A computer implemented method for scenario generation for autonomous vehicle navigation that can include defining a cellular automaton layer that defines a road network level behavior with at least one rule directed to pathways by vehicles on a passageway for travel. The method may further include defining an active matter layer that defines a vehicle level behavior with at least one rule directed to movement of the vehicles on an ideal route for the pathways; and defining a driver agent layer that defines driving nature with at least one rule that impacts changes in the vehicle level behavior dependent upon a characterization of driver behavior. The method may further include combining outputs from the different layer to provide scenario generations for autonomous vehicle navigation. The combining of the outputs can utilize a pseudo random value to determine at an order in the execution and duration of execution for the layers.

The disclosure relates collecting feedback from passengers of autonomous vehicles. For instance, that a triggering circumstance for triggering a feedback request has been met may be determined. The triggering circumstance may include a driving event, a presence of other road users, or a trip state. A display requirement and data collection parameters for the feedback request are identified based on the determination. The display requirement defines when the feedback request is displayed and the data collection parameters identify information that the feedback request is to collect. The feedback request is provided for display based on the display requirement and data collection parameters. In response, feedback from a passenger of the autonomous vehicle is received and stored for later use.

A computer-implemented method of evaluating the performance of a target planner for an ego robot in a real or simulated scenario, the method comprising: receiving evaluation data for evaluating the performance of the target planner in the scenario, the evaluation data generated by applying the target planner at incrementing planning steps, in order to compute a series of ego plans that respond to changes in the scenario, the series of ego plans being implemented in the scenario to cause changes in an ego state the evaluation data comprising: the ego plan computed by the target planner at one of the planning steps, and a scenario state at a time instant of the scenario, wherein the evaluation data is used to evaluate the target planner by: computing a reference plan for said time instant based on the scenario state, the scenario state including the ego state at that time instant as caused by implementing one or more preceding ego plans of the series of ego plans computed by the target planner, and computing at least one evaluation score for comparing the ego plan with the reference plan.

An automated driving assistance apparatus includes an occupant's emotion learning section and a control parameter setting section. The occupant's emotion learning section creates an occupant's emotion model based on vehicle driving state information and occupant's emotion information. The occupant's emotion model is used to estimate an emotion of the occupant from a vehicle driving state. The control parameter setting section calculates an ideal driving state based on the occupant's emotion model, and set a control parameter for automated driving of the vehicle based on the ideal driving state. The control parameter setting section output input values relevant to the vehicle driving state to the occupant's emotion model, and select, from the input values received by the occupant's emotion model, an input value that causes a current occupant's emotion to become closer to a target emotion as the ideal driving state of the vehicle.

A system for controlling an autonomous vehicle includes a memory configured to store parameters of a g-g plot defining admissible space of values of longitudinal and lateral accelerations. The g-g plot parameters define a mapping between user driving preferences and constrained control of the autonomous vehicle. The g-g plot parameters include a maximum forward acceleration, a maximum backward acceleration, a maximum lateral acceleration and a shape parameter defining profile of curves connecting maximum values of forward, backward, and lateral accelerations. The system accepts a comfort level as a feedback from a passenger of the vehicle, determines a dominant parameter corresponding to the feedback, updates the dominant parameter of the g-g plot based on the comfort level indicated in the feedback, and controls the vehicle to maintain dynamics of the vehicle within the admissible space defined by the parameters of the updated g-g plot.

In one embodiment, a method by a computing system of a vehicle includes determining an environment of the vehicle. The method includes generating, based on the environment, multiple proposed vehicle actions with associated operational data. The method includes determining a comfort level for each proposed vehicle action by processing the environment and operational data using a model for predicting comfort levels of vehicle actions. The model is trained using records of performed vehicle actions. The record for each performed vehicle action includes environment data, operational data, and a perceived passenger comfort level for the performed vehicle action. The method includes selecting a vehicle action from the proposed vehicle actions based on the determined comfort level. The method includes causing the vehicle to perform the selected vehicle action.

A vehicle travel control device executes vehicle travel control such that a vehicle follows a target trajectory. An automated driving control device generates a first target trajectory that is the target trajectory for automated driving of the vehicle. The vehicle travel control device further determines whether or not an activation condition of travel assist control is satisfied. When the activation condition is satisfied, the vehicle travel control device generates a second target trajectory that is the target trajectory for the travel assist control. When the second target trajectory is generated during the automated driving, or when the second target trajectory is generated during the automated driving and a priority condition for giving priority to the second target trajectory is satisfied, the vehicle travel control device executes the vehicle travel control by giving more weight to the second target trajectory than to the first target trajectory.

A system and method for controlling an autonomous vehicle to affect a view seen by an occupant of the autonomous vehicle is described. In one embodiment, a method for controlling an autonomous vehicle to affect a view seen by an occupant of the autonomous vehicle includes determining a navigation route, determining content associated with the navigation route, monitoring current conditions of the autonomous vehicle and the occupant, determining, based on the current conditions, whether to change a position of the vehicle to affect the view seen by the occupant, and when the current conditions permit, moving the autonomous vehicle to affect the view seen by the occupant.

Devices, systems, and methods for integrated predictive dynamic control of a vehicle powertrain in an autonomous vehicle are described. An example method for controlling a vehicle includes generating, based on performing an optimization on a blended smooth wheel domain fuel consumption map subject to a modified torque availability constraint, one or more wheel domain control commands, converting the one or more wheel domain control commands to one or more powertrain-executable engine domain control commands, and transmitting the one or more powertrain-executable engine domain control commands to a powertrain of the vehicle, the powertrain configured to operate a plurality of gears, wherein the one or more powertrain-executable engine domain control commands enable the vehicle to track a reference kinematic trajectory associated with a vehicle speed driving plan within a predetermined tolerance.

A vehicle control system includes a recognizer configured to recognize a surrounding situation of a vehicle, a driving controller configured to perform driving control on at least one of steering and a speed of the vehicle on the basis of a recognition result of the recognizer, an environment controller configured to control an operation of a predetermined device for providing a comfortable environment of the vehicle and limit an operation state of the predetermined device at a timing when a user gets out of the vehicle, and a reproducer configured to reproduce the operation state of the predetermined device at a timing when the user gets into the vehicle when the driving controller performs driving control for moving the vehicle from a parking area and picking up the user.