This technology relates to dynamically detecting, managing and mitigating driver fatigue in autonomous systems. For instance, interactions of a driver in a vehicle may be monitored to determine a distance or time when primary tasks associated with operation of the vehicle or secondary tasks issued by the vehicle computing were last performed. If primary tasks or secondary tasks are not performed within given distance thresholds or time limits, then one or more secondary tasks are initiated by the computing device of the vehicle. In another instance, potential driver fatigue, driver distraction or overreliance on an automated driving system is detected based on gaze direction or pattern of a driver. For example, a detected gaze direction or pattern may be compared to an expected gaze direction or pattern given the surrounding environment in a vicinity of the vehicle.

A vehicular collision avoidance system includes a sensor disposed at a vehicle for sensing exterior and forwardly of the vehicle. A processor processes sensor data captured by the sensor to determine the presence of a pedestrian ahead of the vehicle and outside a path of travel of the vehicle. The processor determines a projected path of travel of the pedestrian based on movement of the pedestrian. The processor determines where the forward path of travel of the vehicle intersects the projected path of travel of the pedestrian. The system, responsive at least in part to prediction that the pedestrian will be in the forward path of travel of the vehicle when the vehicle time to intersection elapses, adjusts the speed of the vehicle based at least in part on a driver attentiveness parameter pertaining to a determined attentiveness of a driver of the vehicle.

Operating an autonomous machine using analysis of machine vibration while it is operational. Accelerometers are used to measure the machines vibrations while it is being operated. If the vibrations exceed a predetermined acceleration a controller adjust the velocity of the machine to prevent/reduce further vibrations.

Techniques for determining a degraded state associated with a sensor are discussed herein. For example, a sensor associated with vehicle may captured data of an environment. A portion of the data may represent a portion of the vehicle. Data associated with a region of interest can be determined based on a calibration associated with the sensor. For example, in the context of image data, image coordinates may be used to determine a region of interest, while in the context of lidar data, a beam and/or azimuth can be used to determine a region of interest. A data metric can be determined for data in the region of interest, and an action can be determined based on the data metric. For example, the action can include cleaning a sensor, scheduling maintenance, reducing a confidence associated with the data, or slowing or stopping the vehicle.

A method, system and non-transitory computer readable medium for suppressing autonomous vehicle external human machine interface (eHMI) notifications to prevent confusion or distraction of a road user. The suppression conditions are based on situations where the eHMI notification is redundant, not necessary, can be simplified or should be modified due to weather or light intensity. The eHMI notifications can be suppressed on selected displays and enhanced on others. The eHMI notifications can include text and icons, flashing lights, color or light patterns, which can be modified or suppressed based on the determination that the eHMI notification will confuse or distract the road user. At a four-way stop intersection, the eHMI notification can be suppressed until the autonomous vehicle has the right-of-way. The autonomous vehicle can form a mesh network with connected vehicles at the intersection and broadcast a group eHMI while requesting that the connected vehicles suppress their eHMIs.

A system for controlling platooning by a following vehicle includes a sensor located in or on the following vehicle configured to detect data corresponding to a shape of a leading vehicle. The system further includes an electronic control unit (ECU) located in or on the following vehicle, coupled to the sensor, and configured to determine an optimal distance from the following vehicle to the leading vehicle based on the shape of the leading vehicle, the optimal distance corresponding to a distance at which drag applied to the following vehicle is reduced based on a pressure wake from the leading vehicle.

Evasive steering assist (ESA) systems and methods for a vehicle utilize a set of vehicle perception systems configured to detect an object in a path of the vehicle, a driver interface configured to receive steering input from a driver of the vehicle via a steering system of the vehicle, a set of steering sensors configured to measure a set of steering parameters, and a controller configured to determine a set of driver-specific threshold values for the set of steering parameters, compare the measured set of steering parameters and the set of driver-specific threshold values to determine whether to engage/enable an ESA feature of the vehicle, and in response to engaging/enabling the ESA feature of the vehicle, command the steering system to assist the driver in avoiding a collision with the detected object.

It is advantageous for a vehicle to detect road wetness or related environmental conditions. This is particularly true for self-driving vehicles, which can then adjust the manner of automated operation of the vehicle to increase safety by reducing speed, braking earlier, adjusting internal estimates of road traction parameters, or adjusting autonomous operation in some other manner. It is difficult to directly measure road wetness (e.g., using spectroscopy or other methods directed at the road surface), however, it is possible to indirectly estimate road wetness based on road noise audio signals detected via one or more microphones disposed on the vehicle. The location of the microphones, the type of post-processing applied to the audio signals, or other factors can be adapted to increase the useful road wetness-related content of such audio signals while reducing the presence of engine noise, road noise, or other confounding signals.

A system, comprising a computer including a processor and a memory storing instructions executable by the processor to determine a first lateral boundary for movement of a vehicle. The first lateral boundary is parallel to a longitudinal axis of the vehicle and is based on at least a size of the vehicle. Based on at least the size of the vehicle and a detected yaw rate of the vehicle, the instructions include to determine a wind condition at a location. The instructions include to update a distance of the first lateral boundary from the longitudinal axis to obtain an updated first lateral boundary, based on the wind condition.

A method for providing a positive decision signal for a vehicle which is about to perform a traffic scenario action. The method includes receiving information about at least one surrounding road user, which information is indicative of distance to the surrounding road user with respect to the vehicle and at least one of speed and acceleration of the surrounding road user; calculating a value based on the received information; providing the positive decision signal to perform the traffic scenario action when the calculated value is fulfilling a predetermined condition. The value is calculated based on an assumption that the surrounding road user will react on the traffic scenario action by changing its acceleration.