Abstract: Methods, systems, and non-transitory computer-readable media are configured to perform operations comprising determining a lead obstacle in front of an ego vehicle; determining an optimizable function associated with an optimized preferred following distance between the ego vehicle and the lead obstacle; and generating the optimized preferred following distance based on the optimizable function.

Abstract: The present teaching relates to method, system, medium, and implementations for sensor calibration. An ego vehicle determines whether a sensor deployed on the ego vehicle to facilitate autonomous driving of the ego vehicle needs to be calibrated and sends, if it is determined that the sensor needs to be calibrated, a request for assistance in collaborative calibration of the sensor, with a first position of the ego vehicle or a first configuration of the sensor with respect to the ego vehicle. When a response of the request is received, an assisting vehicle is indicated to travel to be near the ego vehicle to facilitate the calibration of the sensor by collaborating with the moving ego vehicle and the ego vehicle coordinates with the assisting vehicle to enable the sensor to acquire information of a target present on the assisting vehicle for the collaborative calibration of the sensor.

Abstract: The present teaching relates to apparatus, method, medium, and implementations for simultaneously calibrating multiple sensors of different types. Multiple sensors of different types are first activated to initiate simultaneous calibration thereof based on a 3D construct including a plurality of fiducial marks. Sensors of different types including visual and depth based sensors operate in their respective coordinate systems. Each of the sensors is calibrated by acquiring sensor information of the 3D construct, detecting a feature point on each of the plurality of fiducial markers based on the sensor information, estimating a set of 3D coordinates, with respect to its coordinate system, corresponding to the detected feature points, based on which calibration parameters are generated. Sets of 3D coordinates derived in different coordinate systems are then used to compute at least one transformation matrix for corresponding at least one pair of the plurality of sensors.

Abstract: A method includes an initial trailer health assessment and real-time trailer health monitoring. The initial trailer health assessment includes autonomous pre-trip maneuvers of the autonomous vehicle during a first time period, and detecting a pre-trip vehicle health condition. A vehicle health score is calculated based on the pre-trip vehicle health condition. If the vehicle health score is at least a threshold value, real-time trailer health monitoring is performed during a trip of the autonomous vehicle during a second time period, by actively monitoring vehicle dynamics data and/or image data associated with the autonomous vehicle, to determine a fault condition of the autonomous vehicle. If the fault condition meets a first criteria, a control parameter and/or a travel plan of the autonomous vehicle is adjusted. If the fault condition meets a second criteria different from the first criteria, a signal is sent to cause the autonomous vehicle to cease movement.

Abstract: This application is directed to aerial view generation for vehicle control. A vehicle obtains a forward-facing view of a road captured by a front-facing camera of a vehicle and applies a machine learning model to process the forward-facing view to predict determine a trajectory of the vehicle and a road layout based on a Frenet-Serret coordinate system of the road for the vehicle. The trajectory of the vehicle is combined with the road layout to predict an aerial view of the road, and the aerial view of the road is used to at least partially autonomously drive the vehicle. In some embodiments, the machine learning model is applied to process the forward-facing view to determine a first location of each of an obstacle vehicle in the Frenet-Serret coordinate system. The first location of the obstacle vehicle is converted to a vehicle location on the aerial view of the road.

Abstract: This application is directed to aerial view generation for vehicle control. A vehicle obtains a forward-facing view of a road captured by a front-facing camera of a vehicle and applies a machine learning model to process the forward-facing view to predict determine a trajectory of the vehicle and a road layout based on a Frenet-Serret coordinate system of the road for the vehicle. The trajectory of the vehicle is combined with the road layout to predict an aerial view of the road, and the aerial view of the road is used to at least partially autonomously drive the vehicle. In some embodiments, the machine learning model is applied to process the forward-facing view to determine a first location of an obstacle vehicle in the Frenet-Serret coordinate system. The obstacle vehicle is placed on the aerial map based on the first location of the obstacle vehicle.

Abstract: The present teaching relates to method, system, medium, and implementations for sensor calibration. A request is received from an ego vehicle in motion on a route for assistance in collaborative calibration of a sensor deployed on the ego vehicle. The request specifies a position of the ego vehicle and a configuration of the sensor with respect to the ego vehicle. A collaborative means along the route is identified based on the ego vehicle's position, the configuration of the sensor, the route, and the position associated with the collaborative means. A calibration assistance package is generated in response to the request and sent to the ego vehicle. The calibration assistance package includes information about the collaborative means that can be used to identify the collaborative means while in motion along the route for calibrating the sensor while the ego vehicle is in the vicinity of the collaborative means.

Abstract: A method includes receiving, iteratively over time, sets of data including vehicle dynamics data, image data, sound data, third-party data, and wind speed sensor data, each detected at an autonomous vehicle and associated with a time period. The method also includes estimating a first wind speed and a first wind direction for each time period, in response to receiving the sets of data and based on the sets of data, via a processor of the autonomous vehicle. The method also includes iteratively modifying a lateral control and/or a longitudinal control of the autonomous vehicle based on the estimated first wind speed and the estimated first wind direction, via the processor of the autonomous vehicle and during operation of the autonomous vehicle.

