Abstract: The present teaching relates to method, system, medium, and implementation of estimating a pose of an object in autonomous driving. Stereo images are obtained from a scene via at least two cameras. Objects present in the scene are then detected from the stereo images. For each object detected, a plurality of feature points are identified and depth information associated with the feature points are obtained. An orientation of each object is then estimated based on the feature points and depth information of the object and the pose of the object is then estimated based on the orientation and additional features of the object.

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

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, and medium, for operating a vehicle. Real-time data related to the vehicle are received. A current mode of operation of the vehicle and a state of the driver present in the vehicle are determined. A first risk associated with the current mode of operation of the vehicle is evaluated based on the real-time data and the state of the driver in accordance with a risk model. In response to the first risk satisfying a first criterion, a second risk associated with switching the current mode to a different mode of operation of the vehicle is determined based on the state of the driver. The vehicle is switched from the current mode to the different mode when the second risk satisfies a second criterion.

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, 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, and medium, for switching a mode of a vehicle. Real-time data related to the vehicle and a state of a driver are received and used to determine a set of tasks to be performed to switch the vehicle from a current mode to a different mode based on a task model. A first duration of time required to switch is determined based on a first risk associated with the current mode. A task duration time needed to complete each task is estimated. A second risk associated with the switching is determined based on the first duration as required and a total duration of time determined based on the task duration time needed to complete the set of tasks. The vehicle mode is switched if the second risk satisfying a criterion.

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, 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, 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, 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 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 determining depth information in autonomous driving. Stereo images are first obtained from multiple stereo pairs selected from at least two stereo pairs. The at least two stereo pairs have stereo cameras installed with the same baseline and in the same vertical plane. Left images from the multiple stereo pairs are fused to generate a fused left image and right images from the multiple stereo pairs are fused to generate a fused right image. Disparity is then estimated based on the fused left and right images and depth information can be computed based on the stereo images and the disparity.

Abstract: The present teaching relates to method, system, medium, and implementation of estimating a pose of an object in autonomous driving. Stereo images are obtained from a scene via at least two cameras. Objects present in the scene are then detected from the stereo images. For each object detected, a plurality of feature points are identified and depth information associated with the feature points are obtained. An orientation of each object is then estimated based on the feature points and depth information of the object and the pose of the object is then estimated based on the orientation and additional features of the object.

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

Abstract: The present teaching relates to method, system, and medium, for switching a mode of a vehicle. Real-time data related to the vehicle and a state of a driver are received and used to determine a set of tasks to be performed to switch the vehicle from a current mode to a different mode based on a task model. A first duration of time required to switch is determined based on a first risk associated with the current mode. A task duration time needed to complete each task is estimated. A second risk associated with the switching is determined based on the first duration as required and a total duration of time determined based on the task duration time needed to complete the set of tasks. The vehicle mode is switched if the second risk satisfying a criterion.

Abstract: The present teaching relates to method, system, medium, and implementation of route planning for an autonomous driving vehicle. A source location and a destination location are first obtained, where the destination location is where the autonomous driving vehicle is to drive to. One or more available routes between the source location and the destination location are identified. A self-aware capability model is instantiated with respect to the one or more available routes and is predictive of the operational capability of the autonomous driving vehicle with respect to each of the one or more available routes. Based on the self-aware capability model, a planned route to the destination location is then automatically selected for the autonomous driving vehicle.

Abstract: The present teaching relates to method, system, medium, and implementation of route planning for an autonomous driving vehicle. A source location and a destination location are first obtained, where the destination location is where the autonomous driving vehicle is to drive to. One or more available routes between the source location and the destination location are identified. A self-aware capability model is instantiated with respect to the one or more available routes and is predictive of the operational capability of the autonomous driving vehicle with respect to each of the one or more available routes. The preference of a passenger present in the autonomous driving vehicle is determined in terms of a route to take for the autonomous vehicle to drive to the destination location. Based on the self-aware capability model and the preference of the passenger, a planned route to the destination location is then automatically selected for the autonomous driving vehicle.

Abstract: The present teaching relates to method, system, medium, and implementation of automatic motion planning for an autonomous driving vehicle. Information is obtained with respect to a current location of the autonomous driving vehicle operating in a current vehicle motion, wherein the information is to be used to estimate the operational capability of the autonomous driving vehicle with respect to the current location. Sensor data are obtained, via one or more sensors in one or more media types. A reaction of a passenger present in the vehicle with respect to a current vehicle motion is estimated based on the sensor data. Motion planning is performed based on the information with respect to the current location and the reaction of the passenger to the current vehicle motion.

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. Information indicating presence of a passenger in the vehicle is obtained and used to retrieve a personalized lane control model related to the passenger. Lane control for the autonomous vehicle is planned based on the detected current lane and self-aware capability parameters in accordance with the personalized 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 personalized lane control model is generated based on recorded human driving data.