A method for vehicle operating session simulation includes receiving, at a server computing device, vehicle data representing characteristics of a vehicle during a vehicle operating session and identifying a first actual value of the vehicle data corresponding to at least one operator input provided during a corresponding segment of the vehicle operating session. The method also includes determining, using the vehicle data, a first expected value corresponding to the first actual value and, in response to a determination that the first actual value is outside of a range of the first expected value, generating a vehicle simulation corresponding to the segment. The method also includes providing, at one or more simulation interfaces, the vehicle simulation.

A method for vehicle operating session simulation includes receiving, at a server computing device, vehicle data representing characteristics of a vehicle during a vehicle operating session and identifying a first actual value of the vehicle data corresponding to at least one operator input provided during a corresponding segment of the vehicle operating session. The method also includes determining, using the vehicle data, a first expected value corresponding to the first actual value and, in response to a determination that the first actual value is outside of a range of the first expected value, generating a vehicle simulation corresponding to the segment. The method also includes providing, at one or more simulation interfaces, the vehicle simulation.

The invention discloses a dual-function electric vehicle, which falls under category of electric go-kart, toy car and ORV. The dual-function electric vehicle includes throttle unit, brake unit, VCU, battery group, steering wheel unit, mode change-over switch, steering wheel, driving wheel, and external PC/game machine. The throttle unit is connected with the VCU for controlling the torque output of the whole vehicle. The brake unit is connected with VCU. The steering wheel unit includes lock switch, the second motor and steering wheel. The second motor outputs to motor shaft at both ends, one end connecting with the steering wheel and the other end with the lock switch. VCU is connected with the second motor electrical signal, and battery group and external PC/game machine are connected to VCU. The dual-function electric vehicle can be converted into two working modes through mode change-over switch. It can be used as electric vehicle and racing simulator. It is simple to operate and multifunctional, reducing production cost.

Implementations generally relate to providing a hyper realistic simulated driving experience. In some implementations, a method includes receiving vehicle control input from a user of a vehicle, where the vehicle control input is based on user interaction with vehicle controls of the vehicle. The method further includes generating a simulated driving experience based on the vehicle control input and based on simulated road conditions, where the simulated driving experience includes visual feedback and motion feedback. The method further includes displaying the visual feedback on a display associated with a frunk of the vehicle. The method further includes controlling the motion of the vehicle based on the vehicle control input and the simulated road conditions.

Implementations generally relate to providing a hyper realistic simulated driving experience. In some implementations, a method includes receiving vehicle control input from a user of a vehicle, where the vehicle control input is based on user interaction with vehicle controls of the vehicle. The method further includes generating a simulated driving experience based on the vehicle control input and based on simulated road conditions, where the simulated driving experience includes visual feedback and motion feedback. The method further includes displaying the visual feedback on a display associated with a frunk of the vehicle. The method further includes controlling the motion of the vehicle based on the vehicle control input and the simulated road conditions.

Implementations generally relate to providing a hyper realistic simulated driving experience. In some implementations, a method includes monitoring, at a media unit, user interaction with vehicle controls of a vehicle, wherein the media unit is configured to provide visual feedback to a user. The method further includes generating a simulated driving experience based on the user interaction with the vehicle controls and based on simulated road conditions. The method further includes displaying the visual feedback on the display, wherein the display is configured to be stored in a frunk of the vehicle when the display is in a retracted position, and wherein the display is configured to be positioned in front of a user when the display is in a protracted position. The method further includes controlling motion of the vehicle based on the vehicle control input and the simulated road conditions.

Implementations generally relate to providing a hyper realistic simulated driving experience. In some implementations, a method includes monitoring, at a media unit, user interaction with vehicle controls of a vehicle, wherein the media unit is configured to provide visual feedback to a user. The method further includes generating a simulated driving experience based on the user interaction with the vehicle controls and based on simulated road conditions. The method further includes displaying the visual feedback on the display, wherein the display is configured to be stored in a frunk of the vehicle when the display is in a retracted position, and wherein the display is configured to be positioned in front of a user when the display is in a protracted position. The method further includes controlling motion of the vehicle based on the vehicle control input and the simulated road conditions.

A racing coach device stores a first path of travel along a racetrack over a first time period and a second path of travel along the racetrack over a second time period. The racing coach device identifies, for each of a plurality of geolocations along the racetrack, one of the first path of travel or the second path of travel that is associated with a shorter duration of time over which the user traversed a segment of the path of travel associated with each of the plurality of geolocations. The device determines an optimal path of travel along the racetrack based on the identified first and second path of travel for each segment of the path of travel at each of the plurality of geolocations that results in a calculated lap time to traverse the racetrack that is less than the first time period and the second time period.

In various examples, a plurality of poses corresponding to one or more configuration parameters within an environment—such as a location of a machine within an environment, an orientation of a machine within an environment, a sensor angle pose of a machine, or a sensor location of a machine—may be used to generate training data and corresponding ground truth data for training a machine learning model—such as a deep neural network (DNN). As a result, the machine learning model, once deployed, may more accurately compute one or more outputs—such as outputs representative of lane boundaries, trajectories for an autonomous machine, etc.—agnostic to machine and/or sensor poses of the machine within which the machine learning model is deployed.

System, methods, and other embodiments described herein relate to a training system to train a driver about occurrences of anomalous driving events of automated vehicle systems. In one embodiment, a method includes determining, upon receiving a selection of a vehicle behavior from one or more anomalous driving events and a detected state change signal, whether the vehicle behavior affects one or more entities. The method includes assessing a state of the one or more entities to simulate the vehicle behavior according to a safety standard. The method includes triggering simulation of the vehicle behavior if the state satisfies a threshold. The method includes simulating the vehicle behavior by at least controlling the vehicle to simulate the vehicle behavior during automated driving of the vehicle.