The invention is a method for determining a speed profile for minimizing emissions of at least one pollutant generated by a vehicle during a journey. The method requires a model of the vehicle dynamics, an analytical model of the emissions of the pollutant, and at least one speed profile model divided into at least two phases, each of the phases corresponding to a traction acceleration mode of the vehicle with a number of acceleration modes preferably being five. Then, a speed profile minimizing the emissions of at least one pollutant is determined by seeking, from speed profiles with respect to the distance, duration, and initial and final speeds of the journey and distinguished by distinct phase durations with the speed profile for which the emissions of the pollutant are modelled is by use of an analytical model for which the pollutants are the lowest.

At the start of a path planning cycle for an autonomous vehicle, the system identifies a current plan associated with the autonomous vehicle, a speed plan that defines one or more velocities over time for the autonomous vehicle during the path planning cycle, and a current state of the autonomous vehicle. The current plan includes a spatial plan that defines a proposed trajectory for the autonomous vehicle during the path planning cycle. The current state defines one or more dynamic states of the autonomous vehicle. The system generates a sequence of predicted states of the autonomous vehicle over a prediction horizon period, identifies a predicted state from the sequence that corresponds to a publishing time of an updated plan for the autonomous vehicle, generates the updated plan, and causes the autonomous vehicle to execute the updated plan.

A Level IV fuel-saving robot system for ACE HDTs of the present disclosure focuses on the minimization of actual fuel consumption (L/100 km) for long-haul freight at first based on an electrical power split device (cPSD) and a mixed hybrid powertrain architecture. A basic model Level I fuel-saving robot realizes a longitudinal L1 automatic driving function through a predictive adaptive cruise (PACC) technology within an Operational Design Domain (ODD) for highways and reduces the actual fuel consumption of an ACE HDT by more than 20% compared with modern diesel HDTs, and the energy-saving and emission-reducing effect of the basic model Level I fuel-saving robot is decoupled from both the technical level of a vehicle engine and the driving level of a driver; an advanced Level IV fuel-saving robot has a IA automatic driving function within the ODD for highways, operates in a “shadow mode” or “detached mode”, automatically generates a discrepancy report or detachment report, completes the “3R.” batch validation for an L4 system on a billion mile scale quickly with high cost effectiveness on the premise of ensuring the traffic safety of existing road users and reduces the total validation expense by more than 65% compared with the modern HDT with internal combustion engine equipped with the L4 system, promoting the early commercialization of the Level IV fuel-saving robot.

A parallel computing method for man-machine coordinated steering control of a smart vehicle based on risk assessment is provided, comprising the following steps: building a lateral kinetic equation model of a vehicle; building a target function by targeting at minimizing an offset distance of a vehicle driving track from a lane center line and making a change in a front wheel steering angle and a longitudinal acceleration as small as possible in a driving process; building a parallel computing architecture of a prediction model and the target function, and employing a triggering parallel computing method; solving and computing a gradient with a manner of back propagation and using a gradient descent method to obtain an optimal control amount of the front wheel steering angle and an optimal control amount of the longitudinal acceleration; and computing a driving weight, obtaining a desired front wheel steering angle and completing real time control.

Techniques are described to determine parameters and/or values for a control model that can be used to operate an autonomous vehicle, such as an autonomous semi-trailer truck. For example, a method of obtaining a data-driven model for autonomous driving may include obtaining data associated with a first set of variables that characterize movements of an autonomous vehicle over time and commands provided to the autonomous vehicle over time, determining, using at least the first set of data, non-zero values and an associated second set of variables that describe a control model used to perform an autonomous driving operation of the autonomous vehicle, and calculating values for a feedback controller that describes a transfer function used to perform the autonomous driving operation of the autonomous vehicle driven on a road.

Disclosed is a method for controlling engagement of an engine clutch in a hybrid electric vehicle in which an engagement control method of the engine clutch is accurately determined so as to minimize a determination error and a sense of discontinuity caused by conversion of the engagement control method resulting therefrom.

Provided is an automatic driving system based on a model predictive control, the automatic driving system where, in an event of a failure of an actuator, identification between a real vehicle and a vehicle model is simplified. Based on information regarding the failure of the actuator, the automatic driving system updates a spot in the vehicle model, the spot corresponding to the spot of failure detected, to a fixed value, and causes an actuator control device for the actuator, where the failure is detected, to fix a command value that is overwritten in accordance with a state of the actuator. With this configuration, the identification between the real vehicle and the vehicle model is simplified.

A moving object control device includes: a moving object position acquiring unit acquiring moving object position information indicating a position of a moving object; a target position acquiring unit acquiring target position information indicating a target position to which the moving object is caused to travel; and a control generating unit generating a control signal indicating a control content for causing the moving object to travel toward the target position on a basis of model information indicating a model that is trained using a calculation formula for calculating a reward including a term for calculating a reward by evaluating whether or not the moving object is traveling along a reference route by referring to reference route information indicating the reference route, the moving object position information acquired by the moving object position acquiring unit, and the target position information acquired by the target position acquiring unit.

A performance dimension is selected, and a gap in machine performance, according to the selected dimension, is identified. A target value is identified to improve machine control according to the selected dimension. A dependent dimension, which depends on the selected dimension, is selected and a dependency indicator, that indicates a dependency of the dependent dimension on the selected dimension, is accessed to identify a value of the dependent dimension that will change if the machine is controlled so that the value of the selected dimension is moved from a current value to the target value. The change in value of the selected dimension, and the dependent dimension are aggregated to determine whether machine control should be modified so the value of the selected dimension moves toward the target value. If so, a corresponding control operation is identified, and control signals are generated to control the machine to perform the identified control operation.

The technology relates to determining whether a vehicle operating in an autonomous driving mode is experiencing an anomalous condition, for instance due to a loss of tire pressure, a mechanical failure, or a shift or loss of cargo. The actual current pose of the vehicle is compared to an expected pose of the vehicle, where the expected pose is based on a model of the vehicle. If a pose discrepancy is identified, the anomalous condition is determined from information associated with the pose discrepancy. The vehicle is then able to take corrective action based on the nature of the anomalous condition. The corrective action may include making a real-time driving change, modifying a planned route, alerting a remote operations center, or communicating with one or more other vehicles.