In one embodiment, when an autonomous driving vehicle (ADV) is parked, the ADV can determine, based on criteria, whether to operate in an open-space mode or an on-lane mode. The criteria can include whether the ADV is within a threshold distance and threshold heading relative to a vehicle lane. If the criteria are not satisfied, then the ADV can enter the open-space mode. While in the open-space mode, the ADV can maneuver it is within the threshold distance and the threshold heading relative to the vehicle lane. In response to the criteria being satisfied, the ADV can enter and operate in the on-lane mode for the ADV to resume along the vehicle lane.

Embodiments described herein include a method of receiving, by a moving assisting vehicle, a calibration assistance request related to a moving ego vehicle that requested assistance in collaborative calibration of a sensor deployed on the moving ego vehicle. The method further includes analyzing the calibration assistance request to extract at least one of a schedule or an assistance route associated with the requested assistance. The method includes communicating with the moving ego vehicle about a desired location relative to the position of the moving ego vehicle for the moving assisting vehicle to be in order to assist the sensor to acquire information of a target present on the moving assisting vehicle. The method includes facilitating to drive the moving assisting vehicle to reach the desired location to achieve the collaborative calibration of the sensor on the moving ego vehicle.

A system for determining and executing an autonomous-vehicle vehicle travel route, including a hardware-based processing unit and a non-transitory computer-readable storage medium. The storage medium includes an input-interface module that, when executed by the hardware-based processing unit, obtains factor data indicating factors relevant to determining a vehicle travel route. The storage medium also includes a route-generation module comprising a route-complexity sub-module. The route-complexity sub-module determines, based on the factor data, route-complexity indexes corresponding to respective optional routes. The route-generation module determines the vehicle travel route based on the route-complexity indexes. The storage in various embodiments includes other sub-modules associated with other elements, such as autonomous-driving safety, comfort, stress, pollution, scenery, or infrastructure-accessibility, for determining and executing an autonomous-driving travel route. In some embodiments, the storage includes an autonomous-driving perceptions module and an autonomous-driving control module for modifying vehicle functions in executing the autonomous-driving travel route.

According to various embodiments, a vehicle may be provided. The vehicle may include: a sensor configured to sense a marking, wherein the marking is at least substantially not visible to the human eye under a natural lighting condition; and a localization circuit configured to determine a location of the vehicle based on the sensing of the sensor.

A target-orientated navigation system and related method for a vehicle having a generic navigation system includes one or more processors and a memory. The memory includes one or more modules that cause the processor to receive perception data, discretize the perception data into a plurality of lattices, generate a collision probability array having a plurality of cells that correspond to the plurality of lattices, determine which cells of the collision probability array satisfy a safety criteria, receive an artificial potential field array having a plurality of cells that correspond to the plurality of cells of the collision probability array, generate, an objective score array having a plurality of cells corresponding to the cells of the collision probability array, and direct a vehicle control system of the vehicle to guide the vehicle to a location representative of a cell in the objective score array that has a highest value.

A computer-implemented method of adaptively controlling an autonomous operation of a vehicle is provided. The method includes steps of (a) in a critic network in a computing system configured to autonomously control the vehicle, determining, using samples of passively collected data and a state cost, an estimated average cost, and an approximated cost-to-go function that produces a minimum value for a cost-to-go of the vehicle when applied by an actor network; and (b) in an actor network in the computing system and operatively coupled to the critic network, determining a control input to apply to the vehicle that produces the minimum value for the cost-to-go, wherein the actor network is configured to determine the control input by estimating a noise level using the average cost, a cost-to-go determined from the approximated cost-to-go function, a control dynamics for a current state of the vehicle, and the passively collected data.

Embodiments herein can determine an optimal route for an autonomous electric vehicle. The system may score viable routes between the start and end locations of a trip using a numeric or other scale that denotes how viable the route is for autonomy. The score is adjusted using a variety of factors where a learning process leverages both offline and online data. The scored routes are not based simply on the shortest distance between the start and end points but determine the best route based on the driving context for the vehicle and the user.

A computer-implemented method of managing a fleet of autonomous vehicles is provided. The method comprises receiving at least one request for a passenger journey from a client device associated with a passenger and allocating an autonomous vehicle to the passenger from the fleet. Confirmation that the autonomous vehicle has reached the passenger is then received and one or more vehicle status signals are received from the autonomous vehicle. Subsequently, the autonomous vehicle is authorised to proceed with the passenger journey in dependence on the vehicle status signals.

Systems, methods, tangible, non-transitory computer-readable media, and devices for configuration of a vehicle compartment are provided. For example, a method can include receiving, by a computing system, occupancy data based in part on one or more states of one or more objects. Based in part on the occupancy data and compartment data, a compartment configuration can be determined for one or more compartments of an autonomous vehicle. The compartment data can be based in part on a state of the one or more compartments. The compartment configuration can specify one or more spatial relations of one or more compartment components associated with the one or more compartments. One or more configuration signals can be generated based in part on the compartment configuration to control the one or more compartments of the autonomous vehicle.

According to an embodiment, a mobile object management device for managing a ridable mobile object that a user is allowed to get on and which moves inside of a prescribed area includes an acquirer configured to acquire location information of the ridable mobile object, a manager configured to manage the ridable mobile object and a terminal device of the user on the ridable mobile object in association with each other, and an event operation instructor configured to cause the ridable mobile object to execute a prescribed operation corresponding to an event via the terminal device of the user on the basis of the location information and information about the event that is executed inside of the prescribed area. The manager manages whether or not to permit participation in the event of the user on the basis of a state in which the user uses the ridable mobile object.