In one aspect, an operating method of an intelligence vehicle driving control system is provided that comprises: a collecting step of collecting big data including a wheel torque and a speed for every vehicle type and traffic information; a torque calculating step of learning the big data using a predetermined machine learning model and inputs a specific desired speed profile to the machine learning model to calculate a motor torque of a driving vehicle; and an optimal speed profile deriving step of calculating an energy consumption required to generate the calculated motor torque using a predetermined dynamic programming method and a reverse vehicle dynamic model and deriving an optimal speed profile in which the energy consumption is minimized.

A vehicle driving assistance apparatus predicts (i) a first consumed energy amount corresponding to a consumed energy amount consumed by a driving apparatus of an own vehicle when executing a first following control and (ii) a second consumed energy amount corresponding to the consumed energy amount consumed by the driving apparatus of the own vehicle when executing the second following control. The apparatus executes the second following control when the second consumed energy amount is smaller than the first consumed energy amount. On the other hand, the apparatus executes the first following control when the second consumed energy amount is equal to or greater than the first consumed energy amount.

A method includes detecting, via a processor of an autonomous vehicle, an upcoming downhill road segment of a route on which the autonomous vehicle is currently travelling. The detection is based on map data, camera data, and/or inertial measurement unit (IMU) data. In response to detecting the upcoming downhill road segment, a descent plan is generated for the autonomous vehicle. The descent plan includes a speed profile and a brake usage plan. The brake usage plan specifies a non-zero amount of retarder usage and an amount of foundation brake usage for a predefined time period. The method also includes autonomously controlling the autonomous vehicle, based on the descent plan, while the autonomous vehicle descends the downhill road segment.

A processor unit is configured for determining target torque values (21), which lie within a prediction horizon (20), and target speed values (19), which lie within the prediction horizon (20), by executing an MPC algorithm, which includes a longitudinal dynamics model of a drive train of the motor vehicle. An autonomous driving function of the motor vehicle is carried out in a torque specification operating mode or in a speed specification operating mode as a function of the level of the target torque values (21). In the torque specification operating mode, a prime mover of the drive train is controlled by an open-loop system based on the target torque values (21). In the speed specification operating mode, a speed governor of the drive train is controlled by an open-loop system based on the target speed values (19).

This invention involves the effect of multiple rules of the road at different elevation profiles on the speed constraints and therefore the overall fuel efficiency. A vehicle designed to optimize fuel consumption that is comprised of the rules of the road that determine maximum speed, minimum speed, stop signs, streetlights, and/or changes in other rules that determine the allowable speeds of the road, a localization mechanism, and an optimization engine to optimize the fuel economy by selecting a speed profile within that maintains the vehicle within the assigned range of speeds and minimizes fuel consumption. A wide variety of methods that typically are used to optimize the fuel efficiency of human drivers operating standard vehicles can also be applied towards autonomous vehicles driving at different speed constraints and with different changes in the elevation.

In one embodiment, a driving environment is perceived based on sensor data obtained from a variety of sensors, including determining a current speed of an ADV. In response to a request for driving with an idle speed, a speed guideline is generated based on an idle speed curve in view of the current speed of the ADV. A speed planning operation is performed by optimizing a cost function based on the speed guideline to determine the speeds of the trajectory points at different points in time along a trajectory planned to drive the ADV. One or more control commands are then generated to control the ADV with the planned speeds along the planned trajectory, such that the ADV moves according to an intended idle speed.

Determination of a trajectory for a first vehicle (1) by model predictive control (MPC) is provided. Trajectory information about a second vehicle (18) traveling in the area ahead of the first vehicle (1) is utilized. In particular, discretization points (P.sub.1, P.sub.2, P.sub.3) and arrival times of the vehicles (1, 18) at the discretization points (P.sub.1, P.sub.2, P.sub.3) are utilized to generate constraints for the model predictive control of the first vehicle (1).

A method for determining a speed profile to be followed by a vehicle, including acquiring event data including a distance from an event and a target speed at this event for the vehicle, and determining a speed profile to be followed as a function of time, between an initial speed and the target speed in three successive distinct phases, respectively a first phase in which the jerk is set constant at a predetermined maximum jerk value to reach an optimal target acceleration value, a second phase in which the optimal target acceleration value is kept constant, and a third phase in which the jerk is again set constant to reach a zero acceleration value at the end of the third phase. The optimal target acceleration value is such that the distance required to carry out the three phases of the profile is equal to the distance from the event.

A computer implemented method for scenario generation for autonomous vehicle navigation that can include defining a cellular automaton layer that defines a road network level behavior with at least one rule directed to pathways by vehicles on a passageway for travel. The method may further include defining an active matter layer that defines a vehicle level behavior with at least one rule directed to movement of the vehicles on an ideal route for the pathways; and defining a driver agent layer that defines driving nature with at least one rule that impacts changes in the vehicle level behavior dependent upon a characterization of driver behavior. The method may further include combining outputs from the different layer to provide scenario generations for autonomous vehicle navigation. The combining of the outputs can utilize a pseudo random value to determine at an order in the execution and duration of execution for the layers.

A speed control system (12) for a vehicle (100), the speed control system (12) being configured to: automatically cause application of positive and negative torque, as required, to one or more wheels of a vehicle (100) to cause a vehicle to travel in accordance with a target speed value, the target speed value being stored in a memory of the control system (12); and detect a crest of a slope ahead of the vehicle (100); wherein the speed control system (12) is configured automatically to attempt to adjust a speed of the vehicle (100) to cause the vehicle (100) to travel at a predetermined crest speed value when a crest of a slope is detected ahead of the vehicle (100), the predetermined crest speed value being determined in dependence at least in part on terrain gradient information respect of terrain prior to the crest.