Abstract: Certain embodiments of the present disclosure provide techniques for eliminating an appearance of one or more objects in an environment surrounding a vehicle during operation of the vehicle. A method generally includes detecting the one or more objects in first sensor data collected from first sensors onboard the vehicle, wherein the first sensor data is representative of the environment surrounding the vehicle; receiving, from other vehicles, second sensor data collected from second sensors onboard the other vehicles, wherein the second sensor data is representative of the environment surrounding each of the one or more other vehicles; generating one or more augmented reality images depicting portions of the environment obstructed by the one or more objects in the first sensor data; displaying the one or more augmented reality images in an augmented reality display such that the augmented reality images are positioned to overlay the one or more objects.

Abstract: A system for detecting a cognitive status of a driver of a vehicle includes a first sensor configured to detect vehicle performance data, a second sensor configured to detect driver condition data, and an electrical control unit (ECU) coupled to the first sensor and the second sensor. The ECU is configured to receive the vehicle performance data corresponding to the cognitive status of the driver, receive the driver condition data corresponding to the cognitive status of the driver, and transmit the vehicle performance data and the driver condition data to a remote device.

Abstract: A driver monitor system and method of optimizing use of cognitive resources of a user in a vehicle is disclosed. The system includes in-cabin video cameras, a display device, and processing circuitry. The method performed by the processing circuitry includes monitoring eye gaze direction using images from the in-cabin video cameras, gradually softening image regions and lines displayed in the display device that are outside a first vision area of the user of the vehicle, and using the eye gaze direction to predict whether the user is transitioning into a new focus vision area that had been outside the first vision area. When the user's eyes begin to move, begin sharpening softened image regions and lines such that by the time eyes have shifted into the new focus vision area, the image regions and lines in the new focus vision area reach full sharpness.

Abstract: Based on a model and an identity of an operator of a vehicle, a setting for a cruise control to maintain a distance between the vehicle and a preceding vehicle can be determined. The model can be based on historical distances maintained by the operator. The model can also be based on information about an environment in which the vehicle is operating or information about a state of the operator. The identity can be received from an interface configured to receive information associated with the identity, a cloud computing platform, or a biometric system configured to determine the identity. The cruise control can be caused to operate according to the setting. A rate of energy consumption by the vehicle when the vehicle is controlled by the cruise control can be less than the rate of energy consumption when the vehicle lacks being controlled by the cruise control.

Abstract: Methods, systems, and apparatus for transporting cargo. The system includes a vehicle having an electronic control unit (ECU) configured to communicate vehicle cargo data, the vehicle cargo data including dimensions of a cargo compartment and dimensions of one or more cargo compartment access points. The system also includes a mobile device configured to receive a transportation request from a user, the transportation request including a destination location and user cargo data. The system also includes a remote data server. The remote data server is configured to determine whether user cargo fits within the vehicle based on the vehicle cargo data and the user cargo data. The remote data server is configured to communicate an indication to the mobile device that the user cargo fits within the vehicle, causing the mobile device to render a user interface to provide a notification that the user cargo fits within the vehicle.

Abstract: A roadmanship system comprises a computational device and a vehicle comprising a plurality of sensors and a vehicle control system in communication with the computational device and the plurality of sensors. The computational device can be configured to: (i) receive driving data from a group of vehicles; (ii) calculate a regression curve based on the driving data; (iii) calculate a threshold value of an engineering parameter based on the regression curve and a predetermined roadmanship level; and (iv) output the threshold value to the vehicle control system. The vehicle control system can be configured to: (a) receive the threshold value from the computational device; (b) receive operational information associated with at least one of the vehicle and a driving environment surrounding the vehicle from the plurality of sensors; and (c) cause the vehicle to perform a vehicle maneuver based on the threshold value and the operational information.

