A vehicular sensing system includes a camera and a radar sensor disposed at a vehicle. The system, responsive to processing of radar data captured by the radar sensor, determines an edge of a road the vehicle is traveling along. Responsive to processing of image data captured by the camera, location of a lane marking of the road is determined. As the vehicle travels along the road, the system, responsive to failing to determine the location of the lane marking of the road via processing of the image data captured by the camera, predicts the location of the lane marking of the road based on the radar data. The vehicle is controlled based in part on the predicted location of the lane marking of the road.

In an in-cabin radar apparatus, transmitting antennas are disposed at one side in a direction parallel to a control circuit and disposed in a line in a vertical direction, and receiving antennas are disposed at one side in a direction perpendicular to the control unit and disposed in a line in a horizontal direction. Each transmission side feed line may be perpendicularly connected to one of the transmitting antennas, and each receiving side feed line may be perpendicularly connected to one of the receiving antennas. Each of a distance between the transmitting antennas and a distance between the receiving antennas may be implemented to be less than or equal to half of a transmitting and receiving wavelength.

The disclosed subject matter includes a method and system for navigating an unmanned ground vehicle (UGV), that include: generating, based on the scanning output data, a first map comprising a first group of cells and characterized by a first size; generating, based on the scanning output data, a second map representing an area smaller than that of the first map comprising a second group of cells, which are characterized by a second size being smaller than the first size; wherein each cell in the first group of cells and the second group of cells is classified to a class selected from at least two classes, comprising traversable and non-traversable, wherein the second part at least partly overlaps the first part; navigating the UGV based on data deduced from crossing between cells in the first map and second map.

The invention relates to a control device for a driver assistance system, wherein the control device comprises a sensor interface via which the control device can be connected to at least one sensor module to receive data from the at least one sensor module, a power processor which is adapted to detect objects and to provide object data based on the data from the at least one sensor module, and a system interface via which the control device can be connected to a higher-level control device of the driver assistance system for forwarding object data provided by the power processor.

An in-car safety system includes: a detecting device, a processor, an in-car equipment and a piezoelectric device. The detecting device is disposed on a car. The processor is disposed in the car. The processor is electrically connected to the detecting device. The processor is configured to receive a detecting signal transmitted by the detecting device and transmit an electrical signal in accordance with the detecting signal. The in-car equipment is disposed in the car. The piezoelectric device is disposed on the in-car equipment. The piezoelectric device is electrically connected to the processor. The piezoelectric device is configured to receive the electrical signal and generate a vibration to the in-car equipment in accordance with the electrical signal.

Provided are methods for semantic annotation of sensor data using unreliable map annotation inputs, which can include training a machine learning model to accept inputs including images representing sensor data for a geographic area and unreliable semantic annotations for the geographic area. The machine learning model can be trained against validated semantic annotations for the geographic area, such that subsequent to training, additional images representing sensor data and additional unreliable semantic annotations can be passed through the neural network to provide predicted semantic annotations for the additional images. Systems and computer program products are also provided.

A method of detecting a lane line based on lidar data can include detecting, by a processor, points each estimated as a lane line in a lidar data, performing, by the processor, an estimation operation of estimating parameters of a mathematical model using the detected points, and performing, by the processor, a setting operation of calculating distances between each of the detected points and the mathematical model in which the parameters are estimated and setting the calculated distances as scores. The method can further include performing, by the processor, a summation operation of summing the scores, and setting, by the processor, the mathematical model determined according to the summation score as a lane line.

An apparatus for monitoring the surrounding environment of a vehicle includes: a plurality of detection sensors to detect an object outside the vehicle according to a frame at a predefined period; and a controller to extract a stationary object from among objects detected by the detection sensors, to map the stationary object to a grid map, to calculate an occupancy probability parameter indicative of a probability that the stationary object will be located on a grid of the grid map, and to monitor the surrounding environment of the vehicle based on the occupancy probability parameter. The controller maps the stationary object to the grid map while updating the grid map by changing an index of each grid constituting the grid map according to behavior information of the vehicle.

Provided is a scooter radar detection system for a scooter, including: a control module for controlling operation of the scooter radar detection system; two detection radars flanking a license plate, facing the rear of the scooter, and being in signal connection with the control module; two flash alert units disposed at rear-view mirrors on two sides of the scooter, respectively, and being in signal connection with the control module; and a vibration alert module disposed below a seat and being in signal connection with the control module.

A lane change support device includes a control unit configured to execute lane change control for enabling a vehicle to automatically change lanes from a lane in which the vehicle is traveling to an adjacent lane. The control unit counts a holding time for which an operation part that is operated to a predetermined operation position to start the lane change control is continuously held at the operation position, starts the lane change control when the counted holding time reaches a predetermined threshold time, and calculates a proficiency level of a driver of the vehicle for an operation of the lane change support device during execution of the lane change control and sets the threshold time to be used for a successive lane change control based on the proficiency level.