Abstract: A system includes an electronic control unit (ECU) having a housing and configured to be disposed within an interior of an autonomous vehicle. The system also includes an inertial measurement unit (IMU) rigidly disposed with the housing of the ECU. The system also includes a processor configured to receive measurement data from the IMU. The IMU and the processor is powered by a battery separate from a power source of the ECU. The processor is configured to identify a tamper event of the ECU based on the measurement data from the IMU. The processor is also configured to send a signal to cause a tamper response of the tamper event.

Abstract: Methods, systems, and non-transitory computer-readable media are configured to perform operations comprising determining a symmetric scenario for a scenario; training a first machine learning model for the scenario based on first training data generated from second training data for the symmetric scenario; and generating a prediction for the scenario based on the first machine learning model.

Abstract: Embodiments provide a vehicle computer coupled to a vehicle. The vehicle computer may be configured to compute (e.g., generate) an automated lane change maneuver moving the vehicle from a first lane into a second, adjacent lane. The lane change maneuver may have commenced while the maneuver was safe to conduct, but a threat vehicle (or another threat object) may be subsequently detected moving into the same target lane. The option to immediately abort the maneuver and return to the original lane may not be appropriate after some time into the lane change maneuver. The vehicle computer may control the vehicle in a lateral position hold along the lane demarcation line for a predetermined amount of time before moving into the second lane when the threat vehicle clears the second lane or returning to the first lane when the threat vehicle does not clear the second lane.

Abstract: This application is directed to generic obstacle detection for at least partially autonomous vehicle driving. A first vehicle obtains a road image. The road image includes a road surface along which the first vehicle is travelling. The first vehicle identifies one or more identifiable objects on the road surface in the road image. The first vehicle detects a plurality of objects on the road surface in the road image. The first vehicle eliminates the one or more identifiable objects from the plurality of objects in the road image to determine one or more unidentifiable objects on the road surface in the road image. The first vehicle at least partially autonomously drives the first vehicle by treating the one or more unidentifiable objects differently from the one or more identifiable objects.

Abstract: Methods, systems, and non-transitory computer readable media are configured to perform operations comprising obtaining, by a computing system, a lane-keeping safe set associated with constraints on lateral movement of a vehicle traveling on a road, generating, by the computing system, a lane-keeping safe set with preview based on the lane-keeping safe set and preview data, the preview data associated with characteristics of the road ahead of the vehicle, receiving, by the computing system, a steer command to control the lateral movement of the vehicle, and generating, by the computing system, a safe steer command by modifying the steer command based on the lane-keeping safe set with preview.

Abstract: Techniques are described for determining whether to process a job request. An example, method can include a device emitting a first pulse using a light detection and ranging (LIDAR) system coupled to an autonomous vehicle. The device can receive a first signal reflected off of an object. The device can emit a second pulse using the system, a threshold time interval being configured for the second laser pulse to hit the object in motion. The device can receive a second signal reflected off of the object. The device can determine a first time of flight information of the first signal and a second time of flight information of the second signal. The device can determine a velocity of the object based at least in part on the first time of flight information and the second time of flight information.

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: Embodiments provide a vehicle computer coupled to a vehicle. The vehicle computer may be configured to compute (e.g., generate) an automated lane change maneuver moving the vehicle from a first lane into a second, adjacent lane. The lane change maneuver may have commenced while the maneuver was safe to conduct, but a threat vehicle (or another threat object) may be subsequently detected moving into the same target lane. The option to immediately abort the maneuver and return to the original lane may not be appropriate after some time into the lane change maneuver. The vehicle computer may control the vehicle in a lateral position hold along the lane demarcation line for a predetermined amount of time before moving into the second lane when the threat vehicle clears the second lane or returning to the first lane when the threat vehicle does not clear the second lane.

Abstract: A representation of a first set of weights associated with a vehicle prior to a drive are received, the vehicle including a plurality of axles, a plurality of wheels attached to the plurality of axles, and a load. A warning is caused to be output in response to at least one weight from the first set of weights being outside a predetermined range. Longitudinal and lateral dynamics associated with the vehicle during the drive are determined. A second set of weights associated with at least one axle from the plurality of axles during the drive are determined. A determination is made if at least one remedial action should be performed based on the longitudinal dynamics, the lateral dynamics, and the second set of weights. In response to determining that the at least one remedial action should be performed, the at least one remedial action is caused to be performed.

Abstract: The present teaching relates to method, system, medium, and implementations for sensor calibration. A request is received from an ego vehicle in motion for assistance in collaborative calibration, when the ego vehicle determines that a sensor deployed thereon needs to be calibrated. The request includes at least one of a first position of the ego vehicle and a first configuration of the sensor with respect to the ego vehicle. An assisting vehicle is identified based on the first position, the first configuration, and a second position associated with the assisting vehicle and an assistance plan is generated in response to the request indicative of the assisting vehicle to travel to the ego vehicle to facilitate the calibration of the sensor by collaborating with the moving ego vehicle. Such generated assistance plan is then sent out to initiate the collaborative calibration.

