According to various embodiments, systems, methods, and media for evaluating an open space planner in an autonomous vehicle are disclosed. In one embodiment, an exemplary method includes receiving, at a profiling application, a record file recorded by the ADV while driving in an open space using the open space planner, and a configuration file specifying parameters of the ADV; extracting planning messages and prediction messages from the record file, each extracted message being associated with the open space planner. The method further includes generating features from the planning message and the prediction messages in view of the specified parameters of the ADV; and calculating statistical metrics from the features. The statistical metrics are then provided to an automatic tuning framework for tuning the open space planner.

A vehicle may include a primary system for generating data to control the vehicle and a secondary system that validates the data and/or other data to avoid collisions. For example, the primary system may localize the vehicle, detect an object around the vehicle, predict an object trajectory, and generate a trajectory for the vehicle. The secondary system may localize the vehicle, detect an object around the vehicle, predict an object trajectory, and determine a likelihood of a collision of the vehicle with the object. A simulation system may generate simulation scenarios that test aspects of the primary system and the secondary system. Simulation scenarios may include simulated vehicle control data that causes the primary system to generate a driving trajectory and simulated object data that causes the secondary system to determine a collision.

A method for ascertaining a highly accurate piece of yaw rate information for controlling a vehicle is provided. The method includes ascertaining a first yaw rate estimated value of the vehicle based on a fusion of sensor data of an inertial sensor, a GNSS sensor, a wheel velocity sensor and/or a steering angle sensor; ascertaining a second yaw rate estimated value of the vehicle by an evaluation of sensor data of a camera assigned to the vehicle, which optically detects the surroundings of the vehicle; carrying out a correction of the first yaw rate estimated value with the aid of the second yaw rate estimated value to ascertain a corrected yaw rate estimated value; and outputting the corrected yaw rate estimated value as a piece of yaw rate information to generate a control signal for controlling the vehicle.

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.

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.

A wiring harness design for Hybrid electric vehicles (HEV) or Dual Power vehicles that uses a wiring harness that is operatively coupled to a cloud connected motor controller that detects mode of operation in two-wheelers and continuously transmit telemetry data to a cloud server.

A vehicle includes a storage device configured to store an estimation algorithm configured to output a degree of deterioration of a component mounted on the vehicle in response to an input of a value of a parameter related to the component, a sensor configured to detect the value of the parameter, and a control device. The control device is configured to execute a performance test by autonomous driving of the vehicle, acquire data indicating performance of the component during the performance test, and update the estimation algorithm by using the data acquired during the performance test.