause the processor to: in response to detecting the one or more inconsistencies, trigger mapping of the driving environment based on the output of the at least one sensor, update the navigational map based on the mapped driving environment, and drive the autonomous vehicle using the updated navigational map.

An approach is provided for pattern-based map matching of a probe trace to a digital map. The approach involves querying the digital map for a set of road links within a threshold distance of a probe point. The approach also involves determining a match starting point for each road link of a set of road links. The approach further involves selecting a sampled probe point from the probe trace. The approach also involves generating one or more patterns for said each road link based on the match starting point, the sampled probe point, the topology polyline, or a combination thereof. The approach further involves selecting a matched road link from among the set of road links based on the one or more patterns. The probe point is then snapped to the matched road link.

Systems and methods for navigating a host vehicle. The system may perform operations including receiving, from an image capture device, at least one image representative of an environment of the host vehicle; analyzing the at least one image to identify an object in the environment of the host vehicle; determining a location of the host vehicle; receiving map information associated with the determined location of the host vehicle, wherein the map information includes elevation information associated with the environment of the host vehicle; determining a distance from the host vehicle to the object based on at least the elevation information; and determining a navigational action for the host vehicle based on the determined distance.

Provided are a lane separation line detection correcting device/method and an automatic driving system for stabilizing the behavior of a vehicle by correcting overestimated curvature information resulting from an erroneous detection of a curvature of a lane separation line. A travel speed detecting circuit detects, for example, a target travel speed as vehicle sensor information. A maximum curvature estimating circuit estimates, based on the target travel speed, a maximum curvature of a road along which an own vehicle is traveling. A curvature correcting circuit corrects a curvature of a lane separation line input thereto based on the maximum curvature. A control unit controls steering of the own vehicle based on the lane separation line having a corrected curvature. As a result, vehicle steering can be automatically controlled so as to prevent the own vehicle while traveling from departing from a driving lane.

A system and method for large-scale lane marking detection using multimodal sensor data are disclosed. A particular embodiment includes: receiving image data from an image generating device mounted on a vehicle; receiving point cloud data from a distance and intensity measuring device mounted on the vehicle; fusing the image data and the point cloud data to produce a set of lane marking points in three-dimensional (3D) space that correlate to the image data and the point cloud data; and generating a lane marking map from the set of lane marking points.