Abstract: A method, system and non-transitory computer readable medium which monitor a road user in order to move the external position of the vehicle intent notification (eHMI) to another external position that can be seen by the road user based on the gaze direction of the road user. In some aspects, the eHMI notification displays the vehicle intent for a single autonomous vehicle. In another aspect, a group eHMI notification displays the trajectories for a plurality of autonomous and non-autonomous vehicles. Based on the gaze direction of the road user, the eHMI notification can be displayed on a single external position or on multiple external positions. Different eHMI notifications can be displayed at different external positions on the autonomous vehicle to provide information to more than one road user.

Abstract: 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.

Abstract: A system and a method for adjusting a yielding space of a platoon are provided. The system can include camera modules, sensors, interface circuitry, processing circuitry, and memory. The sensors can detect one or more behaviors of one or more vehicles adjacent to the platoon. The processing circuitry can determine one or more triggering events between the one or more vehicles and the platoon based on the one or more behaviors. The processing circuitry can adjust the yielding space of the platoon.

Abstract: Based on a model and an identity of an operator of a vehicle, a setting for a cruise control to maintain a distance between the vehicle and a preceding vehicle can be determined. The model can be based on historical distances maintained by the operator. The model can also be based on information about an environment in which the vehicle is operating or information about a state of the operator. The identity can be received from an interface configured to receive information associated with the identity, a cloud computing platform, or a biometric system configured to determine the identity. The cruise control can be caused to operate according to the setting. A rate of energy consumption by the vehicle when the vehicle is controlled by the cruise control can be less than the rate of energy consumption when the vehicle lacks being controlled by the cruise control.

Abstract: Systems and methods for alerting a pedestrian of the density of automated vehicles to non-automated vehicles in an area by broadcasting a visual signal representing a group mode status of automated vehicles travelling towards or within a roadway intersection. The group mode status may be broadcast as a percentage of automated vehicles to total vehicles or can be broadcast as a ratio of automated vehicles to non-automated vehicles on an external mode indicator of a subject automated vehicle. The visual signal may be broadcast as a pattern of lighting, a change in light intensity, a change in colors or a change in text. The group mode status may also be broadcast to a pedestrian equipped with a wireless device via vehicle-to-infrastructure communications. The group status may also be broadcast to and displayed on infrastructure component, such as a street light, display post, crosswalk sign, or building display configured to receive signals.

Abstract: A roadmanship system comprises a computational device and a vehicle comprising a plurality of sensors and a vehicle control system in communication with the computational device and the plurality of sensors. The computational device can be configured to: (i) receive driving data from a group of vehicles; (ii) calculate a regression curve based on the driving data; (iii) calculate a threshold value of an engineering parameter based on the regression curve and a predetermined roadmanship level; and (iv) output the threshold value to the vehicle control system. The vehicle control system can be configured to: (a) receive the threshold value from the computational device; (b) receive operational information associated with at least one of the vehicle and a driving environment surrounding the vehicle from the plurality of sensors; and (c) cause the vehicle to perform a vehicle maneuver based on the threshold value and the operational information.

Abstract: A driver monitor method and system for mitigating cognitive tunneling in a vehicle includes in-cabin video cameras, a heart rate monitor, an audio-visual device, and processing circuitry that detects eye gaze direction, eye lid position, and head position using images from the in-cabin video cameras, and measures heart rate variability using the heart rate monitor. A machine learning device uses the eye gaze direction, eye lid position, head position, and heart rate variability to predict whether a driver is transitioning into a cognitive tunneling state or a fatigue state. The audio-visual device outputs one audio-visual cue to mitigate the cognitive tunneling state and outputs a different cue to mitigate the fatigue state. The machine learning device learns by performing a reinforcement learning algorithm in which the audio-video device outputs a verification request and receives a response to the verification request that is fed back to the machine learning device.

