Systems and methods for detecting and tracking objects using radar data are disclosed. The methods include receiving measurement data corresponding to an environment of a vehicle from a radar sensor associated with the vehicle, and identifying one or more object tracks of a plurality of object tracks that lie within an extended field of view (FOV) of the radar sensor. The extended FOV may be determined based on an original FOV of the radar sensor. The methods further include performing association of the measurement data with the one or more object tracks to identify an associated object track, and outputting the associated object track and the measurement data to a navigation system of the vehicle.

System, methods, and embodiments described herein relate to dynamically generating a wide field-of-view three-dimensional pseudo point cloud of an environment around a vehicle. A disclosed method may include capturing, via a camera, a first view in a first image, determining a first depth map based on the first image, obtaining, from an external system, a second image of a second view that overlaps the first view and a second depth map based on the second image, inputting the first image and second image into a self-supervised homograph network that is trained to output a homographic transformation matrix between the first image and the second image, and generating a three-dimensional pseudo point cloud that combines the first depth map and the second depth map based on the homographic transformation matrix.

A blind spot warning system is provided for a motor vehicle having a longitudinal axis and a steering shaft. The system includes a steering angle sensor (SAS), which is coupled to the steering shaft and generates a steering signal associated with a wheel angle in response to the steering shaft rotating. The system further includes a computer having one or more processors and a non-transitory computer readable storage medium storing instructions. The processor is programmed to determine a blind spot region extending from a sideview mirror of the motor vehicle and based on the wheel angle. The processor is further programmed to generate an actuation signal associated with the blind spot region. The system further includes one or more light projectors that project a light onto a roadway adjacent to the motor vehicle to indicate a current size and a current location of the blind spot region.

A driving assistance device configured to execute deceleration assistance for a driver's vehicle when the driver's vehicle turns right or left at an intersection is configured to recognize, based on a detection result from an external sensor of the driver's vehicle, an adjacent vehicle traveling in an adjacent lane adjacent to a traveling lane of the driver's vehicle, determine whether the adjacent vehicle turns in the same direction of the driver's vehicle at the intersection based on the detection result from the external sensor when the adjacent vehicle is recognized and the driver's vehicle turns right or left at the intersection, and execute the deceleration assistance to cause a vehicle-to-vehicle distance between the driver's vehicle and the adjacent vehicle to reach a distance equal to or larger than a target driver's vehicle-to-adjacent vehicle distance when the driving assistance device determines that the adjacent vehicle turns in the same direction.

A method of verifying accuracy of a perception system of a vehicle includes causing the vehicle to traverse a path around a target that is fixed in an environment. The target has a known pose. The path is configured so that the target comes into a respective field of view (FOV) of each respective perception sensor of one or more perception sensors of the perception system along the path. The method further includes, for each respective perception sensor of the one or more perception sensors, while the target is within the respective FOV of the respective perception sensor, acquiring a respective image of the target using the respective perception sensor; at the perception system, determining a respective pose of the target based on the respective image; and at a computer system communicatively coupled with the perception system, determining whether the respective pose matches the known pose of the target.

An apparatus for assisting driving of a vehicle includes a camera mounted to the vehicle for viewing an area in front of the vehicle, a radar sensor mounted to the vehicle to sense around the vehicle, and a controller connected to the camera and/or the radar sensor to detect obstacles and perform collision avoidance control. The controller is further configured to recognize a first obstacle approaching in a lateral direction from an outside of a driving lane of the vehicle, generate and store a collision avoidance path for avoiding collision with the first obstacle, recognize a third obstacle passing behind a second obstacle in the lateral direction after the recognition of the first obstacle is interrupted by the second obstacle, and perform collision avoidance control of the vehicle based on a similarity between the first obstacle and the third obstacle and based on the stored collision avoidance path.

A behavior control method for controlling a behavior of a vehicle comprising: specifying a blind-spot region as blind-spot of an environment recognition portion along a travel route for the vehicle; determining a jump-out possibility of a moving object to the travel route from the blind-spot region; performing a possibility reduction behavior to lower the jump-out possibility, in response to that the jump-out possibility is confirmed; and performing a travel behavior compliant with the travel route after starting the possibility reduction behavior.

A method of adjusting a driving strategy for a driverless vehicle is provided, which relates to a field of artificial intelligence, in particular to autonomous driving, cloud computing, NLP, computer vision and other fields, and may be applied to an interaction scene between a driverless vehicle and a pedestrian. A specific implementation solution includes: detecting an emotion of at least one pedestrian in response to the at least one pedestrian being detected within a preset range in front of the driverless vehicle; and adjusting a current driving strategy for the driverless vehicle based on a specified emotion in response to detecting that the at least one pedestrian includes a target pedestrian exhibiting the specified emotion.

The technology involves pullover maneuvers for vehicles operating in an autonomous driving mode. A vehicle operating in an autonomous driving mode is able to identify parking locations that may be occluded or otherwise not readily visible. A best case estimation for any potential parking locations, including partially or fully occluded areas, is compared against predictions for identified visible parking locations. This can include calculating a cost for each potential parking location and comparing that cost to a baseline cost. Upon determining that an occluded location would provide a more viable option than any visible parking locations, the vehicle is able to initiate a driving adjustment (e.g., slow down) prior to arriving at the occluded location. This enables the vehicle to minimize passenger discomfort while also providing notice to other road users of an intent to pull over.

A traffic safety control method is provided. The method includes obtaining first related information of a road when a vehicle is traveling on a road. Second related information is detected using a detecting device of the vehicle when the first related information indicates that there is the intersection in front of the vehicle on the road. The vehicle is controlled according to the first related information and the second related information.