Abstract: Aspects of the disclosure provide for determining when to provide and providing secondary disengage alerts for a vehicle having autonomous and manual driving modes. For instance, while the vehicle is being controlled in the autonomous driving mode, user input is received at one or more user input devices of the vehicle. In response to receiving the user input, the vehicle may be transitioned from the autonomous driving mode to a manual driving mode and provide a primary disengage alert to an occupant of the vehicle regarding the transition. Whether to provide a secondary disengage alert may be determined based on at least circumstances of the user input. After the transition, the secondary disengage alert may be provided based on the determination.

Abstract: Aspects of the disclosure relate to analyzing head movements in a test driver tasked with monitoring the driving of a vehicle operating in an autonomous driving mode. For instance, a sensor may be used to capture sensor data of a test driver's head for a period of time. The sensor data may be analyzed to determine whether the test driver's head moved sufficiently enough to suggest that the test driver is engaged in monitoring the driving of the vehicle. Based on the determination of whether the test driver's head moved sufficiently enough, an intervention response may be initiated.

Abstract: Assessing a likelihood of a person experiencing a fatigue event when the person tasked with monitoring a vehicle operating in an autonomous driving mode may include receiving a set of response times for a psychomotor vigilance test administered to the person. The test may include a plurality of trials which involve a person lifting a finger from a user input device. Whether the person passed or failed each trial of the set of trials may be determined. A model trained using data from prior psychomotor vigilance tests administered to the person may be identified for the person. Results of the determinations of whether the person passed or failed each trial of the set of trials may be input into the model in order to determine a value representative of a likelihood of a fatigue event. An intervention response may be initiated based on the value.

Abstract: Aspects of the disclosure provide for determining when to provide and providing secondary disengage alerts for a vehicle having autonomous and manual driving modes. For instance, while the vehicle is being controlled in the autonomous driving mode, user input is received at one or more user input devices of the vehicle. In response to receiving the user input, the vehicle may be transitioned from the autonomous driving mode to a manual driving mode and provide a primary disengage alert to an occupant of the vehicle regarding the transition. Whether to provide a secondary disengage alert may be determined based on at least circumstances of the user input. After the transition, the secondary disengage alert may be provided based on the determination.

Abstract: Methods, systems, and apparatus, including computer programs encoded on computer storage media, for using comfort scales to assess the performance of autonomous vehicles. One of the methods includes receiving data representing a traffic encounter between a vehicle and a pedestrian. A plurality of comfort scale ratings of the encounter assigned by a rater belonging to a first rater pool are received. An input element is generated for a rating transformation model configured to predict how comfort scale ratings assigned by a particular rater pool would have been assigned by a representative rater belonging to a reference rater pool. An inference pass is performed over the rating transformation model using the input element to obtain a plurality of transformed comfort scale ratings for the reference rater pool.

Abstract: Assessing a likelihood of a person experiencing a fatigue event when the person tasked with monitoring a vehicle operating in an autonomous driving mode may include receiving a set of response times for a psychomotor vigilance test administered to the person. The test may include a plurality of trials which involve a person lifting a finger from a user input device. Whether the person passed or failed each trial of the set of trials may be determined. A model trained using data from prior psychomotor vigilance tests administered to the person may be identified for the person. Results of the determinations of whether the person passed or failed each trial of the set of trials may be input into the model in order to determine a value representative of a likelihood of a fatigue event. An intervention response may be initiated based on the value.

Abstract: Aspects of the disclosure relate to models for estimating the likelihood of fatigue in test drivers. In some instances, training data including videos of the test drivers while such test drivers are tasked with monitoring driving of a vehicle operating in an autonomous driving mode may be identified. The training data also includes driver drowsiness values generated from one or more human operators observing the videos. The training inputs and outputs may be used to train the model such that when a new video of a first test driver is input into the model, the model will output an estimate of a likelihood of fatigue for that test driver.

Abstract: 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.

Abstract: The disclosure provides for a system that includes one or more cabin components in a vehicle and one or more computing devices of the vehicle. The one or more computing devices are configured to determine that a vehicle will have an acceleration in a first direction at a future time and an acceleration time associated with the acceleration of the vehicle, alter the one or more cabin components of the vehicle before the acceleration time, and adjust the one or more cabin components of the vehicle based on the acceleration of the vehicle over time.

Abstract: 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.

Abstract: Aspects of the disclosure provide for determining when to provide and providing secondary disengage alerts for a vehicle having autonomous and manual driving modes. For instance, while the vehicle is being controlled in the autonomous driving mode, user input is received at one or more user input devices of the vehicle. In response to receiving the user input, the vehicle may be transitioned from the autonomous driving mode to a manual driving mode and provide a primary disengage alert to an occupant of the vehicle regarding the transition. Whether to provide a secondary disengage alert may be determined based on at least circumstances of the user input. After the transition, the secondary disengage alert may be provided based on the determination.

Abstract: A pre-formed cone filling apparatus (100) is disclosed for filling paper cones (130) with a ground product such as tobacco or another leaf- or plant-based product such as cannabis The apparatus 100 has individual cone holders (110) mounted vertically on the circumference of a horizontally-oriented rotating wheel (120). The cone holders move in pairs between a number of processing stations which are located around the circumference of the wheel (120). The processing stations include a loading station (200) for loading an individual cone (130) into each cone holder (110), a seating station (300) to ensure the cones (130) are correctly seated in the holders (110) and correctly shaped, a filling station (500) for filling a cone with the product, and a closing station (600) for closing the open end of the filled cone. The filling station also has primary and secondary tamping pins for controlling the product density during and after filling.

Abstract: Aspects of the disclosure provide for determining when to provide and providing secondary disengage alerts for a vehicle having autonomous and manual driving modes. For instance, while the vehicle is being controlled in the autonomous driving mode, user input is received at one or more user input devices of the vehicle. In response to receiving the user input, the vehicle may be transitioned from the autonomous driving mode to a manual driving mode and provide a primary disengage alert to an occupant of the vehicle regarding the transition. Whether to provide a secondary disengage alert may be determined based on at least circumstances of the user input. After the transition, the secondary disengage alert may be provided based on the determination.

Abstract: 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.

Abstract: Aspects of the disclosure provide for determining when to provide and providing secondary disengage alerts for a vehicle having autonomous and manual driving modes. For instance, while the vehicle is being controlled in the autonomous driving mode, receiving user input at one or more using input devices of the vehicle. In response to receiving the user input, the vehicle may be transitioned from the autonomous driving mode to a manual driving mode and provide a primary disengage alert to an occupant of the vehicle regarding the transition. Whether to provide a secondary disengage alert may be determined based on at least circumstances of the user input. After the transition, the secondary disengage alert may be provided based on the determination.

Abstract: Aspects of the disclosure relate to models for estimating the likelihood of fatigue in test drivers. In some instances, training data including videos of the test drivers while such test drivers are tasked with monitoring driving of a vehicle operating in an autonomous driving mode may be identified. The training data also includes driver drowsiness values generated from one or more human operators observing the videos. The training inputs and outputs may be used to train the model such that when a new video of a first test driver is input into the model, the model will output an estimate of a likelihood of fatigue for that test driver.