Abstract: Systems, methods, and non-transitory computer readable media can perform operations comprising generating a first type of pose and a second type of pose based on visual geometry localization; determining a mode for planning a planning path for a vehicle based on at least one of the first type of pose and the second type of pose; and generating the planning path for the vehicle based on the mode.

Abstract: An autonomous vehicle can obtain sensor data. Upon determining that the autonomous vehicle is in a lane adjacent a shoulder, and there is an object in the shoulder, the autonomous vehicle can determine if performing a lane change maneuver out of the lane prior to the autonomous vehicle being positioned adjacent to the object is feasible. If it is, the lane change maneuver can be performed. If it is not, a nudge maneuver and/or a deceleration can be performed.

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: Embodiments provide a vehicle computer coupled to a vehicle. The vehicle computer may be configured to compute (e.g., generate) a first (e.g., regular, main) trajectory and a second (e.g., fail-safe, minimal risk maneuver) trajectory for the vehicle. Embodiments may provide techniques for incorporating sensor input into the response for managing a minimal risk maneuver. The low-resolution sensor data may be received from a low-resolution sensor having integrated object detection processing such as an automotive radar sensor. The safety processor may modify the second trajectory using the low-resolution sensor data to avoid any new hazards along the second trajectory. Embodiments may also provide, in addition to the low-resolution radar sensor data, pre-computed analysis of the low-resolution radar sensor data from the main processor to the safety processor to reduce the false positives (e.g., false positive object detections) along the second trajectory.

Abstract: Methods, systems, and non-transitory computer-readable media are configured to perform operations comprising acquiring data regarding historical movement of a lead obstacle and one or more obstacles in front of the lead obstacle; predicting movement of at least the lead obstacle based at least in part on the data; and applying the predicted movement of the lead obstacle to an optimizable function associated with movement of an ego vehicle.

Abstract: The present teaching relates to method and system for an assembly for protecting a sensor. The assembly includes a first structure having a rectangular prism and connected with a truncated tube, wherein the rectangular prism and the truncated tube have parallel longitudinal axes and the tube is truncated to yield a cross section having a norm that forms an angle with the longitudinal axis of the truncated tube and a second structure of a trapezoid prism having a longitudinal axis parallel to the longitudinal axes and with the first structure embedded therein. The second structure has a slanted surface in the front that meets the cross section of the truncated tube and has the norm of the cross section.

Abstract: The present teaching relates to method, system, medium, and implementation for sensor protection. It is determined whether a cleaning operation needs to be applied to a portion of a sensor assembly deployed on a vehicle and hosting a sensor therein to acquire information of a surrounding through the portion to facilitate autonomous driving. If the cleaning operation is to be applied, at least one type of cleaning is selected to be carried out by one or more devices mounted on the sensor assembly with respect to the portion. For each of the devices, needed control signal is generated for activating the device to perform a cleaning task the device is configured for and sent to the device to clean the certain portion of the sensor assembly in order to prevent degradation in the information acquired by the sensor through the portion.

Abstract: The present teaching relates to apparatus, method, medium, and implementations for initiating sensor calibration. A first GPS signal is received by a GPS receiver residing in an ego vehicle and is used to determine a first geo-position of the ego vehicle. A GPS related signal transmitted by a fiducial marker is received and is used to obtain a second geo-position of the fiducial marker. A distance between the ego vehicle and the fiducial marker is determined based on the first and second geo-positions and is used to determine whether to initiate calibration of one or more sensors using the fiducial marker.

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 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: A user-generated graphical representation can be sent into a generative network to generate a synthetic image of an area including a road, the user-generated graphical representation including at least three different colors and each color from the at least three different colors representing a feature from a plurality of features. A determination can be made that a discrimination network fails to distinguish between the synthetic image and a sensor detected image. The synthetic image can be sent, in response to determining that the discrimination network fails to distinguish between the synthetic image and the sensor-detected image, into an object detector to generate a non-user-generated graphical representation. An objective function can be determined based on a comparison between the user-generated graphical representation and the non-user-generated graphical representation.

Abstract: An apparatus includes a processor configured to be disposed with a vehicle and a memory coupled to the processor. The memory stores instructions to cause the processor to receive, at least two of: radar data, camera data, lidar data, or sonar data. The sensor data is associated with a predefined region of a vicinity of the vehicle while the vehicle is traveling during a first time period. At least a portion of the vehicle is positioned within the predefined region during the first time period. The method also includes detecting that no other vehicle is present within the predefined region. An environment of the vehicle during the first time period is classified as one state from a set of states that includes at least one of dry, light rain, heavy rain, light snow, or heavy snow, based on at least two of the sensor data to produce an environment classification. An operational parameter of the vehicle based on the environment classification is modified.