A method for determining an integrity range of a parameter estimation is disclosed. The integrity range describes the range in which an estimated parameter lies with a minimum probability. The method includes at least the following steps: a) ascertaining first integrity information on the basis of at least data from at least one first sensor or on the basis of a first method for determining the integrity information, b) ascertaining second integrity information on the basis of at least data from at least one second sensor that is different from the first sensor or on the basis of a second method that is different from the first method, for determining the integrity information, and c) determining the integrity range by merging at least the first integrity information and the second integrity information.

A method for determining a slip angle λ.sub.r of a rear wheel of a single-track motor vehicle for the purpose of traction control of the rear wheel of the single-track motor vehicle by means of a closed loop control Is provided. The slip angle λ.sub.r of the rear wheel is determined as a feedback value of the closed loop using at least one of three model-based steps. A slip angle λ.sub.r1, λ.sub.r2 or λ.sub.r3 is determined by one of the three steps representing the slip angle λ.sub.r or the slip angle λ.sub.r is determined from at least two of the slip angles λ.sub.r1, λ.sub.r2 and λ.sub.r3.

Disclosed embodiments include techniques for detecting an acknowledgement from a driver of a vehicle. A driver state and reaction tracking system determines that a detected condition exceeds a threshold hazard potential with the vehicle. The driver state and reaction tracking system detects a gesture, by a driver, associated with the detected condition. The driver state and reaction tracking system, in response to detecting the gesture, reduces an urgency level for alerting the driver of the detected condition to generate a reduced urgency level. The driver state and reaction tracking system determines whether to issue an alert to the driver based on the reduced urgency level. The driver state and reaction tracking system may transmit an acknowledgement to other systems associated with pedestrians, bicyclists, motorcyclists, semiautonomous vehicles, manually driven vehicles and/or the like. to confirm that the driver has seen the pedestrian, bicyclist, motorcyclist, or vehicle associated with the other system.

A framework for safely operating autonomous machinery, such as vehicles and other heavy equipment, in an in-field or off-road environment, includes detecting, identifying, and tracking objects from on-board sensors configured with the autonomous machinery as it performs activities in either an agricultural setting or a transportation environment. The framework generates commands for navigational control of autonomously-operated vehicles in response to detected objects and predicted tracks thereof for safe operation in the performance of those activities. The framework processes image data and range data in multiple fields of view around the autonomously-operated to discern and track objects in a deep learning to accurately interpret this data for determining and effecting such navigational control.

Techniques relating to monitoring map consistency are described. In an example, a monitoring component associated with a vehicle can receive sensor data associated with an environment in which the vehicle is positioned. The monitoring component can generate, based at least in part on the sensor data, an estimated map of the environment, wherein the estimated map is encoded with policy information for driving within the environment. The monitoring component can then compare first information associated with a stored map of the environment with second information associated with the estimated map to determine whether the estimated map and the stored map are consistent. Component(s) associated with the vehicle can then control the object based at least in part on results of the comparing.

In one embodiment, method for real-time monitoring of a safety redundancy autonomous driving system operating within a predefined risk tolerable boundary includes calculating a zone failure risk score for each of predetermined zones based on a sensor failure risk score associated with each of sensors mounted on the ADV. The predetermined zones being defined based on a sensor layout of the sensors. A sensor capability coverage of the ADV is determined based on the zone failure risk score associated with each of the predetermined zones. A drivable area of the ADV is determined based on the sensor capability coverage in view of map data associated with a current location of the ADV. A trajectory is planned based on the drivable area to autonomously drive the ADV to navigate a driving environment surrounding the ADV.

A vehicle configured to operate in an autonomous mode is provided. The vehicle is configured to obtain an indication of a final destination, and, if the final destination is not on a pre-approved road for travel by the vehicle, the vehicle is configured to determine a route from the vehicle's current location to an intermediary destination. The vehicle is further configured to determine a means for the vehicle user to reach the final destination from the intermediate destination.

Various applications for use of mass estimations of a vehicle, including to control operation of the vehicle, sharing the mass estimation with other vehicles and/or a Network Operations Center (NOC), organizing vehicles operating in a platoon and/or partially controlling the operation of one or more vehicles operating in a platoon based on the relative mass estimations between the platooning vehicles. When vehicles are operating in a platoon, the relative mass between a lead and a following vehicle may be used to scale torque and/or brake commands generated by the lead vehicle and sent to the following vehicle.

Systems and methods for predicting object motion and controlling autonomous vehicles are provided. In one example embodiment, a computer implemented method includes obtaining state data indicative of at least a current or a past state of an object that is within a surrounding environment of an autonomous vehicle. The method includes obtaining data associated with a geographic area in which the object is located. The method includes generating a combined data set associated with the object based at least in part on a fusion of the state data and the data associated with the geographic area in which the object is located. The method includes obtaining data indicative of a machine-learned model. The method includes inputting the combined data set into the machine-learned model. The method includes receiving an output from the machine-learned model. The output can be indicative of a plurality of predicted trajectories of the object.

An ECU performs collision avoidance control for an avoiding collision with an object based on at least one of first information, which is a detection result of the object based on a reflected wave corresponding to a transmission wave, and second information, which is a detection result of the object based on a captured image of an area in front of a vehicle captured by an image capturing means. When the state changes from a state where the object is detected by the first information and the second information to a state where the object is detected only by the first information, the ECU determines whether or not the object is located in a near area predetermined as an area in front of the vehicle in which the second information is not be able be acquired. When it is determined that the object is located in the near area, the ECU maintains an activation condition of the collision avoidance control to that in the state where the object is detected by the first information and the second information.