The present disclosure relates to a method and system for integrated path planning and path tracking control of an autonomous vehicle. The method includes: obtaining five input control variables and eleven system state variables of an autonomous vehicle at current time; constructing a vehicle path planning-tracking integrated state model according to the obtained variables at the current time; enveloping external contours of two autonomous vehicles using elliptical envelope curves to determine elliptical vehicle envelope curves of the two autonomous vehicles, respectively; determining time to collision (TTC) between the vehicles according to elliptical vehicle envelope curves and vehicle driving states; establishing an objective function of a model prediction controller (MPC) according to the model; and solving the objective function based on the TTC, and determining input control variables to the MPC at the next time. Autonomous vehicle collision avoidance can be achieved according to the present disclosure.

The technology relates to predicting that an object is going to enter into a trajectory of a vehicle. This may include receiving sensor data identifying a first location of the object in an environment of the vehicle at a first point in time and receiving sensor data identifying a second location of the object in the environment at a second point in time. In addition, a boundary of the trajectory is determined by defining at least a two-dimensional area through which the vehicle is expected to travel in the future. A first distance between the boundary and the first location and a second distance between the trajectory and the second location are determined. The first distance and the second distance are used to determine that the object is going to enter into the trajectory at a future point in time.

A control system includes a controller configured to determine a target speed between a first target position of a haul vehicle relative to a harvester and a second target position of the haul vehicle relative to the harvester based on a flow rate of agricultural product through a conveyor of the harvester. The haul vehicle is coupled to a storage compartment, an outlet of the conveyor is aligned with a first unloading point within the storage compartment while the haul vehicle is positioned at the first target position, and the outlet of the conveyor is aligned with a second unloading point within the storage compartment while the haul vehicle is positioned at the second target position. Furthermore, the controller is configured to output a control signal indicative of instructions to direct the haul vehicle from the first target position to the second target position at the target speed.

A method of recognizing a median strip and predicting risk of a collision through analysis of an image includes acquiring an image of the road ahead including a median strip and a road bottom surface through a camera of a moving vehicle (S110), generating a Hough space by detecting an edge from the image (S120), recognizing an upper straight line of the median strip from the Hough space (S130), generating a region of interest (ROI) of the median strip using information on the upper straight line of the median strip and a lane (S140), detecting an object from an internal part of the ROI of the median strip through a labeling scheme (S150), and determining a tracking-point set of the objects that satisfy a specific condition (S160).

Systems and methods are provided to provide a steering indicator to a driver of a vehicle. It is determined whether a first obstacle is in a forward path of the vehicle and whether a second obstacle is present at a side of the vehicle. In response to (a) determining the first obstacle is in the forward path and (b) determining whether the second obstacle is present at the one or more sides of the vehicle, a suggested steering action indicator indicating one or more movements for the vehicle to avoid the first obstacle is provided to a user interface of the vehicle.

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.

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.

Provided are a longitudinal and lateral integrated moving horizon decision making method and apparatus for an autonomous vehicle in a snowy and icy environment based on trajectory prediction, with an aim of reducing the complexity of planning a lane-changing trajectory. The method includes: screening out at least one obstacle, planning a lane-changing trajectory according to the at least one obstacle, establishing a decision making model according to the lane-changing trajectory, and controlling the autonomous vehicle by designing a longitudinal and lateral integrated moving horizon decision making controller according to the decision making model. Further provided are a longitudinal and lateral integrated moving horizon decision making apparatus for an autonomous vehicle in a snowy and icy environment based on trajectory prediction and a storage medium.

A system for estimating a spacing profile for a road agent includes a first module and a second module. The first module includes instructions that cause one or more processors to receive data related to characteristics of the road agent and road agent behavior detected in an environment of an autonomous vehicle, initiate an analysis of the road agent behavior, and estimate the spacing profile of the road agent as part of the analysis. The spacing profile includes a lateral gap preference and predicted behaviors of the road agent related to changes in lateral gap. The second module includes instructions that cause the one or more processors to determine one or more components of autonomous vehicle maneuver based on the estimated spacing profile and send control instructions for performing the autonomous vehicle maneuver.