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.

According to an exemplary method, a smoothing module can be used in an ADV to iteratively perform a smoothing operation on a raw reference segment using an ordered list of sets of smoothing parameters, starting from the set of strictest parameters, until the smoothing operation is successful. The method includes the operations of generating multiple sets of smoothing parameters, including a first set of smoothing parameters, a second set of smoothing parameters, and at least one set of smoothing parameters interpolated in between; for each set of smoothing parameters, performing a quadratic programming (QP) smoothing operation on the raw reference line segment until the QP smoothing operation is successful; and controlling the ADV according to a smoothed reference line segment generated by the successful QP smoothing operation.

Techniques are provided for autonomous vehicle operation using linear temporal logic. The techniques include using one or more processors of a vehicle to store a linear temporal logic expression defining an operating constraint for operating the vehicle. The vehicle is located at a first spatiotemporal location. The one or more processors are used to receive a second spatiotemporal location for the vehicle. The one or more processors are used to identify a motion segment for operating the vehicle from the first spatiotemporal location to the second spatiotemporal location. The one or more processors are used to determine a value of the linear temporal logic expression based on the motion segment. The one or more processors are used to generate an operational metric for operating the vehicle in accordance with the motion segment based on the determined value of the linear temporal logic expression.

A vehicle performance evaluation method, device and terminal are provided. The method includes: acquiring a labeled ADE score of an ADE item within a time period in which the ADE item occurs, and recording labeled data of an ADE index for indicating a vehicle state; acquiring a correlation between the ADE item and the vehicle state, according to the labeled ADE score and the labeled data of the ADE index; acquiring data of a target ADE index within a preset time period, and acquiring a target ADE score according to the data of the target ADE index and a correlation between the ADE item and the vehicle state; and acquiring a vehicle performance evaluation result according to the target ADE score. The passenger participation is not necessary, costs can be reduced and a vehicle evaluation efficiency can be improved.

An apparatus and a method for controlling a velocity of an autonomous driving vehicle is provided. The method includes steps of: obtaining information of an environment surrounding the vehicle when an obstacle is detected to be on a planning path of the vehicle; obtaining an initial reference velocity profile of the vehicle; determining a safety factor based on the initial reference velocity profile, the information of the environment and information of the vehicle, wherein the safety factor at least comprises a safety distance between the vehicle and the obstacle for the vehicle to follow the obstacle; determining an optimized reference velocity profile based on the information of the environment, the information of the vehicle and the safety factor; and performing the step of determining the safety factor by using the optimized reference velocity profile as the initial reference velocity profile and the step of determining the optimized reference velocity iteratively.

Techniques are discussed for generating and optimizing a trajectory using closed-form numerical integration in route-relative coordinates. A decision planner component of an autonomous vehicle, for example, can receive or generate a reference trajectory, which may correspond to an ideal route for an autonomous vehicle to traverse through an environment, such as a center of a road segment. Lateral dynamics (e.g., steering angles, curvature values of trajectory segments) and longitudinal dynamics (e.g., velocity and acceleration) can be represented in a single algorithm such that optimizing the reference trajectory (e.g., based on loss functions or costs) can substantially simultaneously optimize the lateral dynamics and longitudinal dynamics in a single convergence operation. In some cases, the trajectory can be used to control the autonomous vehicle to traverse an environment.

A mobile ecosystem includes an autonomous control system in a vehicle. The autonomous control system receives user information from at least one of a user device or the vehicle and identifies a trajectory plan and a vehicle configuration based on the user information. The trajectory plan identifies a departure time for the vehicle to depart a starting location, an arrival time for the vehicle to arrive at a destination location, and a travel route for the vehicle to traverse at least partially between the starting location and the destination location. The trajectory plan may identify an entry time for the user to enter the vehicle. The vehicle configuration identifies travels settings for components in at least one of dynamic systems or interior systems of the vehicle. The autonomous control system causes the components to be configured according to the travel settings and causes the vehicle to traverse the travel route.

An example operation includes one or more of providing, by a transport, a second location for pick-up proximate a first location, wherein the second location is based on a prospective occupant's ability to access the transport, and sending, by the transport, a notification to a device associated with the prospective occupant containing the second location.

A control device is used in a subject vehicle capable of performing autonomous driving with no obligation for a driver to monitor periphery. The control device determines whether a permission state, in which a specific act other than driving is permitted to the driver, is continued or not when approach of an emergency vehicle to the subject vehicle is detected during an autonomous cruising period in which the autonomous driving is being performed. The control device restricts display of a content provided to the driver when the permission state of the specific act is determined to be not continued.

The present disclosure provides a method comprising identifying at least one of a characteristic and an identity of an item for delivery from an origin to a destination and selecting one of a plurality of possible routes between the origin and the destination. For each of a plurality of route segments of the selected route, mapping information is used to characterize the route segment and one of a plurality of driving modes is selected for the route segment based on the characterization of the route segment and the at least one of the item characteristic and the item identity. A driving plan comprising a collection of the selected driving modes corresponding to the plurality of route segments comprising the selected route is provided to a vehicle and the vehicle delivers the item from the origin to the destination via the selected route using the driving plan.