A LIDAR system includes a plurality of LIDAR units. Each of the LIDAR units includes a housing defining a cavity. Each of the LIDAR units further includes a plurality of emitters disposed within the cavity. Each of the plurality of emitters is configured to emit a laser beam. The LIDAR system includes a rotating mirror and a retarder. The retarder is configurable in at least a first mode and a second mode to control a polarization state of a plurality of laser beams emitted from each of the plurality of LIDAR units. The LIDAR system includes a polarizing beam splitter positioned relative to the retarder such that the polarizing beam splitter receives a plurality of laser beams exiting the retarder. The polarizing beam is configured to transmit or reflect the plurality of laser beams exiting the retarder based on the polarization state of the laser beams exiting the retarder.

Provided are a vehicle driving control method and apparatus. The method includes: determining, according to driving data of a vehicle, at least one piece of candidate speed information of the vehicle, the candidate speed information is used to indicate traveling speed of the vehicle at each moment in a first preset time period; predicting, according to movement data of an obstacle on a road, an obstacle trajectory of the obstacle in the first preset time period; determining right-of-way information corresponding to the obstacle trajectory, the right-of-way information is used to indicate a priority in traveling of the vehicle and the obstacle; and determining target speed information in the at least one piece of candidate speed information according to the right-of-way information corresponding to the obstacle trajectory and the obstacle trajectory, and controlling the vehicle to travel according to a route corresponding to the vehicle and the target speed information.

An autonomous driving control method includes collecting travel information on a host vehicle traveling autonomously and on at least one other vehicle, determining a driving intention of the other vehicle based on the travel information on the other vehicle, predicting a driving route of the other vehicle based on the driving intention of the other vehicle, and determining a driving route of the host vehicle based on the predicted driving route of the other vehicle.

A vehicle control device recognizes an intersection present in front of a vehicle proceeding in a first direction on a first road, a first another vehicle proceeding in a second direction opposite to the first direction on the first road to approach the intersection, and a second another vehicle traveling after the first another vehicle, controls the vehicle based on a first relative relation between the first another vehicle and the vehicle and a second relative relation between the second another vehicle and the vehicle, determines, when the first and second another vehicles are expected to enter the second road, whether the vehicle enters the second road after the first another vehicle and before the second another vehicle or after the second another vehicle based on relative relations between a basis position and the vehicle, the first and second another vehicles, and controls the vehicle based on a determining result.

A method for autonomous navigation of an autonomous vehicle includes: accessing a first scan image containing data captured by a sensor on the autonomous vehicle at a first time; identifying a first group of points in the first scan image representing an object in a field proximal the autonomous vehicle; characterizing a first motion of the object at the first time based on the first group of points; characterizing an uncertainty of the first motion of the object at the first time; calculating a predicted second uncertainty of a second motion of the object at a second time based on the first motion of the object and motion of the autonomous vehicle at the first time; and, in response to the predicted second uncertainty falling below the uncertainty, muting the object from braking consideration for object avoidance by the autonomous vehicle at the second time.

In accordance with an aspect of a driver assistance system includes a radar sensor installed in a vehicle, having a side sensing field of view of the vehicle, and configured to acquire side sensing data; and a controller including a processor configured to process the side sensing data; and the controller may be configured to estimate motion state of a moving object at the side of the vehicle based on applying the side sensing data to at least one of a first estimation model and a second estimation model.

An autonomous vehicle computing system can include a primary perception system configured to receive a plurality of sensor data points as input generate primary perception data representing a plurality of classifiable objects and a plurality of paths representing tracked motion of the plurality of classifiable objects. The autonomous vehicle computing system can include a secondary perception system configured to receive the plurality of sensor data points as input, cluster a subset of the plurality of sensor data points of the sensor data to generate one or more sensor data point clusters representing one or more unclassifiable objects that are not classifiable by the primary perception system, and generate secondary path data representing tracked motion of the one or more unclassifiable objects. The autonomous vehicle computing system can generate fused perception data based on the primary perception data and the one or more unclassifiable objects.

The invention provides a computer-implemented method of planning a path for a mobile robot such as an autonomous vehicle in the presence of K obstacles. The method uses, for each of the K obstacles, a shape Bk and a density function pk(x) representing the probabilistic position of the obstacle. The method repeats the following steps for at least two different paths A: —choosing a path A, where A is the swept area of the robot within a given time interval; and—calculating based on the density function of each obstacle and the swept path an upper bound on the total probability of at least one collision F.sub.D between the robot and the K obstacles. This allows a number of candidate paths to be ranked for safety. By precomputing factors of the computational steps over K obstacles, the computation per path is O(N), and not O(NK). A safety threshold can be used to filter out paths below that threshold. An operating path to control the robot can be selected from the remaining paths, optionally based on other factors such as comfort or efficiency.

The disclosure is directed to systems and methods for operating or controlling an ADAS mechanism. A first sensor of an ADAS mechanism can determine a potential obstacle to a vehicle, and a position of the potential obstacle relative to the first sensor. A second sensor can determine a gaze angle of a user of the vehicle. An activation engine in communication with the first sensor and the second sensor can determine a proximity of the gaze angle of the user to the determined position of the potential obstacle. The activation engine can control, in response to the determined proximity, an operation of the ADAS mechanism for responding to the potential obstacle.

A vehicle traveling control apparatus for a vehicle identifies a mobile object as a crossing mobile object if the supplementary angle of the angle formed between the velocity vector of the mobile object and the traveling direction of the vehicle is greater than a threshold. In a case where the supplementary angle was equal to or less than the threshold before the identification as the crossing mobile object and where a lateral position of the crossing mobile object is equal to or less than a distance threshold at the time of the identification, or in a case where the size of the crossing mobile object has been less than a predetermined value for a predetermined period of time or longer and where the lateral position is equal to or less than the distance threshold at the time of the identification, the crossing mobile object is excluded from emergency braking targets.