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 implementations for trailer pose estimation. At least a dimension of a trailer connected to a vehicle is obtained where the vehicle and the trailer have respective opposing surfaces facing one another. A first set of feature points is identified from a first image of a target present on one the opposing surfaces acquired at a first time instant by a sensor deployed on the other the opposing surfaces. A second set of corresponding feature points is identified from a second image of the target acquired at a subsequent time instant by the sensor. Based on the first and second sets of feature points, a first transformation is determined and used to compute a second transformation that the trailer undergoes from the first to the second time instants. A pose of the trailer at the second time instant is estimated based on the second transformation and the dimension.

Abstract: The present teaching relates to method, system, medium, and implementations for detecting sensor tampering. Information is received from an inertial measurement unit (IMU) attached to a structure hosting at least one sensor. The information includes one or more measurements associated with the IMU. The at least one sensor is deployed on a vehicle for sensing surrounding information to facilitate autonomous driving. The one or more measurements are analyzed with respect to pre-determined criteria to detect tampering of the at least one sensor. When the tampering is detected, a response is generated to the event.

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: 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 of the set of tasks by which the task is to be completed. A current state of the driver is obtained and used to determine a set of warnings to alert the driver to perform the set of tasks. Each warning corresponds to a task in the set of tasks and is created based on the current state of the driver. 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, 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.