Abstract: The present invention extends to methods, systems, and computer program products for formulating lane level routing plans. In general, aspects of the invention are used in motorized vehicles to guide a driver to a terminal vehicle configuration according to a lane level routing plan that balances travel time with routing plan robustness. A lane level routing plan can be based on terminal guidance conditions (e.g., exiting a highway in the correct off ramp lane), statistical patterns of lanes themselves, current vehicle state, and state of the local environment near the vehicle. Lane level routing plans can be communicated to the driver with audio, visual, and/or haptic cues. Lane level routing plans can be revised online and in (essentially) real-time in response to changing conditions in the local environment (e.g., a trailing vehicle in a neighboring lane has decided to increase speed).

Abstract: A controller receives sensor data during a ride and provides it to a server system. A passenger further provides feedback concerning the ride in the form of some or all of an overall rating, flagging of ride anomalies, and flagging of road anomalies. The sensor data and feedback are input to a training algorithm, such as a deep reinforcement learning algorithm, which updates an artificial intelligence (AI) model. The updated model is then propagated to controllers of one or more autonomous vehicle which then perform autonomous navigation and collision avoidance using the updated AI model.

Abstract: A controller of an autonomous vehicle receives a travel itinerary of a passenger and an airport map. The controller then uses the airport map to arrive at a terminal corresponding to the passenger's travel itinerary. Upon arrival at the terminal the controller communicates with parked vehicles (V2V) or infrastructure to identify an unoccupied parking spot and then autonomously parks. When picking up a passenger, the controller determines whether the passenger has checked luggage and adjusts and arrival time accordingly and may account for the storage volume of the luggage. The controller may also loop a circuit at the airport where a wait time has been exceeded. In some embodiments, augmented reality may be used to help the passenger identify the vehicle.

Abstract: An autonomous vehicle includes a microphone sensing sounds in the interior of the vehicle. The output of the interior microphone is processed according to an unsupervised machine learning model such that anomalies are indicated by the model. In response to detection of an anomaly, a remote dispatcher is notified, who may then dismiss the anomaly or transmit an instruction to the vehicle to alter its trajectory. The output of an exterior microphone and infotainment system may be removed from the output of the interior microphone prior to processing. An anomaly may be found to occur in response to detecting speaking of a keyword in the output of the interior microphone.

Abstract: Systems, devices and methods are described for physiological monitoring, for example monitoring EEG signals to detect the onset or probability of adverse events. The systems, devices and methods discussed herein may monitor received EEG signals to identify trends or patterns in the signal that are either indicative of ongoing seizures or indicative of a future risk of seizure. The systems, devices and methods provide the user with increased control and flexibility in the monitoring processes that produce the alerts. In particular, in some implementations the physician is able to make adjustments during monitoring and customize the process by which EEG data is displayed and analyzed during the patient monitoring without pausing the monitoring to make the adjustments.

Abstract: A controller receives outputs from a plurality of sensors such as a camera, LIDAR sensor, RADAR sensor, and ultrasound sensor, which may be rearward facing. A probability is updated each time a feature in a sensor output indicates presence of an object. The probability may be updated as a function of a variance of the sensor providing the output and a distance to the feature. Where the variance of a sensor is directional, directional probabilities may be updated according to these variances and the distance to the feature. If the probability meets a threshold condition, actions may be taken such as a perceptible alert or automatic braking. The probability may be decayed in the absence of detection of objects. Increasing or decreasing trends in the probability may be amplified by further increasing or decreasing the probability.

Abstract: A method is disclosed for using an autonomous vehicle to teach a student how to drive. The method may include generating, by the autonomous vehicle during a training session, a plan for navigating a section of road. During the training session, the autonomous vehicle may receive control instructions from the student regarding how the student would like to navigate the section of road. If the autonomous vehicle determines that implementing the instructions would be safe and legal, it may implement those instructions. However, if the autonomous vehicle determines that implementing the instructions would not be safe and legal, it may execute the plan. During a training session, an autonomous vehicle may provide on a head-up display to the student certain visual feedback regarding a driving performance of the student.

Abstract: Systems, methods, and devices for predicting driver intent and future movements of a human driven vehicles are disclosed herein. A computer implemented method includes receiving an image of a proximal vehicle in a region near a vehicle. The method includes determining a region of the image that contains a driver of the proximal vehicle, wherein determining the region comprises determining based on a location of one or more windows of the proximal vehicle. The method includes processing image data only in the region of the image that contains the driver of the proximal vehicle to detect a driver's body language.

Abstract: Methods, devices and apparatuses pertaining to identifying the candidate road segments for autonomous operations are described. A method may involve obtaining data of a first plurality of road segments that are permitted for one or more autonomous operations. The method may further include generating a road segment template based on the data of the first plurality of road segments, and reconstructing a second plurality of road segments based on the road segment template to obtain a plurality of reconstructed road segments.

