A passenger may be rather vulnerable to safety risks during pickup and/or drop-off of a passenger by a vehicle. To mitigate or eliminate such risk, the vehicle may determine an endpoint for a vehicle route to pickup or drop-off a passenger at a location. The vehicle may determine an estimated path between the endpoint and the location and may determine a safety confidence score by a machine-learned model for the estimated path and/or may predict a trajectory of a detected object to ascertain whether the estimated path is safe. The vehicle may execute any of a number of different mitigation actions to reduce or eliminate a safety risk if one is detected.

A battery management system includes: a battery capacity division section that virtually divides a capacity of a battery mounted on a mobile object into a predetermined number of areas; a capacity change reception section that receives a capacity change request for requesting a change in a capacity of a first area allocated to a first user be changed; a capacity change adjustment section that, when the capacity change request is received, determines whether or not, a capacity of a second area allocated to a second user other than the first user can be changed according to the capacity change request; and an area capacity change section that, when the capacity change adjustment section determines that the capacity of the second area can be changed according to the capacity change request, changes the capacities of the first area and the second area according to the capacity change request.

A passenger may be rather vulnerable to safety risks during pickup and/or drop-off of a passenger by a vehicle. To mitigate or eliminate such risk, the vehicle may determine an endpoint for a vehicle route to pickup or drop-off a passenger at a location. The vehicle may determine an estimated path between the endpoint and the location and may determine a safety confidence score by a machine-learned model for the estimated path and/or may predict a trajectory of a detected object to ascertain whether the estimated path is safe. The vehicle may execute any of a number of different mitigation actions to reduce or eliminate a safety risk if one is detected.

Various embodiments relate generally to autonomous vehicles and associated mechanical, electrical and electronic hardware, computer software and systems, and wired and wireless network communications to provide an autonomous vehicle fleet as a service. In particular, a method may include receiving first sensor data from a first sensor disposed on a vehicle, the first sensor data associated with a first sensor modality, and receiving second sensor data from a second sensor disposed on the vehicle, the second sensor data associated with a second sensor modality different than the first sensor modality. The method may further include generating fused sensor data representing at least a portion of the first sensor data and the second sensor data, generating a trajectory for the vehicle based in part on the fused sensor data, and controlling the vehicle based in part on the trajectory.

When an online system receives an order from a customer, the online system can fulfill the order (for example, using a picker to acquire and deliver the ordered items) and use audio verification to verify delivery. When using audio verification, a customer or picker's mobile device plays verification audio including a verification code specific to the order. If the mobile devices (and, by proxy, the customer and picker) are nearby (for example, within 10 feet), the other party's mobile device can detect the verification audio through a microphone and decode the verification code from the captured audio. In some implementations, audio verification can also be performed using a smart doorbell or smart home without the presence of the customer.

Drone-based systems and methods are described for providing an airborne relocatable communication hub within a delivery vehicle for broadcast-enabled devices maintained within the delivery vehicle. Such a method has an aerial communication drone paired with the delivery vehicle transitioning to an active power state, uncoupling from a secured position on an internal docking station fixed within the delivery vehicle and then moving to a first deployed airborne position within the delivery vehicle. At a first position, the method has the aerial communication drone establishing a first wireless data communication path to a first broadcast-enabled device within the delivery vehicle, then establishing a second wireless data communication path to a second broadcast-enabled device within the delivery vehicle. The drone then couples the first and second wireless data communication paths it established operating as the airborne relocatable communication hub for the devices.

The present disclosure relates to systems and methods for managing a fleet of ridesharing vehicles. In one implementation, the system may include a communications interface configured to receive requests for shared rides from a plurality of users; a memory configured to store indications of passenger-capacity for specific ridesharing vehicles in the fleet; and at least one processor configured to receive information from the communications interface and access the memory The at least one processor may be further configured to assign, to ridesharing vehicles already transporting users, additional users for simultaneous transportation in the ridesharing vehicles; track a current utilized capacity of each specific ridesharing vehicle; and implement a threshold block that prevents assignment of additional users to a ridesharing vehicle when the ridesharing vehicle's current utilized capacity is above a threshold being less than the ridesharing vehicle's passenger-capacity.

A system and method of determining a policy to prevent fading drivers is described. The system and method creates virtual trajectories of incentives such as coupons offered to drivers in a transportation hailing system and corresponding states of drivers in response to the incentives. A joint policy simulator is created from an incentive policy, a confounding incentive policy, and an incentive object policy to generate the simulated actions of drivers in response to different incentives. The rewards of the simulated actions of the drivers is determined by a discriminator. The incentive policy for preventing fading drivers is optimized by reinforcement learning based on the virtual trajectories generated by the joint policy simulator and discriminator.

Systems and methods for a cloud-based server system configured for an On-Demand Autonomy (ODA) service are disclosed. The cloud-based server in communication with a first vehicle and a second vehicle to receive a trip request for the ODA service from the first vehicle, and seek an agreement to establish a virtual link with the first vehicle to the second vehicle by identifying one second vehicle by broadcasting the trip request to the group of second vehicles; identifying a second vehicle from submitted responses based on an application using a modeled clone with a set of functionalities to perform a matching operation between the first and second vehicles; and coordinating responses based on results of the matching operation to confirm an acceptance of the agreement; and creating a trip plan for the trip request for the ODA service based on a set of preferences received from each of the vehicles.

There is provided a method for providing a driving related guidance service by a service server. The method includes receiving advanced driver assistance system (ADAS) data of a vehicle related to a specific driving situation of the vehicle, location data of the vehicle, driving data of the vehicle, and a driving image captured during driving of the vehicle from a vehicle terminal device, generating guidance information related to the specific driving situation of the vehicle by analyzing the received data and the driving image, and providing a driving related guidance service for the vehicle using the generated guidance information.