Abstract: System, methods, and other embodiments described herein relate to addressing inconsistencies between a trajectory plan and a communication from an occupant of an autonomously operated vehicle. A method of resolving inconsistent communication includes obtaining a trajectory plan for the vehicle, detecting, using one or more internal sensors, body language of the occupant, analyzing sensor data from the one or more internal sensors to determine a verbal or non-verbal communication indication by the occupant, and detecting an inconsistency between the verbal or non-verbal communication indication and the trajectory plan and: 1) modifying the trajectory plan to form a modified trajectory plan aligned with the verbal or non-verbal communication indication, or 2) transmitting a notification to the occupant prompting the occupant to adjust the verbal or non-verbal communication indication.

Abstract: A system and a method for adjusting a yielding space of a platoon are provided. The system can include camera modules, sensors, interface circuitry, processing circuitry, and memory. The sensors can detect one or more behaviors of one or more vehicles adjacent to the platoon. The processing circuitry can determine one or more triggering events between the one or more vehicles and the platoon based on the one or more behaviors. The processing circuitry can adjust the yielding space of the platoon.

Abstract: A system and method for a host vehicle to assess risk for road users is disclosed. The host vehicle monitors its surrounding driving environment and sends alerts to road users if a threat is detected. The host vehicle can determine the best course of action to mitigate a threat, and coordinate such action. The host vehicle comprises processing circuitry configured to perform the foregoing tasks. Further, the host vehicle may use a machine learning-based system comprising a trained neural network.

Abstract: Methods, systems, and apparatus for transporting cargo. The system includes a vehicle having an electronic control unit (ECU) configured to communicate vehicle cargo data, the vehicle cargo data including dimensions of a cargo compartment and dimensions of one or more cargo compartment access points. The system also includes a mobile device configured to receive a transportation request from a user, the transportation request including a destination location and user cargo data. The system also includes a remote data server. The remote data server is configured to determine whether user cargo fits within the vehicle based on the vehicle cargo data and the user cargo data. The remote data server is configured to communicate an indication to the mobile device that the user cargo fits within the vehicle, causing the mobile device to render a user interface to provide a notification that the user cargo fits within the vehicle.

Abstract: Systems and methods for alerting a pedestrian of the density of automated vehicles to non-automated vehicles in an area by broadcasting a visual signal representing a group mode status of automated vehicles travelling towards or within a roadway intersection. The group mode status may be broadcast as a percentage of automated vehicles to total vehicles or can be broadcast as a ratio of automated vehicles to non-automated vehicles on an external mode indicator of a subject automated vehicle. The visual signal may be broadcast as a pattern of lighting, a change in light intensity, a change in colors or a change in text. The group mode status may also be broadcast to a pedestrian equipped with a wireless device via vehicle-to-infrastructure communications. The group status may also be broadcast to and displayed on infrastructure component, such as a street light, display post, crosswalk sign, or building display configured to receive signals.

Abstract: A driver monitor method and system for mitigating cognitive tunneling in a vehicle includes in-cabin video cameras, a heart rate monitor, an audio-visual device, and processing circuitry that detects eye gaze direction, eye lid position, and head position using images from the in-cabin video cameras, and measures heart rate variability using the heart rate monitor. A machine learning device uses the eye gaze direction, eye lid position, head position, and heart rate variability to predict whether a driver is transitioning into a cognitive tunneling state or a fatigue state. The audio-visual device outputs one audio-visual cue to mitigate the cognitive tunneling state and outputs a different cue to mitigate the fatigue state. The machine learning device learns by performing a reinforcement learning algorithm in which the audio-video device outputs a verification request and receives a response to the verification request that is fed back to the machine learning device.

Abstract: A driver monitor system and method of optimizing use of cognitive resources of a user in a vehicle is disclosed. The system includes in-cabin video cameras, a display device, and processing circuitry. The method performed by the processing circuitry includes monitoring eye gaze direction using images from the in-cabin video cameras, gradually softening image regions and lines displayed in the display device that are outside a first vision area of the user of the vehicle, and using the eye gaze direction to predict whether the user is transitioning into a new focus vision area that had been outside the first vision area. When the user's eyes begin to move, begin sharpening softened image regions and lines such that by the time eyes have shifted into the new focus vision area, the image regions and lines in the new focus vision area reach full sharpness.