The present teaching relates to method, system, medium, and implementation of human-like vehicle control for an autonomous vehicle. Recorded human driving data are first received, which include vehicle state data, vehicle control data, and environment data. For each piece of recorded human driving data, a vehicle kinematic model based vehicle control signal is generated in accordance with a vehicle kinematic model based on a corresponding vehicle state and vehicle control data of the piece of recorded human driving data. A human-like vehicle control model is obtained, via machine learning, based on the recorded human driving data as well as the vehicle kinematic model based vehicle control signal generated based on vehicle kinematic model. Such derived human-like vehicle control model is to be used to generate a human-like vehicle control signal with respect to a target motion of an autonomous vehicle to achieve human-like vehicle control behavior.

A system architecture of an autonomous vehicle, wherein the system architecture includes a plurality of application instances and nodes. The application instances are distributed and executed on the computation nodes according to a configuration, wherein measured sensor data of at least one sensor is input to at least part of the application instances, and wherein at least part of the application instances creates and provides control signals for controlling the vehicle. At least one context information of a prevailing context is gathered, and wherein the configuration is adapted according to the at least one gathered context information.

The present teaching relates to method, system, medium, and implementation of human-like vehicle control for an autonomous vehicle. Information related to a target motion to be achieved by the autonomous vehicle is received, wherein the information includes a current vehicle state of the autonomous vehicle. A first vehicle control signal is generated with respect to the target motion and the given vehicle state in accordance with a vehicle kinematic model. A second vehicle control signal is generated in accordance with a human-like vehicle control model, with respect to the target motion, the given vehicle state, and the first vehicle control signal, wherein the second vehicle control signal modifies the first vehicle control signal to achieve human-like vehicle control behavior.

A system, vehicle and method are provided that automatically activate and deactivate a driver assistance system. The automatic activation and deactivation depend on predefined criteria such as brake or gas pedal release, current vehicle velocity, and the presence of a lead vehicle.

A vehicle, system for operating the vehicle and method of activating a control system is disclosed. The system includes a sensor for detecting lane markings of a driving lane, a road wheel angle sensor configured to measure a road wheel angle of the vehicle, and a processor. The processor is configured to determine a distance of the vehicle from a central line of a driving lane form the detected lane markings, adjust an angular threshold for activation of the control system of the vehicle based on the determined distance, and activate the control system when the road wheel angle is within the angular threshold.

The present disclosure is directed to controlling state transitions in an autonomous vehicle. In particular, a computing system can initiate the autonomous vehicle into a no-authorization state upon startup. The computing system can receive an authorization request. The computing system determines whether the authorization request includes a request to enter the first or second mode of operations, wherein the first mode of operations is associated with the autonomous vehicle being operated without a human operator and the second mode of operations is associated with the autonomous vehicle being operable by a human operator. The computing system can transition the autonomous vehicle from the no-authorization state into a standby state in response to determining the authorization request includes a request to enter the first mode of operations or into a manual-controlled state in response to determining the authorization request is a request to enter the second mode of operations.

A vehicle is capable of efficient autonomous driving by changing the detection range and power consumption of a sensor according to the speed of the vehicle. The vehicle includes: an information acquirer configured to acquire vehicle surround information; a speed sensor configured to acquire vehicle speed; and a controller configured to determine a vehicle stopping distance based on the vehicle speed and to determine a detection area for acquiring the vehicle surround information by the information acquirer based on the stopping distance and a risk level for each sensor channel The detection area includes the stopping distance relative to the vehicle.

A system and methods thereof for monitoring proper behavior of an autonomous vehicle are provided. The method includes generating a plurality of agents, wherein each of the plurality of agents describes a physical object, wherein at least one of the plurality of agents is an agent for the DUT, generating a plurality of scenarios, wherein each scenario models a behavior of at least one of the plurality of agents, and monitoring an interaction between the plurality of agents and the DUT agent for a scenario modeling the respective agent.

According to a method for learning travel characteristics and a travel assistance device, in a vehicle capable of switching manual driving by a driver and autonomous-driving, continuity of driving characteristics is determined based on travel data during manual driving by a driver, and a start time and an end time of learning target data, being a learning target of the driving characteristics of the travel data, are set by using a determination result of the continuity.

An electronic control unit includes a first process flag setting unit that is configured to set a first process flag indicative of a progress of a first process that is one of a series of processes related to a program rewrite, a second process flag setting unit that is configured to set a second process flag indicative of a progress of a second process that is an other of the series of processes related to the program rewrite, and a retry point specifying unit that is configured to specify, based on the first process flag and the second process flag, a retry point for resuming the program rewrite when the program rewrite is suspended. The retry point specifying unit is further configured to store an amount of the update data that has been written until the program rewrite was suspended and request the vehicle master device to transfer the update data based on the stored amount of the update data when resuming the program rewrite.