A vehicular driver assistance system includes a front camera module (FCM) disposed at a vehicle. The system, responsive to processing captured image data, generates FCM lane information including information regarding a traffic lane the vehicle is currently traveling along. An e-Horizon module (EHM) generates EHM lane information including information regarding the traffic lane the vehicle is currently traveling along. The vehicular driver assistance system determines an FCM correlation using the FCM lane information and sensor data captured by at least one exterior sensor. The vehicular driver assistance system determines an EHM correlation using the EHM lane information and the sensor data captured by the at least one exterior sensor. Responsive to determining the FCM correlation and the EHM correlation, the system controls lateral movement of the vehicle based on one selected from the group consisting of (i) the FCM lane information and (ii) the EHM lane information.

Disclosed are an apparatus and method for generating a link for each lane when there is a plurality of exit lanes. The apparatus may acquire information about a speed of a probe vehicle from the probe vehicle, and determine a section for generating a divided link along a lane, based on a speed difference of the probe vehicle in an exit direction. Accordingly, it is possible to improve the accuracy and reliability of the speed of each lane.

A method of autonomous vehicle control, comprising: receiving an image of a lenticular human-imperceptible marker embedded in an element of an environment that an autonomous vehicle is moving in, the marker having a pattern usable for determining positional data of the moving vehicle, the image captured using human-invisible light, analyzing the received image of the human-imperceptible marker, and controlling the autonomous vehicle based on the analyzed image of the human-imperceptible marker.

This application is directed to augmenting training data used for vehicle driving modelling. A computer system obtains a first image of a road and identifies a drivable area of the road within the first image. The computer system obtains an image of an object and generates a second image from the first image by overlaying the image of the object over the drivable area. The second image is added to a corpus of training images to be used by a machine learning system to generate a model for facilitating driving of a vehicle (e.g., at least partial autonomously). In some embodiments, the computer system applies machine learning to train a model using the corpus of training images and distributes the model to one or more vehicles. In use, the model processes road images captured by the one or more vehicles to facilitate vehicle driving.

A lane centering system for a vehicle includes a light detection and ranging (LIDAR) system configured to (i) emit light pulses towards raised pavement markers on a road along which the vehicle is traveling and (ii) receive light pulses reflected by the raised pavement markers that collectively form LIDAR point cloud data, and a controller configured to detect a set of lane lines defining one or more lanes on the road based on the LIDAR point cloud data, based on at least the detected set of lane lines.sub.; generate a polynomial curve corresponding to a center of a lane in which the vehicle is traveling, and control steering of the vehicle based on the polynomial curve to keep the vehicle centered within the lane.

A motor-vehicle path-controlling module is arranged to model the path of the vehicle during a change in traffic lane by a Bezier curve relating a value of a parameter to a value of a lateral deviation of the vehicle from the center of a traffic lane and to a value of a time-dependent variable representative of the variation in the change of path; determine a setpoint state vector of a closed feedback loop of a path-controlling device, the loop being designed to control the motor vehicle so that it follows the path modelled by the Bezier curve, the vector being determined on the basis of the lateral deviation, of the time-dependent variable and of the parameter, and transmit the setpoint state vector to the input of the loop.

A driver assistance system for the automated longitudinal guidance of a motor vehicle detects a desired turning maneuver of the driver of the motor vehicle when the vehicle is in motion with automated longitudinal guidance, and reduces the speed of the motor vehicle in accordance with the detected turning maneuver.

A driver assistance system for an ego vehicle, and a method for a driver assistance system is provided. The system is configured to refine a coarse geolocation method based on the detection of the static features located in the vicinity of the ego vehicle. The system performs at least one measurement of the visual appearance of each of at least one static feature located in the vicinity of the ego vehicle. Using the at least one measurement, a position of the ego vehicle relative to the static feature is calculated. The real world position of the static feature is identified. The position of the ego vehicle relative to the static feature is calculated, which is, in turn, used to calculate a static feature measurement of the vehicle location. The coarse geolocation measurement and the the static feature measurement are combined to form a fine geolocation position. By combining the measurements, a more accurate location of the ego vehicle can be determined.

A system comprises a computer including a processor and a memory. The memory includes instructions such that the processor is programmed to: receive safety messages from a plurality of vehicles in communication with the edge server, determine an uplink frequency recommendation for transmitting safety messages from at least one vehicle of the plurality of vehicles based on at least one of a position error or a collision risk, determine a downlink frequency recommendation for transmitting safety messaging to at least one vehicle of the plurality of vehicles based on at least one of a position error or a collision risk, and transmit the frequency recommendations to the at least one vehicle.

Methods and devices for generating data for controlling a Self-Driving Car (SDC) are disclosed. The method includes: (i) acquiring a predicted object trajectory for an object, (ii) acquiring a set of anchor points along the lane for the SDC, (iii) for each one of the set of anchor points, determining a series of future moments in time when the SDC is potentially located at the respective one of the set of anchor points, thereby generating a matrix structure including future position-time pairs, (iv) for each future position-time pair in the matrix structure, using the predicted object trajectory for determining a distance between a closest object to the SDC as if the SDC is located at the respective future position-time pair, and (v) storing the distance between the closest object to the SDC in association with the respective future position-time pair in the matrix structure.