Abstract: A system for an automated-taxi includes a display and a controller. The display is viewable by one or more of a plurality of clients of an automated-taxi. The controller-circuit is in communication with the display. The controller is configured to determine a seating-arrangement of the plurality of clients transported by the automated-taxi. The seating-arrangement is determined in accordance with destinations of the plurality of clients. The controller is also configured to operate the display to show the seating-arrangement to one or more of the plurality of clients. A method of operating an automated-taxi includes receiving destinations of a plurality of clients to transported by an automated-taxi; determining a seating-arrangement of the plurality of clients in accordance with the destinations; and operating a display viewable by one or more of a plurality of clients of an automated-taxi to show the seating-arrangement to one or more of the plurality of clients.

Abstract: Disclosed are computer systems and techniques for authenticating a passenger of an autonomous vehicle and operating doors of the autonomous vehicle. For passenger authentication, the computer system is configured to receive a ride request, generate a passcode, transmit the passcode to a user account and the autonomous vehicle, authenticate the user using the passcode, and enable departure of the autonomous vehicle. For door operation, the computer system is configured to detect environmental conditions surrounding an autonomous vehicle, determine based on a set of operational conditions whether one or more doors of the autonomous vehicle are desirable for use, and operate a door if the door is safe to operate and desirable for operation.

Abstract: Among other things, a vehicle is caused to drive autonomously through a road network toward a defined goal position, and, if no potential stopping place that is in the vicinity of the goal position and is acceptable and feasible can be identified, a human operator not onboard the vehicle is enabled to drive the vehicle to a stopping place.

Abstract: Provided are methods for track segment cleaning of tracked objects using neural networks, which can include detecting a first track segment and a second track segment. The method includes applying a machine learning model trained to determine if the first track segment and second track segment capture real objects and if the first track segment and the second track segment are representative of an identical object exterior to a vehicle. The method further includes combining the first track segment and the second track segment to form a single track segment having a single trajectory in response to the first track segment and the second track segment being determined to be representative of the identical object. Systems and computer program products are also provided.

Abstract: Enclosed are embodiments for navigation with drivable area detection. In an embodiment, a method comprises: receiving a point cloud from a depth sensor, receiving image data from a camera; predicting at least one label indicating a drivable area by applying machine learning to the image data; labeling the point cloud using the at least one label; obtaining odometry information; generating a drivable area by registering the labeled point cloud and odometry information to a reference coordinate system; and controlling the vehicle to drive within the drivable area.

Abstract: Using a planning circuit of a vehicle, a map is accessed that includes information identifying at least one lane on which vehicles can travel. Using the planning circuit and from the map, a graph representing a driving environment of the vehicle is generated. The graph includes multiple trajectories. At least one trajectory includes a lane change. Each trajectory is a path for the vehicle to move from a first spatiotemporal location to a second spatiotemporal location. The trajectory includes at least one lane alone which the vehicle can move. Using the planning circuit, a trajectory of the multiple trajectories for the vehicle to travel is selected based on an initial vehicle trajectory of the vehicle. The selected trajectory includes a stem. The stem is a portion of the selected trajectory to which the vehicle is configured to adhere. Using the control circuit, the vehicle is moved along the selected trajectory.

Abstract: A scenario aware perception system (10) suitable for use on an automated vehicle includes a traffic-scenario detector (14), an object-detection device (24), and a controller (32). The traffic-scenario detector (14) is used to detect a present-scenario (16) experienced by a host-vehicle (12). The object-detection device (24) is used to detect an object (26) proximate to the host-vehicle (12). The controller (32) is in communication with the traffic-scenario detector (14) and the object-detection device (24). The controller (32) configured to determine a preferred-algorithm (36) used to identify the object (26). The preferred-algorithm (36) is determined based on the present-scenario (16).

Abstract: A system for an automated-taxi includes a display and a controller. The display is viewable by one or more of a plurality of clients of an automated-taxi. The controller-circuit is in communication with the display. The controller is configured to determine a seating-arrangement of the plurality of clients transported by the automated-taxi. The seating-arrangement is determined in accordance with destinations of the plurality of clients. The controller is also configured to operate the display to show the seating-arrangement to one or more of the plurality of clients. A method of operating an automated-taxi includes receiving destinations of a plurality of clients to transported by an automated-taxi; determining a seating-arrangement of the plurality of clients in accordance with the destinations; and operating a display viewable by one or more of a plurality of clients of an automated-taxi to show the seating-arrangement to one or more of the plurality of clients.

Abstract: Embodiments are disclosed for localization of vehicles using beacons. In an embodiment, a method comprises: determining, using at least one processor of a vehicle, that the vehicle has lost external signals (or is receiving degraded external signals) that are used for estimating a position of the vehicle; determining, using the at least one processor, a set of mobile beacons that are available to assist in estimating the position of the vehicle; receiving, using a communication device of the vehicle, broadcast signals from the set of mobile beacons, the broadcast signals including localization data for the set of mobile beacons; selecting, using the at least one processor, a subset of localization data from the set of mobile beacons for assisting in the position estimation of the vehicle; and estimating, using the at least one processor, the position of the vehicle using the subset of localization data.

