The disclosure relates to a lane change method and system, a storage medium, and a vehicle. The lane change method includes the following steps: receiving consecutive frames of condition information, the condition information including velocity information of a current vehicle, state information of an adjacent vehicle, and lane information; with the condition information as an input to a neural network, processing the condition information by means of the neural network, to obtain an initial lane change strategy; and correcting the initial lane change strategy based on a predetermined rule and the condition information, to generate and output a corrected lane change strategy. According to this lane change method, intelligent, safe and efficient lane change may be achieved during an autonomous driving or driving assistance process.

Image data are input to a machine learning program. The machine learning program is trained with a virtual boundary model based on a distance between a host vehicle and a target object and a loss function based on a real-world physical model. An identification of a threat object is output from the machine learning program. A subsystem of the host vehicle is actuated based on the identification of the threat object.

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.

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 vehicle traveling control device performs control to secure the vehicle-to-vehicle distance between the preceding vehicle and the own vehicle when the lateral position of the preceding vehicle traveling in the adjacent lane reaches the reference lateral position. The vehicle traveling control device includes a speed calculating unit that calculates a relative lateral speed of the preceding vehicle with respect to the own vehicle, a reference position setting unit that sets a reference lateral position based on the relative lateral speed, and a vehicle control unit that controls the traveling of the own vehicle when the preceding vehicle reaches the reference lateral position. The reference position setting unit sets the reference lateral position to the side farther from the own vehicle as the relative lateral speed is smaller.

A vehicle control device of an embodiment includes a recognizer configured to recognize a surrounding situation of a vehicle, and a driving controller configured to execute driving control of controlling one or both of a speed and steering of the vehicle on the basis of the surrounding situation recognized by the recognizer, in which the recognizer recognizes a traffic participant present in front of the vehicle and a traffic participant priority section present in a traveling direction of the vehicle, and the driving controller sets a risk area for the traffic participant priority section on the basis of a position and a traveling direction of the traffic participant, and executes the driving control based on the set risk area and the position of the traffic participant.

While operating a host vehicle in a lane, a target vehicle is detected entering the lane in front of the vehicle. A trajectory of the target vehicle is predicted based on sensor data. Upon determining that the target vehicle will pass through the lane based on the predicted trajectory, the host vehicle is operated based on determining a presence or an absence of a lead vehicle. Upon determining that the target vehicle will remain in the lane based on the predicted trajectory, the host vehicle is operated with the target vehicle as the lead vehicle.

A method of generating an output trajectory of an ego vehicle is described. The method includes extracting high-level features from a bird-view image of a traffic environment of the ego vehicle. The method also includes generating, using an automaton generative network, an automaton including an automaton state distribution describing a behavior of the ego vehicle in the traffic environment according to the high-level features. The method further includes generating the output trajectory of the ego vehicle according to extracted bird-view features of the bird-view image and the automaton state distribution describing the behavior of the ego vehicle in the traffic environment.

A refuse vehicle comprising a chassis, a body assembly coupled to the chassis, the body assembly defining a refuse compartment, one or more sensors coupled to the body and configured to provide data relating to the presence of an obstacle within an area near the refuse vehicle, a controller configured to receive the data from the one or more sensors, determine, using an obstacle detector and the data, the presence of an obstacle within the area and initiate a control action, wherein the control action includes at least one of controlling the movement of the refuse vehicle, controlling the movement of a lift assembly attached to the body assembly, or generating an alert.

Methods, systems, and apparatus, including computer programs encoded on a computer storage medium, for generating unmapped U-turn predictions using a machine learning model. One of the methods includes obtaining features of an agent travelling on a roadway. One or more unmapped U-turn regions in a vicinity of the agent on the roadway are identified. For each of the unmapped U-turn regions and from at least the features of the agent, a respective likelihood score that represents a likelihood that the agent intends to make an unmapped U-turn at the unmapped U-turn region is generated. Based on the respective likelihood scores, one or more of the unmapped U-turn regions are selected. For each selected unmapped U-turn region, data specifying a candidate future trajectory in which the agent makes the unmapped U-turn at the selected unmapped U-turn region is provided as a possible future trajectory for the agent.