Abstract: In one embodiment, location and route information of a route associated with the autonomous vehicle is obtained, where the route includes a starting location and a destination. Real-time traffic information of the route is obtained based on the location and route information. A driving mode of the autonomous vehicle is determined based on the location and route information and the real-time traffic information. A content item is selected from a list of content items obtained from a content database based on the determined driving mode. The selected content item is displayed in a display device within the autonomous vehicle.

Abstract: In one embodiment, a vehicle identifier (ID) and vehicle information of a first autonomous vehicle is transmitted to a cloud server over a network, where the cloud server is communicatively coupled to a plurality of autonomous vehicles. A list of one or more vehicle identifiers (IDs) are received from the cloud server. The vehicle IDs identify one or more autonomous vehicles are within a predetermined geographic proximity of the first autonomous vehicle. The first autonomous vehicle communicates with a second autonomous vehicle that is selected from the list of one or more autonomous vehicles via a wireless local area network to exchange an operational status of the first autonomous vehicle with the second autonomous vehicle.

Abstract: In one embodiment, an image analysis is performed on an image captured using a camera mounted on an autonomous vehicle, the image representing an exterior environment of an autonomous vehicle. Localization information surrounding the autonomous vehicle is obtained at a point in time. A perception of an audience external to the autonomous vehicle is determined based on the image analysis and the localization information. One or more content items are received from one or more content servers over a network in response to the perception of the audience. A first content item selected from the one or more content items is displayed on a display device mounted on an exterior surface of the autonomous vehicle.

Abstract: In one embodiment, a vehicle identifier (ID) and vehicle information of a first autonomous vehicle is transmitted to a cloud server over a network, where the cloud server is communicatively coupled to a plurality of autonomous vehicles. A list of one or more vehicle identifiers (IDs) are received from the cloud server. The vehicle IDs identify one or more autonomous vehicles are within a predetermined geographic proximity of the first autonomous vehicle. The first autonomous vehicle communicates with a second autonomous vehicle that is selected from the list of one or more autonomous vehicles via a wireless local area network to exchange an operational status of the first autonomous vehicle with the second autonomous vehicle.

Abstract: In one embodiment, location and route information of a route associated with the autonomous vehicle is obtained, where the route includes a starting location and a destination. Real-time traffic information of the route is obtained based on the location and route information. A driving mode of the autonomous vehicle is determined based on the location and route information and the real-time traffic information. A content item is selected from a list of content items obtained from a content database based on the determined driving mode. The selected content item is displayed in a display device within the autonomous vehicle.

Abstract: In one embodiment, a server receives a request from a first autonomous vehicle for content delivery. In response to the request, a vision analysis is performed on an image obtained from the request to determine three-dimensional (3D) positioning information of the image. A list of content items are identified based on current vehicle information of the first autonomous vehicle in view of a user profile of a user ridding the first autonomous vehicle. A first content item selected from the list of content items is augmented onto the image based on the 3D positioning information of the image, generating an augmented image. The augmented image is transmitted to the first autonomous vehicle, where the augmented image is to be displayed on a display device within the autonomous vehicle in a virtual reality manner.

Abstract: In one embodiment, a first image of a physical object external to an autonomous vehicle is received, where the first image is captured by an image sensor attached to the autonomous vehicle. An image recognition is performed on the first image to derive one or more keywords related to the physical object. A list of one or more content items are identified based on the one or more keywords. A first content item selected from the list of content items is augmented onto the first image to generate a second image. The second image is displayed on a display device within the autonomous vehicle.

Abstract: According to one embodiment, a master storage node receives a search query for searching images from a client, where the master storage node is coupled to a number of worker storage nodes over a storage network. The master storage node performs a hash operation on one or more keywords of the search query using a first predetermined hash function, generating a first hash value. A first of the worker storage nodes is identified based on the first hash value. The master storage node redirects the search query to the first worker storage node to allow the first worker storage node service the search query. The first worker storage node is to identify one or more images from a first system memory of the first worker storage node based on the search query and to transmit the one or more images to the client.

Abstract: In one embodiment, a cluster manager partitions media data into multiple media data partitions, each of which is to be processed by a computing node of a computing system. A platform module in a computing node receives a number of media files included in a media data partition. The platform module encodes each media file from an original format into a predefined format. The platform module then combines or serializes each encoded media file into a single stream of serialized data, and transmits it to a user module in the computing node. The user module separates from each other the received encoded media files. The user module then decodes each separated, encoded media file from the predefined format into the original format, and performs a specific media processing operation on each decoded file. Examples of the media processing operation include classification, image transformation, and feature extraction.