Abstract: The present teaching relates to method, system, and medium, for generating an augmented alert in a hybrid vehicle. First information indicating an upcoming switch in an operating mode of the vehicle is received, which specifies a set of tasks, arranged in an order, to be completed by a driver in the vehicle to achieve the upcoming switch, and a task duration for each task of the set of tasks by which the task is to be completed. A set of warnings to alert the driver to perform the set of tasks is determined based on a current state of the driver, wherein each warning corresponds to a task in the set of tasks. A warning schedule is generated based on the set of warnings in the order of the set of tasks and transmitted so that warnings in the warning schedule are delivered to the driver.

Abstract: The present teaching relates to method, system, and medium, for switching a mode of a vehicle. Real-time data related to the vehicle are received, which include intrinsic/extrinsic capability parameters, based on which a set of tasks to switch from a current mode to a different mode is determined. A first duration of time required for the switch is determined based on a first risk evaluated with respect to the current mode and the real-time data. A task duration time needed by a driver to complete the task is estimated for each of the set of tasks. A second risk for the switching is estimated based on the required first duration of time and a total task duration times needed to complete the set of tasks. The switch is carried out when the second risk satisfying a criterion.

Abstract: The present teaching relates to method, system, medium, and implementation of lane planning in an autonomous vehicle. Sensor data are received that capture ground images of a road the autonomous vehicle is on. Based on the sensor data, a current lane of the road that autonomous vehicle is currently occupying is detected. Lane control for the autonomous vehicle is planned based on the detected current lane and self-aware capability parameters in accordance with a driving lane control model. The self-aware capability parameters are used to predict operational capability of the autonomous vehicle with respect to a current location of the autonomous vehicle. The driving lane control model is generated based on recorded human driving data to achieve human-like lane control behavior in different scenarios.

Abstract: The present teaching relates to method, system, and medium, for operating a vehicle. The method includes the steps of receiving Real-time data related to the vehicle are received. A current mode of operation of the vehicle is determined. A first risk associated with the current mode of operation of the vehicle is evaluated based on the real-time data in accordance with a risk model. If the first risk satisfies a first criterion, a second risk associated with switching the current mode to a different mode of operation of the vehicle is determined. The vehicle is switched from the current mode to the different mode if the second risk satisfies a second criterion.

Abstract: The present teaching relates to method, system, medium, and implementation of human-like vehicle control for an autonomous vehicle. Information related to a target motion to be achieved by the autonomous vehicle is received, wherein the information includes a current vehicle state of the autonomous vehicle. A first vehicle control signal is generated with respect to the target motion and the given vehicle state in accordance with a vehicle kinematic model. A second vehicle control signal is generated in accordance with a human-like vehicle control model, with respect to the target motion, the given vehicle state, and the first vehicle control signal, wherein the second vehicle control signal modifies the first vehicle control signal to achieve human-like vehicle control behavior.

Abstract: The present teaching relates to method, system, medium, and implementation of human-like vehicle control for an autonomous vehicle. Recorded human driving data are first received, which include vehicle state data, vehicle control data, and environment data. For each piece of recorded human driving data, a vehicle kinematic model based vehicle control signal is generated in accordance with a vehicle kinematic model based on a corresponding vehicle state and vehicle control data of the piece of recorded human driving data. A human-like vehicle control model is obtained, via machine learning, based on the recorded human driving data as well as the vehicle kinematic model based vehicle control signal generated based on vehicle kinematic model. Such derived human-like vehicle control model is to be used to generate a human-like vehicle control signal with respect to a target motion of an autonomous vehicle to achieve human-like vehicle control behavior.

Abstract: The present teaching relates to method, system, and medium, for presenting a warning to a driver. An instruction indicating an upcoming change in an operating mode of the vehicle is obtained, wherein the instruction specifies a set of tasks to be completed by the driver and a task duration associated with each task. Information of a current state of the driver is obtained and used to determine a warning to be used to alert the driver to perform the change, wherein the warning includes warning content and indication of at least one media to be used to deliver the warning content. A warning schedule is generated to comprise a set of warnings, each warning corresponding to one task, and arranged in an order based on an order of the tasks. The warning schedule is transmitted so that each warning of the set of warnings is executed.

Abstract: The present teaching relates to method, system, medium, and implementation of automatic motion planning for an autonomous driving vehicle. Self-aware capability parameters are obtained that are instantiated with respect to a current location of the autonomous driving vehicle and are to be used to estimate the operational capability of the autonomous driving vehicle with respect to the current location. A preference of a passenger present in the autonomous driving vehicle is estimated with respect to vehicle motion. The motion of the autonomous driving vehicle is planned automatically based on the self-aware capability parameters and the preference of the passenger.

Abstract: The present teaching relates to system, method, medium for in-situ perception in an autonomous driving vehicle. A plurality of types of sensor data acquired continuously by a plurality of types of sensors deployed on the vehicle are first received, where the plurality of types of sensor data provide information about surrounding of the vehicle. Based on at least one model, one or more items are tracked from a first of the plurality of types of sensor data acquired by one or more of a first type of the plurality of types of sensors, wherein the one or more items appear in the surrounding of the vehicle. At least some of the one or more items are then automatically labeled on-the-fly via either cross modality validation or cross temporal validation of the one or more items and are used to locally adapt, on-the-fly, the at least one model in the vehicle.

Abstract: The present teaching relates to method, system, medium, and implementation of in-situ perception in an autonomous driving vehicle. A plurality of types of sensor data are acquired continuously via a plurality of types of sensors deployed on the vehicle, where the plurality of types of sensor data provide information about surrounding of the vehicle. One or more items surrounding the vehicle are tracked, based on some models, from a first of the plurality of types of sensor data from a first type of the plurality of types of sensors. A second of the plurality of types of sensor data are obtained from a second type of the plurality of sensors and are used to generate validation base data. Some of the one or more items are labeled, automatically, via validation base data to generate labeled at least some item, which is to be used to generate model updated information for updating the at least one model.

