An autonomous driving controller includes: a processor to collect driving data when a vehicle is traveling and calculate a steering override reference value, which is a criterion of determining an override mode, based on the collected driving data; and a storage to store the collected driving data and a set of instructions executed by the processor to calculate the steering override reference value. In particular, the processor controls autonomous driving by varying the steering override reference value based on the collected driving data or information regarding a driver of the vehicle.

Determining whether another entity is coordinating with an autonomous vehicle and/or to what extent the other entity's behavior is based on the autonomous vehicle may comprise determining a collaboration score and/or negotiation score based at least in part on sensor data. The collaboration score may indicate an extent to which the entity is collaborating with the autonomous vehicle to navigate (e.g., a likelihood that the entity is increasingly yielding the right of way to the autonomous vehicle based on the autonomous vehicle's actions). A negotiation score may indicate an extent to which behavior exhibited by the entity is based on actions of the autonomous vehicle (e.g., how well the autonomous vehicle and the entity are communicating with their actions).

A computer-implemented method of determining a series of control signals for controlling an autonomous vehicle to implement a planned speed change maneuver comprises: receiving from a maneuver planner a position target for the planned speed change maneuver; selecting, from a predetermined family of kinematic functions, a kinematic function for carrying out the planned speed change maneuver, each kinematic function being a first or higher order derivative of acceleration with respect to time; and using the selected kinematic function to determine a series of control signals for implementing the planned speed change maneuver; wherein the kinematic function is selected in a constrained optimization process as substantially optimizing a cost function defined for the speed change maneuver, subject to a set of hard constraints that: (i) require a final acceleration, speed and position corresponding to the selected kinematic function to satisfy, respectively, an acceleration target, a speed target and the position target, given an initial speed and acceleration of the autonomous vehicle, and (ii) impose a jerk magnitude upper limit on the selected kinematic function.

Techniques are disclosed for creating and presenting a graphical user interface for display of autonomous vehicle behaviors. The techniques include determining a trajectory of a vehicle operating in a real-world environment. Sensors of the vehicle obtain sensor data representing an object in the real-world environment. A maneuver of the vehicle to avoid a collision with the object is predicted based on the sensor data and the trajectory of the vehicle. It is determined that a passenger comfort level of a passenger riding in the vehicle will decrease based on the maneuver of the vehicle. The passenger comfort level is measured by passenger sensors of the vehicle. A graphical user interface is generated including representations of the vehicle, the object, and a graphic, text, or a symbol alerting the passenger of the predicted maneuver. The graphical user interface is transmitted to a display device of the vehicle.

The disclosed computer-implemented method may include monitoring, during a ride provided by an autonomous vehicle (AV), passenger communications between a passenger and a remote agent using an in-vehicle electronic device. The method may further include identifying passenger ride preferences based on a passenger request, and in association with a first occurrence of a ride event. The method may also include providing confirmation, via the in-vehicle electronic device, that the request is being fulfilled, the fulfillment of which causes a change in the passenger's AV experience based upon changes to features of the AV. The method may further include generating, based upon the request, a prediction of passenger ride preferences for the passenger and then, during a subsequent AV ride carrying the passenger, applying the predicted passenger ride preferences to an AV during a second occurrence of the ride event. Various other methods, systems, and computer-readable media are also disclosed.

An approach is provided for using a passenger-based driving profile. The approach involves detecting an identity of a user of a vehicle. The approach also involves retrieving a passenger profile of the user based on the identity. The passenger profile is generated based on sensor data indicating a reaction of the user to at least one vehicle driving behavior previously experienced by the user as a vehicle passenger. The approach further involves configuring the vehicle based on the passenger profile.

Aspects of adaptive trust calibration may include receiving a trust model for an occupant of an autonomous vehicle calculated based on occupant sensor data and a first scene context sensor data, and/or receiving a second scene context sensor data associated with an environment of the autonomous vehicle, determining an over trust scenario or an under trust scenario based on the trust model and a trust model threshold, and generating and implementing a human machine interface (HMI) action or a driving automation action based on the determination of the over trust scenario or the determination of the under trust scenario, and/or the second scene context sensor data.

Systems and methods for clustering human trust dynamics are provided. In one embodiment, a computer implemented method for clustering human trust dynamics is provided. The computer implemented method includes receiving trust data for a plurality of participants interacting with one or more agents in an interaction. The computer implemented method also includes identifying a plurality of phases for the interaction. The computer implemented method further includes extracting features characterizing trust dynamics from the trust data for at least one interaction for each participant of the plurality participants. The at least one interaction is between the participant and an agent of the one or more agents. The computer implemented yet further includes assigning the features characterizing trust dynamics to a phase of the plurality of phases. The computer implemented method includes grouping a subset of the participant of the plurality of participants based on the on features characterizing trust dynamics.

A motion planning system includes: a processor; and memory to store instructions that when executed by the processor, cause the processor to: identify a reference path between a departure point and a destination point in an environment including one or more obstacles; generate decomposition segments of a space surrounding the reference path, the decomposition segments including a first free-space segment and a second free-space segment that are devoid of the obstacles; generating a first path segment relative to the reference path for traversing the first free-space segment, and a second path segment relative to the reference path for traversing the second free-space segment; and connecting the first and second path segments to each other to generate a navigational path to traverse the environment.

A method for controlling an autonomous vehicle includes using one or more computing devices to transmit a request for a trip. The trip is from a pickup location to a destination location. The method also includes determining the autonomous vehicle for the trip is within a predetermined distance from the pickup location, providing a set of component controls to receive user input at a user interface after the determining. The set of component controls includes interactive controls for identifying or accessing the autonomous vehicle. A first user input is received at the user interface for one or more of the set of component controls, and control instructions for the autonomous vehicle based on the first user input are transmitted.