Methods and systems are provided that are effective to generate an alarm for a vehicle. The methods include receiving, by a device, a first sensor value from a first sensor for the vehicle. The methods further include receiving, by the device, a second sensor value from a second sensor for the vehicle. The methods further include retrieving, by the device, an instruction from a memory disposed in the vehicle while the memory is in a write-protected mode. The methods further include evaluating, by the device, the first sensor value and the second sensor value based on the instruction. The methods further include determining, by the device, that the first sensor value is outside a range associated with the first sensor based on the evaluation. The methods further include transforming, by the device, the determination into an alarm.

A simulating method for an electric vehicle (EV) includes creating a simulation model associating a plurality of target behaviors of a target vehicle with the EV, obtaining a plurality of vehicle parameters of the EV to generate a set of EV control parameters, obtaining a plurality of configuration parameters of the target vehicle, based on the set of EV control parameters and the plurality of configuration parameters of the target vehicle, using the simulation model to provide a set of simulated target-vehicle controls, where the simulation model is a neural network trained to reflect a relationship between the set of EV control parameters and the set of simulated target-vehicle controls, and outputting the set of simulated target-vehicle controls to the EV, such that the EV is controlled to achieve the plurality of target behaviors of the target vehicle based on the set of simulated target-vehicle controls.

A method for monitoring a drive-by-wire system of a motor vehicle, including: temporally offset reading in of at least two input values of an input quantity of an operating element of the motor vehicle; ascertaining a change over time or rate of change over time of the input quantity from the at least two read-in input values; determination of a monitored quantity for the motor vehicle operation from the change over time or rate of change over time; selection of a monitoring function on the basis of the monitored quantity; monitoring of the monitored quantity for the ascertained motor vehicle operation by the monitoring function.

A method for monitoring a drive-by-wire system of a motor vehicle, including: temporally offset reading in of at least two input values of an input quantity of an operating element of the motor vehicle; ascertaining a change over time or rate of change over time of the input quantity from the at least two read-in input values; determination of a monitored quantity for the motor vehicle operation from the change over time or rate of change over time; selection of a monitoring function on the basis of the monitored quantity; monitoring of the monitored quantity for the ascertained motor vehicle operation by the monitoring function.

A control device of an automatic drive vehicle includes: an information acquisition unit that acquires power generator information as information on a power generator provided in the automatic drive vehicle; an operation control unit that switches between a first state in which automatic driving of the automatic drive vehicle is executed without restriction and a second state in which the automatic driving is partially or entirely restricted; and a determination unit that determines whether to perform switching to the second state by the operation control unit.

A control device of an automatic drive vehicle includes: an information acquisition unit that acquires power generator information as information on a power generator provided in the automatic drive vehicle; an operation control unit that switches between a first state in which automatic driving of the automatic drive vehicle is executed without restriction and a second state in which the automatic driving is partially or entirely restricted; and a determination unit that determines whether to perform switching to the second state by the operation control unit.

A vehicle control device that: judges a state of a vehicle; structures a plurality of VMs that control equipment installed in the vehicle; and in accordance with the state of the vehicle, switches an order of carrying out activation or stoppage of the plurality of VMs.

A vehicle control device that: judges a state of a vehicle; structures a plurality of VMs that control equipment installed in the vehicle; and in accordance with the state of the vehicle, switches an order of carrying out activation or stoppage of the plurality of VMs.

An automated valet parking system, an automated valet parking method, and an automated valet parking infrastructure, and a vehicle having an automated valet parking feature are disclosed. In particular, the vehicle can autonomously move to and park in a designated parking spot by communicating with the infrastructure. In addition, the vehicle can autonomously move to a pickup area from a parking spot by communicating with the infrastructure.

A computer-implemented method of evaluating the performance of a full or partial autonomous vehicle (AV) stack in simulation, the method comprising: applying an optimization algorithm to a numerical performance function defined over a scenario space, wherein the numerical performance function quantifies the extent of success or failure of the AV stack as a numerical score, and the optimization algorithm searches the scenario space for a driving scenario in which the extent of failure of the AV stack is substantially maximized, wherein the optimization algorithm evaluates multiple driving scenarios in the search space over multiple iterations, by running a simulation of each driving scenario in a simulator, in order to provide perception inputs to the AV stack, and thereby generate at least one simulated agent trace and a simulated ego trace reflecting autonomous decisions taken in the AV stack in response to the simulated perception inputs, wherein later iterations of the multiple iterations are guided by the results of previous iterations of the multiple iterations, with the objective of finding the driving scenario for which the extent of failure of the AV stack is maximized.