Abstract: The present teaching relates to a method, system, medium, and implementation of processing image data in an autonomous driving vehicle. Sensor data acquired by one or more types of sensors deployed on the vehicle are continuously received. The sensor data provide different information about surrounding of the vehicle. Based on a first data set acquired by a first sensor of a first type of the one or more types of sensors at a specific time, an object is detected, where the first data set provides a first type of information about the surrounding of the vehicle. Depth information of the object is then estimated via object centric stereo at object level based on the object detected as well as a second data set acquired by a second sensor of the first type of the one or more types of sensors at the specific time. The second data set provides the first type of information about the surrounding of the vehicle with a different perspective as compared with the first data set.

Abstract: The present teaching relates to method, system, medium, and implementation of in-situ perception in an autonomous driving vehicle. A plurality of types of sensor data are acquired continuously via a plurality of types of sensors deployed on the vehicle, where the plurality of types of sensor data provide information about surrounding of the vehicle. One or more items surrounding the vehicle are tracked, based on a model, from a first of the plurality of types of sensor data from a first type of the plurality of types of sensors. For a tracked item, cross temporal validation is performed by obtaining an estimated label for the item, retrieving previous labels corresponding to the item with corresponding previous time stamps, and assigning the estimated label to the item if the estimated label is validated based on previous labels to generate a labeled item. The labeled items are to be used to generate model updated information, which is then used to update the model.

Abstract: The present teaching relates to a method, system, medium, and implementation of processing image data in an autonomous driving vehicle. Sensor data acquired by one or more types of sensors deployed on the vehicle are continuously received to provide different types of information about surrounding of the vehicle. Based on a first data set acquired by a first sensor of a first type of sensors at a specific time, an object is detected, where the first data set provides a first type of information with a first perspective. Depth information of the object is estimated via object centric stereo based on the object and a second data set, acquired at the specific time by a second sensor of the first type of sensors with a second perspective. The estimated depth information is further enhanced based on a third data set acquired by a third sensor of a second type of sensors at the specific time, providing a second type of information about the surrounding of the vehicle.

Abstract: The present teaching relates to system, method, medium for in-situ perception in an autonomous driving vehicle. A plurality of types of sensor data are received, which are acquired by a plurality of types of sensors deployed on the vehicle to provide information about surrounding of the vehicle. Based on at least one model, one or more surrounding items are tracked from a first of the plurality of types of sensor data acquired by a first type sensors. At least some of the tracked items are automatically labeled via cross validation and are used to locally adapt, on-the-fly, the at least one model. Model update information is received which from a model update center, which is derived based on the labeled at least some items. The at least one model is updated using the model update information.

Abstract: The present teaching relates to method, system, medium, and implementation of a global model update center. At least one model is established at the model update center for detecting objects surrounding each of autonomous driving vehicles of a fleet. A plurality of labeled data items are received, from the fleet of autonomous driving vehicles, where each of the labeled data items is detected, based on the at least one model, from sensor data characterizing surroundings of the autonomous driving vehicles. The labeled data items are generated automatically on-the-fly by the autonomous driving vehicles. Based on the received labeled data items, at least some of the models are updated and model update information is accordingly generated. Such generated model update information is then distributed to the fleet of autonomous driving vehicles.

Abstract: A method and apparatus allows owners of electronic signs, such as retailers, real estate owners, other space owners, and content providers, such as advertisers, entertainment producers, event promoters, visual artists, and the general community to participate in an open content network, in which electronic signs are universally uniquely identified and then added to a network in a distributed fashion, after which content is selectively downloaded to the electronic signs. The times at which the content is presented are determined collaboratively by the owners of the electronic signs and the content providers. A mechanism that verifies whether the content is actually presented on the electronic signs is also disclosed.

Abstract: A method and apparatus is disclosed that allows advertisers to define and distribute Short Messaging Service (SMS) and Multimedia Messaging Service (MMS) coupons to consumers, and for consumers to redeem the received coupons, where such SMS and MMS coupons are used by businesses to promote their products and services to consumers.

Abstract: A method and apparatus is disclosed that allows advertisers to define and distribute Short Messaging Service (SMS) and Multimedia Messaging Service (MMS) coupons to consumers, and for consumers to redeem the received coupons, where such SMS and MMS coupons are used by businesses to promote their products and services to consumers.

Abstract: A system for mobile simulated real-time player vs. player (PvP) gaming provides a user interface (UI) to a mobile device for simulated real-time player vs. player (PvP) gaming. A player selects an opponent and issues a challenge to the opponent's record on a song from the opponent's song list. The challenger then performs a simulated real-time challenge to the opponent's record on the song. If the challenger wins the challenge he/she is awarded bonus points. If the challenger loses, he can issue another challenge for the original song. To play a second round, the challenged player from the first round challenges the original challenger's record on the song. Winners get the right to a new challenge. Losers can purchase the right to a new challenge. Gameplay proceeds in a chain in which the players play alternating rounds.

Abstract: An asynchronous, multiplayer game provides a user interface (UI) to a mobile device for asynchronous multiplayer gaming. By way of the UI, a team opens a game by selecting an opponent and provides notice to its team members of the newly-opened game. After team members join the game, opponents trade strategic and tactical moves asynchronously. In an embodiment, opposing guilds fight wars against each other to occupy fortresses held by an opponent and capture the opponent's rations.