Disclosed herein are systems, devices, and methods of a safety system for monitoring the in-vehicle safety of internal objects within a vehicle. The safety system generates a digital twin of the interior objects from vehicle configuration data indicating a configuration of an interior environment of the vehicle, from interior object data associated with the interior object within the interior environment of the vehicle, and from vehicle situation data that indicates an operating status of the vehicle. The digital twin is an abstract model of the interior objects within the interior environment of the vehicle, and the safety system generates, based on the digital twin, a safety score associated with an operating behavior of the vehicle. Based on the safety score, the safety system determines a target level for the operating behavior.

A vehicle control method in a hybrid electric vehicle including an internal combustion engine, a battery, an electric motor, and a control unit. The method includes estimating an estimated vehicle velocity trajectory, estimating an initial engine power trajectory, simulating state of charge of the battery with the vehicle velocity trajectory and the initial engine power trajectory, estimating an initial terminal co-state value, simulating backward co-state dynamics using the state of charge and vehicle velocity trajectory, to obtain a resulting co-state trajectory. The co-state trajectory is used to solve a minimization control and propagate state of charge dynamics forward in time. The method includes updating control and the co-state trajectory, adjusting the terminal co-state value, and controlling a usage of the battery and the internal combustion engine. The method can be performed to optimize the engine power trajectory to minimize fuel consumption in real time.

A vehicle control apparatus includes a first electric motor, a second electric motor, a power storage device, a first consumption amount calculator, a first saving amount calculator, a saving balance calculator, and a display controller. The first consumption amount calculator calculates a first fuel amount consumed by the engine owing to charging when the power storage device is charged. The first saving amount calculator calculates a first fuel amount saved by the engine owing to discharge when the power storage device is discharging. The saving balance calculator calculates, based on the first fuel amount consumed and the first fuel amount saved, a fuel saving balance for each calculation period. The display controller controls, based on the fuel saving balance, fuel saving information to be displayed on a display.

System, methods, and other embodiments described herein relate to emulating vehicle dynamics. In one embodiment, a method for emulating vehicle dynamics in a vehicle having a plurality of wheels and equipped with all-wheel steering, includes receiving emulation settings that indicate one or more environment parameters and/or vehicle parameters, detecting driver inputs including at least steering input and throttle input, executing a simulation model that receives the driver inputs and emulation settings, simulates the vehicle operating based on the driver inputs and the emulation settings, and outputs one or more simulated states of the vehicle based on the simulated operation of the vehicle, determining one or more actuation commands for each wheel of the vehicle to cause the vehicle to emulate the one or more simulated states, and executing the one or more actuation commands, wherein the actuation commands include at least wheel angle commands and torque commands.

A set of actions corresponding to a particular state of the environment of a vehicle is identified. A respective encoding is generated for different actions of the set, using elements such as distinct colors to distinguish attributes such as target lane segments. Using the encodings as inputs to respective instances of a machine learning model, respective value metrics are estimated for each of the actions. One or more motion-control directives to implement a particular action selected using the value metrics are transmitted to motion-control subsystems of the vehicle.

A real-time assessment method of driving risk based on equivalent force includes: S1, collecting traffic environment information and various types of traffic environment use object information in a road environment in an area to be assessed; S2, inputting, into an electronic control unit of a vehicle, the traffic environment use object information and the environment information acquired in S1, wherein a road risk assessment model based on the equivalent force distribution is preset in the electronic control unit; S3, using the road risk assessment model, so as to acquire road traffic risk E of the vehicle i and equivalent force distribution F.sub.ij between the vehicle i and the object j in different traffic environments, wherein the object j represents any traffic element other than vehicle i in various traffic environment use object information. A real-time assessment device of driving risk based on equivalent force is further provided.

