An automatic valet parking system includes a vehicle, a parking server, and a map server. The parking server includes a server-side operation planning portion that generates a server-side operation plan including a route for guiding the vehicle to a target position. The vehicle includes: an operation plan determination portion that determines whether the server-side operation plan is false; an automatic operation control portion that performs automatic operation control according to the server-side operation plan; and an information transmission portion that acquires vehicle-related information about the vehicle and to transmit the vehicle-related information to the parking server when the operation plan determination portion determines that the server-side operation plan is false.

An automatic valet parking system includes a vehicle, a parking server, and a map server including a database. The vehicle includes: an operation plan determination portion that determines whether the saver-side operation plan is false; a request generating portion that requests the map server to transmit the parkable region information when the server-side operation plan is false; a vehicle-side operation planning portion that generates, based on the parkable region information, a vehicle-side operation plan including a route for guiding the vehicle to the target position in the unmanageable region in response to receiving the parkable region information; and an automatic operation control portion that is configured to: perform the automatic operation control according to the server-side operation plan when the server-side operation plan is not false; and perform the automatic operation control according to the vehicle-side operation plan when the server-side operation plan is false.

A systems and methods for ridesharing are provided. The systems and method can include splitting a plurality of GPS locations for a given vehicle into segments, determining a most probable location for each GPS location, and reconstructing the route, for a fleet of ridesharing vehicles.

An intelligent traffic control system may be configured to manage autonomous vehicle traffic, such as by communicating with autonomous vehicles. The intelligent traffic control system may be configured to output an indication of a traffic control command to autonomous vehicles and non-autonomous vehicles. The intelligent traffic control system may be configured to determine traffic control commands for autonomous vehicles and non-autonomous vehicles.

Among other things, techniques are described for a passenger support system. In an example, a reported number of passengers associated with on-demand mobility transportation services is obtained in response to passengers opting-in to a passenger support system. The reported number of passengers is compared with a vehicle detected number of passengers. In response to the reported number of passengers being unequal to the vehicle detected number of passengers, a remote customer assistant is enabled.

Systems and methods for implementing one or more autonomous features for autonomous and semi-autonomous control of one or more vehicles are provided. More specifically, image data may be obtained from an image acquisition device and processed utilizing one or more machine learning models to identify, track, and extract one or more features of the image utilized in decision making processes for providing steering angle and/or acceleration/deceleration input to one or more vehicle controllers. In some instances, techniques may be employed such that the autonomous and semi-autonomous control of a vehicle may change between vehicle follow and lane follow modes. In some instances, at least a portion of the machine learning model may be updated based on one or more conditions.

Technologies for monitoring vehicle traffic include a traffic analysis server that receives infrastructure data from infrastructure sensors positioned along a road segment of a road and vehicle data from one or more vehicles travelling along the road segment. The traffic analysis server determines whether anomalies are present in the traffic data through the road segment based on an expected traffic behavior for the road segment. The traffic analysis server determines the expected traffic behavior for the road segment in a particular time window based on a historical traffic pattern associated with the road segment, based on historical vehicle data and historical infrastructure data captured during a prior time window corresponding to the particular time window for that road segment. Other embodiments are described and claimed.

To provide a route arbitration apparatus, an automatic driving controller, and an automatic driving and route arbitration system which can avoid an overlapping between travel routes and make vehicles carry out a traveling operation smoothly while considering priority between vehicles, in a broad perspective, about a multiple of vehicles existing in a periphery. A route arbitration apparatus receives a target travel route from each vehicle; when it is determined that a mutual overlapping of the target travel routes occurs, determines a priority travel area which is an area where the highest priority vehicle can travel in accordance with the target travel route, transmits the priority travel area to the highest priority vehicle, determines an avoidance travel area which is an area where the low priority vehicle can travel while avoiding the priority travel area, and transmits the avoidance travel area to the low priority vehicle.

A risk prediction determination device includes a predicted traveling route specifying unit, a risk location specifying unit, and a risk prediction determination unit. The predicted traveling route specifying unit specifies a predicted traveling route of a subject vehicle based on traveling lane information indicating a traveling lane of the subject vehicle, road type information indicating a type of a road around the subject vehicle, road link information related to a road link, and sensor information of the subject vehicle. The risk location specifying unit specifies a location with risk based on road shape information around the subject vehicle and location information of an obstacle. The risk prediction determination unit performs a risk prediction determination based on a specified result of the predicted traveling route specifying unit and a specified result of the risk location specifying unit.

A method, which can be implemented by a control unit, for carrying out a localization of at least one vehicle by a vehicle-side control unit includes receiving measuring data from at least one sensor, ascertaining at least one marking from the measuring data, and associating the ascertained marking with a marking entered into a digital map for determining a position.