The technology employs a holistic approach to passenger pickups and other wayfinding situations. This includes identifying where passengers are relative to the vehicle and/or the pickup location. Information synthesis from different sensors, agent behavior prediction models, and real-time situational awareness are employed to identify the likelihood that the passenger to be picked up is at a given location at a particular point in time, with sufficient confidence. The system can provide adaptive navigation by helping passengers understand their distance and direction to the vehicle, for instance using various cues via an app on the person's device. Rider support tools may be provided, which enable a remote agent to interact with a customer via that person's device, such as using the camera on the device to provide wayfinding support to enable the person to find their vehicle. Ride support may also use sensor information from the vehicle when providing wayfinding support.

An autonomous vehicle configured to facilitate its use and/or enhance what it and/or occupants can do with it, such as, for example, by: autonomously acting based on events within the autonomous vehicle, including by autonomously rerouting itself, altering a cabin of the autonomous vehicle, notifying a third party external to the autonomous vehicle, stopping (e.g., parking) the autonomous vehicle, altering how the autonomous vehicle drives itself, and/or performing other actions based on what and/or how an occupant is doing, an emergency, or another event occurring in the cabin of the autonomous vehicle; autonomously acting based on interactions with (e.g., gestures of) humans (e.g., police officers, school-crossing guards, traffic guards at roadwork or other temporary traffic control sites, drivers of other vehicles, etc.) external to the autonomous vehicle; autonomously acting based on indicators placed at particular locations (e.g., drive-through establishments, potholes, parking spots, etc.); facilitating acts of occupants in the cabin of the autonomous vehicle, such as acts unrelated to and normally not done while driving, including, for example, sleeping, exercising, working, eating, cooking, and/or any other suitable act; and/or automatically personalizing the autonomous vehicle for an occupant (e.g., a configuration of the cabin, a self-driving mode, a destination and/or a route, etc.).

Aspects of the disclosure relate to parking behaviors and maneuvering a vehicle in an autonomous driving mode accordingly. For instance, a pullover location for the vehicle to stop and wait for a passenger may be identified. The vehicle may be maneuvered in the autonomous driving mode in order to pull over by pulling forward into the pullover location. Whether to maneuver the vehicle in reverse in the pullover location before or after the passenger enters the vehicle may be determined based on context for the pull over with respect to the passenger. The vehicle may be maneuvered in the autonomous driving mode in reverse based on the determination of whether to maneuver the vehicle in reverse in the pullover location before or after the passenger enters the vehicle.

A method for operating an autonomously operable motor vehicle comprising: receiving location data relating to a planned location of a motor vehicle; determining whether the motor vehicle is authorized to approach the planned location according to the location data by using at least one of the following: a) personal data about at least one person using the motor vehicle; b) a number of persons using the motor vehicle; c) load data about a load that is being or is to be transported by the motor vehicle.

A mobile body control device adjusts a stop position of the mobile body based on an instruction of a user. The device acquires instruction information for designating a predetermined target and acquires a captured image captured in the mobile body. The device determines a position of the predetermined target from the mobile body as a stop position based on a region of the predetermined target identified in the captured image and control traveling of the mobile body toward the determined stop position. In a case where the predetermined target is not identified in the captured image while the mobile body is traveling toward the stop position, the device updates the stop position determined with respect to the predetermined target according to a motion of the mobile body to control the traveling of the mobile body to the stop position.

Techniques described in this application are directed to determining safe path navigation of an autonomous personal mobility device, including a self-driving electric scooter, using sensors and/or data from other sources. The autonomous personal mobility device is configured to generate maps and transform the maps into tile segments that can be shared with other autonomous personal mobility devices. Techniques further include receiving a request for an autonomous personal mobility device at a location, and enabling the autonomous personal mobility device to self-drive to the location.

Automated drive system comprises a control device. The control device executes run control processing of the automated drive vehicle provided to a driverless transportation service. The run control processing includes carriage porch control processing. The carriage porch control processing is performed when a PUDO position of a user corresponds to a carriage porch of a facility including a zone for run and a zone for PUDO. In the carriage porch control processing, it is judged whether or not there is an empty space in the zone for PUDO. If it is judged that there is no empty space, it is judged whether or not a quick PUDO of the user in the carriage porch is expected. If it is judged that the quick PUDO is expected, a target PUDO position to perform a PUDO action of the automated drive vehicle is set to any position in the zone for run.

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.

Provided is an automated valet parking system that automatically moves an autonomous driving vehicle to a pick-up space of a parking place by issuing an instruction to the autonomous driving vehicle parked in the parking place according to a pick-up request from a user. The system includes a user position determination unit configured to determine whether or not a user frontend of the user is located in a preset pick-up area or a preset near-pick-up area including the pick-up space, a pick-up request reception unit configured to receive the pick-up request when the user position determination unit determines that the user frontend is located in the pick-up area or the near-pick-up area, and a vehicle instruction unit configured to instruct the autonomous driving vehicle that is a target of the pick-up request to move to the pick-up space when the pick-up request reception unit receives the pick-up request.

A control system comprises a travel control unit configured to cause a moving body to travel to a designated place designated by a dispatch request, in response to the dispatch request from a user. Interactive communication is performed between the user and the moving body via a mobile terminal held by the user, before the moving body reaches the designated place. Image data in which surroundings of the designated place are captured by an imaging unit is acquired, after the moving body has reached the designated place as a result of the interactive communication. Information that has been transmitted from the user in the interactive communication is evaluated, based on the image data that has been acquired.