Abstract: Embodiments provide a vehicle computer coupled to a vehicle. The vehicle computer may be configured to receive sensor data from a plurality of sensors of the autonomous vehicle, analyze the sensor data, predict a need for a steering torque overlay based on analyzing the sensor data, determine a steering torque overlay output based on at least the sensor data, provide the steering torque overlay output to a steering system of the vehicle, and control the steering system in view of the steering torque overlay output. The steering torque overlay output is provided to the steering system while the vehicle is transitioning from an autonomous mode or a manual mode to a hybrid mode where the steering system is controlled simultaneously by the vehicle computer and a driver when the steering torque overlay output is applied.

Abstract: Methods, systems, and non-transitory computer readable media are configured to perform operations comprising capturing a first sequence of captured data associated with a first time window; generating a second sequence of generated data associated with a second time window based on the first sequence of data; identifying a difference between the second sequence of generated data and a ground truth sequence of captured data associated with the second time window; determining whether the difference between the second sequence of generated data and the ground truth sequence of captured data satisfies a selected threshold value; detecting a change associated with an environment when the difference between the second sequence of generated data and the ground truth sequence of captured data satisfies the selected threshold value; and based on the detected change, providing navigation guidance to a vehicle travelling in the environment.

Abstract: Methods, systems, and non-transitory computer-readable media are configured to perform operations comprising determining lane detection data and object detection data associated with an environment; determining a lane template based on the lane detection data; and generating localization data that identifies a location of an object in the environment based on the lane template and the object detection data.

Abstract: A system includes sensors onboard an autonomous vehicle, a processor, and a memory. The memory stores instructions for the processor to receive a first image pair from the sensors at a first time and a second image pair from the sensors at a second time. Each image pair includes at least one static feature in an environment of the autonomous vehicle. The memory also stores instructions to determine a distance travelled by the autonomous vehicle between the first and second times, and to determine a correction to a disparity map based on (1) a first disparity associated with the static feature(s) and the first image pair, (2) a second disparity associated with the static feature(s) and the second image pair, and (3) the distance travelled. The memory also stores instructions to cause the correction to be applied to the disparity map.

Abstract: Methods, systems, and non-transitory computer readable media configured to perform operations comprise determining, by a computing system, speeds and stations of a plurality of leading vehicles traveling ahead of an ego vehicle; calculating, by the computing system, a coasting viability factor based on the speeds and stations of the plurality of leading vehicles and a speed and station of the ego vehicle; comparing, by the computing system, the coasting viability factor to a coasting viability threshold; and determining, by the computing system, whether the ego vehicle should coast based on the comparing.

Abstract: Techniques are described for scenario data generation. An example method can include processing location-specific sensor information of a traffic accident at a location on a road network, the location-specific sensor information received from a sensor arranged on an infrastructure at the road network. The method can further include determining a location-specific element based on the location-specific sensor information. The method can further include determining a location-specific contributing factor of the traffic accident based on the location-specific element. The method can further include generating a location-specific model for simulating the traffic accident based on the location-specific contributing factor. The method can further include determining control instructions for a location-specific driving maneuver for an autonomous vehicle to mitigate a future traffic accident based on the location-specific model.

Abstract: The present teaching relates to method, system, medium, and implementations for detecting a need for sensor adjustment. Information is received from an inertial measurement unit (IMU) attached to a sensor, including one or more measurements associated with the IMU, where the sensor is deployed on a vehicle for sensing surrounding information to facilitate autonomous driving. The one or more measurements are analyzed with respect to one or more corresponding known measurements of the IMU. The discrepancy between the one or more measurements and the one or more corresponding known measurements is used to determine whether an adjustment to the sensor is needed.

Abstract: An assembly is described. An assembly can include a housing comprising a first compartment and a plurality of vents letting air flow into and out. The assembly can further include a first ECU positioned within the first compartment, the first ECU comprising a first inlet letting air flow into the first ECU. The assembly can further include a second ECU positioned within the first compartment, the second ECU comprising a second inlet letting air flow into the second ECU. The assembly can further include a first plenum positioned between the first ECU and the second ECU and comprising a second compartment. The assembly can further include a first air duct forming a first channel letting air from a first vent of the plurality of vents into the second compartment, wherein the first plenum further comprises a second vent positioned to align with the first inlet of the first ECU.