Abstract: Methods and apparatus pertaining to an intelligent vehicle access point opening system are provided. A method may involve detecting a presence of an object in a vicinity of a cover of an access point of a vehicle. The method may also involve receiving a command to open the cover and activating a mechanism to open the cover responsive to receiving the command. The method may further involve determining whether the object is likely to fall as the cover is being opened. The method may additionally involve pausing opening of the cover responsive to a determination that the object is likely to fall as the cover is being opened.

Abstract: A computing system can crop an image based on a width, height and location of a first vehicle in the image. The computing system can estimate a pose of the first vehicle based on inputting the cropped image and the width, height and location of the first vehicle into a deep neural network. The computing system can then operate a second vehicle based on the estimated pose. The computing system may train a model to identify a type and a location of a hazard according to the estimated pose, the hazard being such things as ice, mud, pothole, or other hazard. The model may be used by an autonomous vehicle to identify and avoid hazards or to provide drive assistance alerts.

Abstract: An autonomous vehicle includes a microphone sensing sounds in the interior of the vehicle. The output of the interior microphone is processed according to an unsupervised machine learning model such that anomalies are indicated by the model. In response to detection of an anomaly, a remote dispatcher is notified, who may then dismiss the anomaly or transmit an instruction to the vehicle to alter its trajectory. The output of an exterior microphone and infotainment system may be removed from the output of the interior microphone prior to processing. An anomaly may be found to occur in response to detecting speaking of a keyword in the output of the interior microphone.

Abstract: A vehicle includes one or more laterally mounted microphones and a controller programmed to detect a signature of an unoccupied position adjacent the vehicle in outputs of the microphones. The signature may be identified using a machine learning algorithm. In response to detecting an unoccupied position, the controller may invoke autonomous parking, store the location of the unoccupied position for later use, and/or report the unoccupied position to a server, which then informs other vehicles of the available parking. The unoccupied position may be verified by evaluating whether map data indicates legal parking at that location. The unoccupied position may also be confirmed with one or more other sensors, such as a camera, LIDAR, RADAR, SONAR, or other type of sensor.

Abstract: Systems, methods, and devices for detecting a vehicle's turn signal status for collision avoidance during lane-switching maneuvers or otherwise. A method includes detecting, at a first vehicle, a presence of a second vehicle in an adjacent lane. The method includes identifying, in an image of the second vehicle, a sub-portion containing a turn signal indicator of the second vehicle. The method includes processing the sub-portion of the image to determine a state of the turn signal indicator. The method also includes notifying a driver or performing a driving maneuver, at the first vehicle, based on the state of the turn signal indicator.

Abstract: A computing system can crop an image based on a width, height and location of a first vehicle in the image. The computing system can estimate a pose of the first vehicle based on inputting the cropped image and the width, height and location of the first vehicle into a deep neural network. The computing system can then operate a second vehicle based on the estimated pose. The computing system may train a model to identify a type and a location of a hazard according to the estimated pose, the hazard being such things as ice, mud, pothole, or other hazard. The model may be used by an autonomous vehicle to identify and avoid hazards or to provide drive assistance alerts.

Abstract: Methods, devices and apparatuses pertaining to aberrant driver classification and reporting are described. A method may involve receiving a message from a user of a first vehicle, the message indicating an instance of aberrant driving of a second vehicle. The method may also involve determining that the instance has occurred using one or more classifiers. The method may further involve collecting information of the second vehicle and generating a warning message based on the information.

Abstract: Methods, devices and apparatuses pertaining to aberrant driver classification and reporting are described. A method may involve receiving a message from a user of a first vehicle, the message indicating an instance of aberrant driving of a second vehicle. The method may also involve determining that the instance has occurred using one or more classifiers. The method may further involve collecting information of the second vehicle and generating a warning message based on the information.

Abstract: A method for mitigating hazards to access to passenger vehicles. The method includes detecting, with one or more sensors, a hazardous condition in an area proximate a vehicle. A processor may calculate a safety metric corresponding to the hazardous condition and analyze the safety metric relative to a predetermined threshold. A vehicle occupant may be automatically notified of the hazardous condition in the event the safety metric satisfies the predetermined threshold. A corresponding system is also claimed herein.

Abstract: A method for generating training data is disclosed. The method may include executing a simulation process. The simulation process may include traversing a virtual camera through a virtual driving environment comprising at least one virtual precipitation condition and at least one virtual no precipitation condition. During the traversing, the virtual camera may be moved with respect to the virtual driving environment as dictated by a vehicle-motion model modeling motion of a vehicle driving through the virtual driving environment while carrying the virtual camera. Virtual sensor data characterizing the virtual driving environment in both virtual precipitation and virtual no precipitation conditions may be recorded. The virtual sensor data may correspond to what a real sensor would have output had it sensed the virtual driving environment in the real world.

Abstract: A system for detecting and identifying foliage includes a tracking component, a tracking parameters component, and a classification component. The tracking component is configured to detect and track one or more features within range data from one or more sensors. The tracking parameters component is configured to determine tracking parameters for each of the one or more features. The tracking parameters include a tracking age and one or more of a detection consistency and a position variability. The classification component is configured to classify a feature of the one or more features as corresponding to foliage based on the tracking parameters.