The present disclosure provides a method comprising receiving by an autonomous vehicle (“AV”) service provider a request for an AV service from a user, wherein the request identifies an origin and a destination for the AV service; identifying a plurality of possible routes between the origin and the destination; and, for each one of the plurality of possible routes assigning to the one of the possible routes at least one of a comfort score, a confidence score, and a risk an risk score based on data associated with the one of the possible routes determining a price for the one of the possible routes based on a combination of the at least one of the comfort score, the confidence score, and the risk score assigned to the one of the possible routes.

Examples described herein provide a computer-implemented method that includes receiving, by a processing device, a current state of a vehicle. The method further includes predicting, by the processing device using an output of an artificial intelligence model, a future state of the vehicle based at least in part on the current state of the vehicle. The method further includes calculating, by the processing device using a tunable reward function, a reward associated with the future state of the vehicle, the tunable reward function comprising multiple tunable coefficients. The method further includes training, by the processing device, the artificial intelligence model based at least in part on the reward.

A control device of a vehicle is provided. The device controls travelling of the vehicle such that the vehicle follows another vehicle based on information received from the other vehicle; and determines whether to allow the vehicle to start to follow another vehicle based on at least one of whether a user of the vehicle is inside the vehicle or an instruction from the user. The processor starts to follow another vehicle in a case where the travel control circuit is allowed to follow the other vehicle.

An apparatus for controlling a direction and speed of travel an autonomous vehicle or driver assisted autonomous vehicle (AV). A GPS and map module receive a start location and a destination location for the AV. A plurality of sensors identify a current and a proposed lane for the AV. A database of AV baseline maneuver profiles used to control one or more of direction and speed of travel of the AV is provided. A trajectory profile generator module generates a planned path for the AV with lateral acceleration less than 2 Hz, based on the start location, the destination location, the current and proposed lane for the AV, and a selected AV baseline maneuver profile from the database. A steering control module controls the direction of travel of the AV based on the generated AV planned path, and a supervisory control module controls the speed of the AV based on the generated AV planned path and inner ear constraints.

A method for controlling an autonomous vehicle including: obtaining trip information of a current trip of a user of the autonomous vehicle and historical traffic information related to the current trip; selecting an autonomous driving (AD) mode that is suitable for the current trip from a plurality of pre-defined AD modes; comparing time required for the autonomous vehicle to complete a portion of the current trip in the selected AD mode with historical average time to complete the portion of the current trip, the historical average time being acquired based on the historical traffic information; and dynamically adjusting driving of the autonomous vehicle based on the comparison to minimize the difference between the time required for the autonomous vehicle to complete the portion of the current trip in the selected AD mode and the historical average time.

A travel assistance method is executed by a processor and comprises: acquiring, from a device for storing map information, information on static traveling path boundaries between a traveling path of a subject vehicle and other than the traveling path; acquiring, from a sensor for detecting surrounding environment of the subject vehicle, information on dynamic traveling path boundaries different from the static traveling path boundaries; generating, based on the information on the static traveling path boundaries, a static traveling path on which the subject vehicle can travel; generating, based on the information on the static traveling path and the dynamic traveling path boundaries, a dynamic traveling path which is shorter than the static traveling path and corresponds to the surrounding environment; and controlling the subject to travel along a traveling path including the static traveling path and the dynamic traveling path.

Aspects of the disclosure relate to identifying and adjusting speed plans for controlling a vehicle in an autonomous driving mode. In one example, an initial speed plan for controlling speed of the vehicle for a first predetermined period of time corresponding to an amount of time along a route of the vehicle is identified. Data identifying an object and characteristics of that object is received from a perception system of the vehicle. A trajectory for the object that will intersect with the route at an intersection point at particular point in time is predicted using the data. A set of constraints is generated based on at least the trajectory. The speed plan is adjusted in order to satisfy the set of constraints for a second predetermined period of time corresponding to the amount of time. The vehicle is maneuvered in the autonomous driving mode according to the adjusted speed plan.

Embodiments for operational envelope detection (OED) with situational assessment are disclosed. Embodiments herein relate to an operational envelope detector that is configured to receive, as inputs, information related to sensors of the system and information related to operational design domain (ODD) requirements. The OED then compares the information related to sensors of the system to the information related to the ODD requirements, and identifies whether the system is operating within its ODD or whether a remedial action is appropriate to adjust the ODD requirements based on the current sensor information. Other embodiments are described and/or claimed.

A method is provided for automated application (10) of a driver assistance system that is configured to implement automated driving functions. At least one application parameter is assigned to each automated driving function (12). Factory settings preset both the application parameter and acceptable ranges for the application parameters that are consistent with safety-critical requirements. A control unit identifies a relevant driving scenario after an automated driving function (12) has been implemented during normal driving operation (13, 14). The control unit uses an objective grading model to evaluate (17) a performance of each implemented automated driving function while continuing execution during a normal driving operation (13, 14). The at least one respectively assigned application parameter is adapted (15), as a result of an optimization (11), on the basis of the evaluation (17) of the performance of the respectively implemented automated driving function (12).

Provided are autonomous vehicles (AV), computer program products, and methods for maneuvering an AV in a roadway, including receiving forecast information associated with predicted trajectories of one or more actors in a roadway, determining a relevant trajectory of an actor based on correlating a forecast for predicted trajectories of the actor with the trajectory of the AV, regenerate a distance table for the relevant trajectory previously generated for processing constraints, generate a plurality of margins for the AV to evaluate, the margins based on a plurality of margin types for providing information about risks and effects on passenger comfort associated with a future proximity of the AV to the actor, classifying an interaction between the AV and the actor based on a plurality of margins, and generating continuous scores for each candidate trajectory that is also within the margin of the actor generated for the relevant trajectory.