Abstract: This application is directed to augmenting training images used for generating vehicle driving models. A computer system obtains a first image of a road, identifies within the first image a drivable area of the road, obtains an image of a traffic safety object, and determines a detour path on the drivable area. The computer system determines positions of a plurality of traffic safety objects to be placed adjacent to the detour path, and generates a second image from the first image by adaptively overlaying a respective copy of the image of the traffic safety object at each of the positions of the plurality of traffic safety objects on the drivable area within the first image. The second image is added to a corpus of training images to be used by a machine learning system to generate a model for facilitating driving a vehicle (e.g., at least partial autonomously).

Abstract: This application is directed to a centralized system for predicting vehicle trajectory. A computer system associated with a fixed installation having one or more first sensors obtains, from the first sensors, information of a plurality of vehicles that are traveling within a zone of interest of a road. The computer system generates a first travel trajectory for a first vehicle of the plurality of vehicles based on the information obtained from the one or more first sensors. The computer system sends, to the first vehicle, the first travel trajectory such that the first vehicle is configured to at least partially autonomously drive the first vehicle based on the first travel trajectory of the first vehicle.

Abstract: Techniques are described for scenario data generation. An example method can include processing location-specific sensor information of a traffic event at a location on a road network. The method can include determining a location-specific element based on the location-specific sensor information, the location-specific element associated with a flow of traffic at a time point. The method can further include generating a location-specific model of the traffic event based on the flow of traffic and the time point. The method can further include determining, by the computing system, control instructions for a location-specific driving maneuver for an autonomous vehicle to mitigate the traffic event based on the location-specific model. The method can further include causing the control instructions to be transmitted to an actuator of the autonomous vehicle to perform the location-specific driving maneuver thereby controlling the autonomous vehicle at the location based on the model.

Abstract: This application is directed to collecting traffic flow data to facilitate vehicle motion planning. A computer system collects, via a plurality of sensors that are positioned on a fixed installation at a road, data of vehicles that are traveling along a road. The computer system determines, according to the data, that the vehicles traveling along the road includes a first vehicle and a second vehicle ahead of the first vehicle, where the second vehicle is beyond a sensing range of the first vehicle. The computer system identifies, from the data, second vehicle data corresponding to the second vehicle, including a location and speed of the second vehicle. The computer system transmits the second vehicle data to the first vehicle such that the first vehicle is enabled to at least partially autonomously drive according to the second vehicle data and additional data collected by first sensors of the first vehicle.

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: 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: This application is directed to improving vehicle steering control of an autonomous vehicle. A first vehicle includes a first sensor, one or more processors, and memory. The first vehicle acquires, from at least the first sensor, first data of one or more first objects in a vicinity of the first vehicle. The first vehicle receives from a computer system associated with a fixed installation having at least a second sensor, via a wireless network, second data corresponding to one or more second objects detected by at least the second sensor. The first vehicle adjusts, by the one or more processors, a steering behavior of the first vehicle according to the first data and the second data. The first vehicle at least partially autonomously drives the vehicle in a first trajectory along the first lane of the road according to the steering behavior of the first vehicle.

Abstract: This application is directed to collecting event-based vehicle traffic data to facilitate driving a vehicle. A computer system includes sensors that are positioned on a fixed installation at a road, one or more processors, and memory. The computer system monitors, using the plurality of sensors on the fixed installation, vehicle traffic data (e.g., associated with one or more events) in a zone of interest of the road over a period of time to generate historical traffic data. The computer system uses the historical traffic data to train a driving model of an at least partially autonomous vehicle. The computer system sends the driving model to one or more vehicles. The driving model is configured to be used by the one or more vehicles to at least partially autonomously drive in a first trajectory while the one or more vehicles are traveling through a similar zone of interest.

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 vehicle computer can determine an operating parameter associated with an environment in which the autonomous vehicle is traveling. The vehicle computer can generate, using a machine learning model, a predicted driving pattern of a second vehicle traveling in the environment. The vehicle computer can determine 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, the operating parameter, and the predicted driving pattern of the second vehicle. The vehicle computer can identify a final time interval for providing a hands-off steering wheel detection warning. The vehicle computer can output the hands-off steering wheel detection warning after the final time interval has elapsed.

Abstract: Sensor data is received at a processor of a vehicle including at least one sensor, a plurality of axles, and a plurality of tires coupled to the plurality of axles. A determination is made by the processor if at least one tire from the plurality of tires is faulty based on the sensor data. A determination is made by the processor of at least one side of the vehicle and at least one axle from the set of axles associated with the at least one tire in response to the determining that the at least one tire is faulty. A determination is made by the processor of at least one remedial action to be performed by the vehicle based on the sensor data, the at least one side, and the at least one axle.

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: 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.