A trajectory generation device is configured to generate a future trajectory along which a vehicle travels in a future. The trajectory generation device includes an action plan unit, a trajectory candidate unit, a trajectory evaluation unit, and a trajectory fixing unit. The action plan unit is configured to plan a future action of the vehicle. The trajectory candidate generation unit is configured to generate a trajectory candidate of the future trajectory in accordance with the planned future action. The trajectory evaluation unit is configured to evaluate whether a traveling along the trajectory candidate is permitted. The trajectory fixing unit is configured to fix the trajectory candidate as the future trajectory when it is determined that the traveling along the trajectory candidate is permitted. The action plan unit is configured to plan the future action again when it is determined that the traveling along the trajectory candidate is not permitted.

Systems and methods for selecting a trajectory for an autonomous vehicle based on perceived risk are provided. A trajectory is selected that satisfies objective safety constraints and minimizes an overall cost based at least in part on cost components representing the perceived risk of the trajectory. The vehicle is navigated according to the selected (e.g., optimized) trajectory.

A safety system for a vehicle may include one or more processors configured to determine, based on a friction prediction model, one or more predictive friction coefficients between the ground and one or more tires of the ground vehicle using first ground condition data and second ground condition data. The first ground condition data represent conditions of the ground at or near the position of the ground vehicle, and the second ground condition data represent conditions of the ground in front of the ground vehicle with respect to a driving direction of the ground vehicle. The one or more processors are further configured to determine driving conditions of the ground vehicle using the determined one or more predictive friction coefficients.

A method includes identifying an object that is invading a lane that an autonomous vehicle is occupying, and generating a constraint about a point of crossing, where the constraint has a direction and a length, and the point of crossing represents a location of where the object and the autonomous vehicle will collide if the object maintains its current trajectory and the autonomous vehicle maintains its current trajectory. The method includes applying the constraint to a motion plan associated with the autonomous vehicle, and issuing one or more commands to adjust movement of the autonomous vehicle in response to encountering the constraint.

Embodiments for operational envelope detection (OED) with situational assessment are disclosed. Embodiments herein relate to an operational envelope detector that is configured to receive, as inputs, information related to sensors of the system and information related to operational design domain (ODD) requirements. The OED then compares the information related to sensors of the system to the information related to the ODD requirements, and identifies whether the system is operating within its ODD or whether a remedial action is appropriate to adjust the ODD requirements based on the current sensor information. Other embodiments are described and/or claimed.

Embodiments for operational envelope detection (OED) with situational assessment are disclosed. Embodiments herein relate to an operational envelope detector that is configured to receive, as inputs, information related to sensors of the system and information related to operational design domain (ODD) requirements. The OED then compares the information related to sensors of the system to the information related to the ODD requirements, and identifies whether the system is operating within its ODD or whether a remedial action is appropriate to adjust the ODD requirements based on the current sensor information. Other embodiments are described and/or claimed.

A method for minimizing disturbance due to wind forces of a trailer being towed by a vehicle. The method also includes receiving, at a data processing hardware data from a sensor system for the tow vehicle. The method also includes determining, at the data processing hardware, a passing object profile. The method also includes predicting, at the data processing hardware, a wind force profile based upon the sensor data the passing object profile. The method also includes determining, at the data processing hardware, at least one preventative action for the vehicle to minimize the effect of disturbance on the trailer.

A safety system for a vehicle may include one or more processors configured to determine, based on a friction prediction model, one or more predictive friction coefficients between the ground and one or more tires of the ground vehicle using first ground condition data and second ground condition data. The first ground condition data represent conditions of the ground at or near the position of the ground vehicle, and the second ground condition data represent conditions of the ground in front of the ground vehicle with respect to a driving direction of the ground vehicle. The one or more processors are further configured to determine driving conditions of the ground vehicle using the determined one or more predictive friction coefficients.

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.

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.