A method for calculating a lateral position of an ego motor vehicle on a traffic lane includes calculating a first theoretical lateral position of the ego vehicle, calculating a second theoretical lateral position of the ego vehicle, calculating a third theoretical lateral position of the ego vehicle, calculating the lateral position of the ego vehicle using a weighted average of the first lateral position, the second lateral position, and the third lateral position.

An autonomous vehicle (AV) includes features that allows the AV to comply with applicable regulations and statutes for performing safe driving operation. Example embodiments relate to an autonomous vehicle having a trailer coupled to a rear thereof. An example method includes continuously predicting a trailer trajectory that is distinct from a planned trajectory of the autonomous vehicle. The method further includes determining that the predicted trailer trajectory is within a minimum avoidance distance away from a stationary vehicle located on a roadway on which the autonomous vehicle is located. The method further includes modifying the planned trajectory of the autonomous vehicle such that the predicted trailer trajectory satisfies the minimum avoidance distance. The method further includes causing the autonomous vehicle to navigate along the modified trajectory based on transmitting instructions to one or more subsystems of the autonomous vehicle.

A navigation system for a host vehicle may include a processor programmed to: receive from a center camera onboard the host vehicle a captured center image including a representation of at least a portion of an environment of the host vehicle, receive from a left surround camera onboard the host vehicle a captured left surround image including a representation of at least a portion of the environment of the host vehicle, and receive from a right surround camera onboard the host vehicle a captured right surround image including a representation of at least a portion of the environment of the host vehicle; provide the center image, the left surround image, and the right surround image to an analysis module configured to generate an output relative to the at least one captured center image; and cause a navigational action by the host vehicle based on the generated output.

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.

An apparatus for controlling lane change is disclosed. The apparatus may include a weight detection unit sensing weight of freight, a safety space setting unit receiving sensed weight and setting a safety space required for a vehicle to safely move to a target lane from a current lane based on the sensed weight, a sensor unit sensing whether an object exists in the safety space using at least one sensor, and a control unit controlling the vehicle to move to the target lane within a preset time when there is no sensed object. A method of controlling lane change is also disclosed.

State information can be determined for a subject that is robust to different inputs or conditions. For drowsiness, facial landmarks can be determined from captured image data and used to determine a set of blink parameters. These parameters can be used, such as with a temporal network, to estimate a state (e.g., drowsiness) of the subject. To improve robustness, an eye state determination network can determine eye state from the image data, without reliance on intermediate landmarks, that can be used, such as with another temporal network, to estimate the state of the subject. A weighted combination of these values can be used to determine an overall state of the subject. To improve accuracy, individual behavior patterns and context information can be utilized to account for variations in the data due to subject variation or current context rather than changes in state.

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 vehicle traveling control device is capable of implementing an automatic driving mode that can cause an own vehicle to travel following a preceding vehicle within a predetermined speed range. The vehicle traveling control device includes an acquiring unit that acquires traffic congestion information relating to a traffic congestion nearby the own vehicle, and a control unit that suppresses acceleration of the own vehicle in a first case in which presence of a traffic congestion is detected based on the traffic congestion information, and the own vehicle can be accelerated, during implementation of the automatic driving mode.

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.