Systems and methods are provided for vehicle navigation. In one implementation, a system may comprise an interface to obtain sensing data of an environment of the host vehicle. The processing device may be configured to determine a planned navigational action; identify, a target vehicle in the environment of the host vehicle; predict a distance between the host vehicle and the target vehicle if the planned navigational action was taken; determine a current host vehicle stopping distance based on a braking capability, acceleration capability, and speed of the host vehicle; determine a current target vehicle braking distance based on a speed and braking capability of the target vehicle; and implement the planned navigational action when the predicted distance of the planned navigational action is greater than a minimum safe longitudinal distance calculated based on the current host vehicle stopping distance and the current target vehicle braking distance.

A method, system and robot for autonomous navigation thereof between two rows of plants, wherein said robot includes two or more sensing devices, sensor A and sensor B, mounted thereon and moves forward along an axis parallel to the rows of plants, being autonomously steered by exerting angular corrections to place the robot as close as possible to the centerline between the rows of plants, wherein the method and system includes the following:

(ii) dividing a two-dimensional grid of square cells into I.sub.G.Math.J.sub.G groups of cells;

(iii) obtaining data points using sensor A and sensor B;

(vii) moving the robot: (a) by turning right; or (b) by turning left; or (c) forward without turning,
depending on whether each group of cells (i,j) is calculated as low-activated, high-activated or not activated using said data points.

Method for the estimation of at least a variable (β; ν.sub.x, ν.sub.y; ψ, μ) affecting a vehicle dynamics (10), including measuring dynamic variables (MQ) of the vehicle (10) during its motion, calculating in real time an estimate (Formula (I)) of said variable (β; ν.sub.x, ν.sub.y; ψ, μ), on the basis of said measured dynamic variables (MQ), The method includes: calculating (230) said estimate of said at least a variable (β; ν.sub.x, ν.sub.y; ψ, μ) by an estimation procedure (DVS.sub.β; DVS.sub.βν; DVS.sub.βνμ) comprising taking in account a set of dynamic variables (MQ) measured during the motion of the vehicle (10) over respective time intervals (n.sub.y, n.sub.w, n.sub.ψ, n.sub.x, n.sub.α) and applying on said set of measured dynamic variables (MQ) at least an optimal nonlinear regression function (ƒ*.sub.β; ƒ*.sub.x, ƒ*.sub.y; ƒ*.sub.β1, ƒ*.sub.β2, ƒ*.sub.ψ1, ƒ.sub.ψ2) calculated with respect to said variable (β; ν.sub.x, ν.sub.y; ψ, μ) to estimate to obtain said estimate of said variable (β; ν.sub.x, ν.sub.y; ψ, μ), said optimal non linear regression function (ƒ*.sub.β; ƒ*.sub.x, ƒ*.sub.y; ƒ*.sub.β1, ƒ*.sub.β2, ƒ*.sub.ψ1, ƒ*.sub.ψ2) being obtained by an optimal calculation procedure (220) including: on the basis of an acquired set of reference data (D.sub.d) and of said set of dynamic variables (MQ) measured during the motion of the vehicle (10), finding, for a desired accuracy level (ε), a regression function (ƒ*.sub.β; ƒ*.sub.x, ƒ*.sub.y; ƒ*.sub.β1, ƒ*.sub.β2, ƒ*.sub.ψ1, ƒ*.sub.ψ2) giving an estimation error lower or equal than said desired accuracy level (ε) in a given set of operative conditions (OC), said acquired set of reference data (D.sub.d) being obtained by acquiring (210) in said given set of operative conditions (OC) a set of reference data (D.sub.d) of variables including variables corresponding to said measured dynamic variables (MQ) of the vehicle (10) and a lateral (v.sub.y) and a longitudinal velocity (v.sub.x) of the vehicle (10).

A control apparatus for controlling a vehicle includes a driving motor configured to drive the vehicle by outputting motor torque based on a supply voltage from a battery, and an engine configured to drive the vehicle by outputting engine torque. The control apparatus may acquire driving mode data which is calculated based on traffic information from the current position to the destination of the vehicle and dimension information of the vehicle, and control the vehicle to drive to the destination according to a driving mode which is determined by applying a travelling condition of the vehicle to the acquired driving mode data, where the power distribution ratio of the motor torque to the engine torque is reflected in the driving mode data.