High-performance road vehicle having: a plurality of replaceable or removable components; a control unit that supervises the operation of the road vehicle; at least one electronic identification device, which is fitted on a corresponding component, has a memory designed to contain at least one unique identifying code of the component and has a first transmission organ designed to send the data contained in the memory; and a second transmission organ designed to communicate with the first transmission organ and connected to the control unit to allow the control unit to interact with the electronic identification device.

A vehicle power management system (100) for optimising power efficiency in a vehicle (400), by managing a power distribution between a first power source (410) and a second power source (420). A receiver (110) receives a plurality of samples from the vehicle (400), each sample comprising vehicle state data, a power distribution and reward data measured at a respective point in time. A data store (350) stores estimated merit function values for a plurality of power distributions. A control system (200) selects, from the data store (350), a power distribution having the highest merit function value for the vehicle state data at a current time, and transmits the selected power distribution to be implemented at the vehicle (400). A learning system (300) updates the estimated merit function values in the data store (350), based on the plurality of samples.

A system, method, and device for operations of an electrically motorized vehicle. The vehicle can utilize an electrically motorized wheel to convert a non-motorized wheeled vehicle to an electrically motorized wheeled vehicle. One system includes a server in communication with the device of each of a plurality of electrically motorized wheels, the server operable to track a position of each of the electrically motorized wheels and communicate the position thereof to a transportation network.

A method for driving dynamics control for a transportation vehicle, wherein a manipulated variable of the driving dynamics is controlled by a control circuit having two degrees of freedom, consisting of a pilot control and a controller, to drive through a planned trajectory, wherein the control circuit has an iteratively learning controller which cyclically repeats classifying the planned trajectory by a classification device, retrieving a manipulated variable profile for the iteratively learning controller from a database based on the classification, recording a control fault of the control circuit and/or a manipulated variable of the controller when driving through the planned trajectory by a memory, and adapting the manipulated variable profile of the iteratively learning controller based on the recorded control fault and/or the recorded manipulated variable of the controller. Also disclosed is an associated device.

A method for operating a motor vehicle incorporates polling regarding the control of the motor vehicle, leading to an improvement in the working conditions of a plurality of evaluation units accessing sensor units of the motor vehicle. Control commands for controlling the motor vehicle are determined from this polling by a conflict checking unit. The conflict checking unit determines the feasibility of the control commands, taking into consideration predetermined verification criteria with regard to conflicts between the individual control commands and the practicability of the individual control commands. The conflict checking unit also determines a control specification for a vehicle control unit based on the feasibilities and certain decision criteria. Finally, the motor vehicle is controlled by use of the vehicle control unit in accordance with the control specification.

Interaction-aware decision making may include training a first agent based on a first policy gradient, training a first critic based on a first loss function to learn goals in a single-agent environment using a Markov decision process, training a number N of agents based on the first policy gradient, training a second policy gradient and a second critic based on the first loss function and a second loss function to learn goals in a multi-agent environment using a Markov game to instantiate a second agent neural network, and generating an interaction-aware decision making network policy based on the first agent neural network and the second agent neural network. The N number of agents may be associated with a driver type indicative of a level of cooperation. When a collision occurs, a negative reward or penalty may be assigned to each agent involved based on a lane priority level of respective agents.

A control system controls a vehicle using a probabilistic motion planner and an adaptive predictive controller. The probabilistic motion planner produces a sequence of parametric probability distributions over a sequence of target states for the vehicle with parameters defining a first and higher order moments. The adaptive predictive controller optimizes a cost function over a prediction horizon to produce a sequence of control commands to one or multiple actuators of the vehicle. The cost function balances a cost of tracking of different state variables in the sequence of the target states defined by the first moments. The balancing is performed by weighting different state variables using one or multiple of the higher order moments of the probability distribution.

A vehicle control data generation method includes causing processing circuitry to execute an obtaining process that obtains a state of a vehicle and a specifying variable, an operating process that operates an electronic device, a reward calculating process that provides a greater reward when a characteristic of the vehicle meets a standard than when the characteristic does not meet the standard, and an updating process that updates relationship defining data. The update map outputs the updated relationship defining data. The reward calculating process includes a changing process that changes the reward, provided when the characteristic of the vehicle is a predetermined characteristic, such that the reward in a case where torque generated by an internal combustion engine is used to generate the propelling force of the vehicle differs from the reward in a case where the torque is not used to generate the propelling force.

In one embodiment, driving faster than a speed limit can be avoided. In response to a current speed being greater than a local speed limit of the vehicle, a vehicle can determine a current vehicle pitch and determine a compensation acceleration to maintain constant velocity of the vehicle at the current vehicle pitch. A threshold control command is determined based on the current speed of the vehicle, and the compensation acceleration. The threshold control command determines whether the vehicle will accelerate or decelerate given the current vehicle pitch of the vehicle. If a driver's control command is greater than the threshold control command, the driver's control command can be overridden or modified to reduce the current speed of the vehicle.

Embodiments of the present disclosure relate to generating velocity profiles for an autonomous vehicle (101). An ECU (107) of the autonomous vehicle (101) receives road information from one or more sensors (106) associated with the autonomous vehicle (101). One or more parameters related to smooth movement of the autonomous vehicle on the road is determined from the road information. Further, a first velocity profile is produced using an AI model and a second velocity profile is produced using a hierarchical model, based on the one or more parameters. Furthermore, one of the first and the second velocity profile is selected by comparing the first and the second velocity profiles. The selected velocity profile has a lower value of velocity value compared to the other velocity profile. The selected velocity profile is provided to the autonomous vehicle (101) for navigating on the road (102) smoothly.