A method is described and includes subsequent to an autonomous vehicle becoming immobilized, initiating a local assistance request; subsequent to the initiating, receiving local assistance input from a passenger of the autonomous vehicle; and using the local assistance input to determine an action to be taken by the autonomous vehicle to mobilize the autonomous vehicle.

In one embodiment, a steering control delay is measured, where the steering delay represents the delay between the time of issuing a steering control command and the time of a response from one or more wheels of an autonomous vehicle. A speed control delay is measured between the time of issuing a speed control command and the time of a response from one or more wheels of the autonomous vehicle or the time of supplying pressure to the gas pedal or brake pedal. In response to a given route subsequently, an overall system delay is determined based on the steering control delay and the speed control delay using a predetermined algorithm. Planning and control data is generated in view of the system delay for operating the autonomous vehicle.

Described embodiments include a self-propelled vehicle, method, and system. The self-propelled vehicle includes an autonomous driving system configured to dynamically determine maneuvers operating the vehicle along a route in an automated mode without continuous input from a human driver. The vehicle includes an input device configured to receive a real-time request for a specific dynamic maneuver by the vehicle operating along the route from the human driver. The vehicle includes a decision circuit configured to select a real-time dynamic maneuver by arbitrating between (i) the received real-time request for the specific dynamic maneuver from the human driver and (ii) a real-time determination relative to the specific dynamic maneuver received from the autonomous driving system. The vehicle includes an implementation circuit configured to output the selected real-time dynamic maneuver to an operations system of the vehicle.

A method of determining a traveling state of a vehicle, such as passing over a speed bump, occurrence of wheel slip, or traveling on a slope, is determined in real time to prevent degradations in wheel slip control performance and to avoid unnecessarily malfunctions in a traction control system without compromise of wheel slip control performance. The method includes steps of: determining a torque command of a drive unit to apply torque to a drive wheel in accordance with vehicle driving information collected during traveling of the vehicle; determining an acceleration error in accordance with the determined torque command and information regarding a measured longitudinal acceleration of the vehicle measured by a first sensor; determining an acceleration disturbance rate in accordance with the determined torque command; and determining a current traveling state of the vehicle in accordance with the determined acceleration error and the determined acceleration disturbance rate.

A driver command interpreter system for a vehicle includes one or more controllers that execute instructions to receive a plurality of dynamic variables, vehicle configuration information, and driving environment conditions, and determine a target vehicle state during transient driving conditions based on the plurality of dynamic variables from the one or more sensors, the vehicle configuration information, and the driving environment conditions. The one or more controllers build a transient vehicle dynamic model based on the target vehicle state during transient driving conditions, the plurality of dynamic variables, the vehicle configuration information, and the driving environment conditions, and solve for desired zeros corresponding to the target vehicle state during transient conditions.

A method is for manufacturing a controller for a control section including a rack of a steering system of a vehicle. The method includes providing measurements characterizing an actual behavior of the control section at different operating points, and specifying a target behavior of the rack. The method also includes determining functions modeling a deviation of the actual behavior from the target behavior at a respective operating point for each one of the different operating points depending on the measurements. At least one parameter of the controller affects an actual transmission behavior of the control section. The at least one parameter includes a proportional gain factor, an integral gain factor, and/or a differential gain factor of the controller, and is determined depending on the deviations determined for the different operating points, such that the control section has a specified target transmission behavior.

In one embodiment, a microcontroller unit (MCU) receives an expected state of an autonomous driving vehicle (ADV) from a controller of the ADV, where the controller controls motions of the ADV using a control algorithm. The MCU receives sensor data from one or more sensors of the ADV. The MCU determine an actual state of the ADV based on the sensor data. The MCU determines a performance metric of the control algorithm based on the expected state and the actual state. In response to determining the performance metric has satisfied a predetermined condition, the MCU determines a plurality of weight values for the control algorithm. The MCU sends the plurality of weight values to the control system to tune one or more weight parameters of the control algorithm using the plurality of weight values, where the controller controls the ADV using the tuned control algorithm.

A processor unit is configured for determining target torque values (21), which lie within a prediction horizon (20), and target speed values (19), which lie within the prediction horizon (20), by executing an MPC algorithm, which includes a longitudinal dynamics model of a drive train of the motor vehicle. An autonomous driving function of the motor vehicle is carried out in a torque specification operating mode or in a speed specification operating mode as a function of the level of the target torque values (21). In the torque specification operating mode, a prime mover of the drive train is controlled by an open-loop system based on the target torque values (21). In the speed specification operating mode, a speed governor of the drive train is controlled by an open-loop system based on the target speed values (19).

Described herein are examples of non-contact systems and methods for managing door access. In an example, a door access device can comprise an array of sensors for detecting one or more movements or poses of an individual in proximity to a door. The device can have a first array of sensors located on a first side of the door associated with a first priority, and a second array of sensors located on a second side of the door associated with a second priority lower than the first priority. In use, requests associated with movements or poses detected by the first array of sensors can be given priority over requests associated with movements or poses detected by the second array of sensors.

An apparatus for controlling an autonomous vehicle disclosure may include a processor and a memory configured to be operatively connected to the processor and to store at least one code performed in the processor, wherein the memory may store a code that, when executed by the processor, causes the processor to control the autonomous vehicle to travel on the basis of a distance from a preceding vehicle in a travel lane in which the autonomous vehicle travels or a preset speed, determine a risk level of a lane change on the basis of a speed of the autonomous vehicle, a speed of a side vehicle traveling in a target lane of a lane change, and a distance between the autonomous vehicle and the side vehicle upon occurrence of a lane change request, and perform longitudinal or lateral control for the lane change on the basis of the risk level.