Abstract: Methods, systems, and non-transitory computer-readable media are configured to perform operations comprising determining a rescue lane scenario for an environment, determining an amount of lateral bias for a vehicle, and generating planning and control data for the vehicle to laterally bias based on the amount of lateral bias.

Abstract: The present teaching relates to method, system, medium, and implementations for sensor calibration. An ego vehicle determines whether a sensor deployed thereon to facilitate autonomous driving 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 to a center. The request includes at least a first position of the ego vehicle on the route and a first configuration of the sensor with respect to the ego vehicle. When the ego vehicle receives a calibration assistant package, which includes information associated with a collaborative means present along the route and to be used to assist the ego vehicle to calibrate the sensor, it identifies, based on the information, the collaborative means along the route when the ego vehicle is in a vicinity of the collaborative means in order to capture a target present on the collaborative means to enable calibration of the sensor.

Abstract: The present teaching relates to generating an alert in a 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: A system includes an electronic control unit (ECU) having a housing and configured to be disposed within an interior of an autonomous vehicle. The system also includes an inertial measurement unit (IMU) rigidly disposed with the housing of the ECU. The system also includes a processor configured to receive measurement data from the IMU. The IMU and the processor is powered by a battery separate from a power source of the ECU. The processor is configured to identify a tamper event of the ECU based on the measurement data from the IMU. The processor is also configured to send a signal to cause a tamper response of the tamper event.

Abstract: Techniques are described for providing a hands-off steering wheel detection warning. An example method can include a vehicle computer determining a real-time level of fatigue of a driver of an autonomous vehicle. The method can further include the vehicle computer determining an operating parameter associated with an environment in which the autonomous vehicle is traveling. The method can further include the vehicle computer determining a time interval for providing a hands-off steering wheel detection warning based at least in part on the real-time level of fatigue of the driver, and the operating parameter. The method can further include the vehicle computer identifying a final time interval for providing a hands-off steering wheel detection warning. The method can further include the vehicle computer outputting the hands-off steering wheel detection warning after the final time interval has elapsed.

Abstract: A method for classifying environmental image frames for vehicles, using a universal iterative classification model, is presented. The method combines text-based querying, active machine-learning models, and user input to form an end-to-end automatic flow for sourcing video frames captured by sensors on a vehicle. An index of frames is used and continuously populated with data for new frames, with each frame scored on its likelihood of containing a representation of a driving scenario of interest. Each iteration of the classification model produces a classification result that is predicted to belong to the scenario of interest. Binary labels can be applied to the results. Subsequent iterative training of classification models can be performed using updated training sets containing previously labeled classification results, to improve precision and accuracy in classifying image data to a driving scenario.

Abstract: Embodiments described herein include a method of receiving, by a moving assisting vehicle, a calibration assistance request related to a moving ego vehicle that requested assistance in collaborative calibration of a sensor deployed on the moving ego vehicle. The method further includes analyzing the calibration assistance request to extract at least one of a schedule or an assistance route associated with the requested assistance. The method includes communicating with the moving ego vehicle about a desired location relative to the position of the moving ego vehicle for the moving assisting vehicle to be in order to assist the sensor to acquire information of a target present on the moving assisting vehicle. The method includes facilitating to drive the moving assisting vehicle to reach the desired location to achieve the collaborative calibration of the sensor on the moving ego vehicle.

Abstract: In some embodiments, a method can include executing a first machine learning model to detect at least one lane in each image from a first set of images. The method can further include determining an estimate location of a vehicle for each image, based on localization data captured using at least one localization sensor disposed at the vehicle. The method can further include selecting lane geometry data for each image, from a map and based on the estimate location of the vehicle. The method can further include executing a localization model to generate a set of offset values for the first set of images based on the lane geometry data and the at least one lane in each image. The method can further include selecting a second set of images from the first set of images based on the set of offset values and a previously-determined offset threshold.

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: Embodiments provide improved techniques for identifying object and vehicle locations on an HD map using camera images. Synthetic data may be generated by populating a high-definition (HD) map with an object. The populated HD map can be projected to a two-dimensional camera view image depicting the object. The HD map and object detection data generated from image (e.g., identifying lane/object locations within the image) can be used to train the model to identify HD map locations of an autonomous vehicle capturing images as it travels, as well as various objects detected from those captured images. Subsequently, a new image may be processed using object detection techniques to detect lane/object locations within the image. An HD map and the detected lane/object data may be provided to the model, which in turn identifies, on the HD map, the locations of the vehicle and the various objects.