Abstract: Techniques are provided for autonomous vehicle operation using linear temporal logic. The techniques include using one or more processors of a vehicle to store a linear temporal logic expression defining an operating constraint for operating the vehicle. The vehicle is located at a first spatiotemporal location. The one or more processors are used to receive a second spatiotemporal location for the vehicle. The one or more processors are used to identify a motion segment for operating the vehicle from the first spatiotemporal location to the second spatiotemporal location. The one or more processors are used to determine a value of the linear temporal logic expression based on the motion segment. The one or more processors are used to generate an operational metric for operating the vehicle in accordance with the motion segment based on the determined value of the linear temporal logic expression.

Abstract: A system includes one or more sensor systems, a controller-circuit, a first module, and a second module. The sensor systems are configured to determine position relationship data between a roadway and a host vehicle. The sensor system includes at least one of a computer-vision system, a radar system, and a LIDAR system. The controller-circuit is configured to receive and transform the position relationship data to effect steering control of the host vehicle. The first module is controlled by the controller-circuit to effect the steering control when the steering control transitions from a manual-mode to an automated mode. The second module is controlled by the controller-circuit to effect steering control of the host vehicle after control by the first module and upon meeting a prescribed condition.

Abstract: A system is described that can include a first database, a simulator, and a second database. The first database can store data indicating operation of at least one module within a computing device of an autonomous vehicle. The simulator can receive the stored data from the first database. The simulator can generate, based on the received data, a simulation of the operation of the at least one module. The simulator can identify at least one portion of the simulation that indicates a deviation between the collected data and the simulated operation of the autonomous vehicle. The simulator can analyze the at least one portion of the simulation to generate metrics for the at least one portion of the simulation. The metrics can be used to avoid another deviation between the collected data and the simulated operation of the autonomous vehicle. The second database can store the metrics.

Abstract: An ambient sound environment is captured by a microphone array of an autonomous vehicle traveling in the ambient sound environment. A perception module of the autonomous vehicle classifies sounds and localizes sound sources in the ambient sound environment. Classification is performed using spectrum analysis and/or machine learning. In an embodiment, sound sources within a field of view (FOV) of an image sensor of the autonomous vehicle are localized in a visual scene generated by the perception module. In an embodiment, one or more sound sources outside the FOV of the image sensors are localized in a static digital map. Localization is performed using parametric or non-parametric techniques and/or machine learning. The output of the perception module is input into a planning module of the autonomous vehicle to plan a route or trajectory for the autonomous vehicle in the ambient sound environment.

Abstract: Provided are methods for prediction error scenario mining for machine learning methods, which can include determining a prediction error indicative of a difference between a planned decision of an autonomous vehicle and an ideal decision of the autonomous vehicle. The prediction error is associated with an error-prone scenario for which a machine learning model of an autonomous vehicle is to make planned movements. The method includes searching a scenario database for the error-prone scenario based on the prediction error. The scenario database includes a plurality of datasets representative of data received from an autonomous vehicle sensor system in which the plurality of datasets is marked with at least one attribute of the set of attributes. The method further includes obtaining the error-prone scenario from the scenario database for inputting into the machine learning model for training the machine learning model. Systems and computer program products are also provided.

Abstract: A technique for tracking objects includes: determining a set of detected measurements based on a received return signal; determining a group that includes a set of group measurements and a set of group tracks; creating a merged factor, including a merged set of track state hypotheses associated with a merged set of existing tracks including a first set of existing tracks and a second set of existing tracks, by calculating the cross-product of a first set of previous track state hypotheses and a second set of previous track state hypotheses; determining a first new factor and a second new factor; calculating a first set of new track state hypotheses for the first new factor based on a first subset of the group measurements; and calculating a second set of new track state hypotheses for the second new factor based on a second subset of the group measurements.

Abstract: Provided are methods for vehicle scenario mining for machine learning methods, which can include determining a set of attributes associated with an untested scenario for which a machine learning model of an autonomous vehicle is to make planned movements. The method includes searching a scenario database for the untested scenario based on the set of attributes. The scenario database includes a plurality of datasets representative of data received from an autonomous vehicle sensor system in which the plurality of datasets is marked with at least one attribute of the set of attributes. The method further includes obtaining the untested scenario from the scenario database for inputting into the machine learning model for training the machine learning model. The machine learning model is configured to make the planned movements for the autonomous vehicle. Systems and computer program products are also provided.

Abstract: Techniques are provided for autonomous vehicle fleet management for reduced traffic congestion. A request is received for a vehicular ride. The request includes an initial spatiotemporal location and a destination spatiotemporal location. A processor is used to generate a representation of lane segments. Each lane segment is weighted in accordance with a number of other vehicles on the lane segment. A vehicle located within a threshold distance to the initial spatiotemporal location is identified such that the identified vehicle has at least one vacant seat. The processor is used to determine a route for operating the identified vehicle from the initial spatiotemporal location to the destination spatiotemporal location. The route includes one or more lane segments of the lane segments. An aggregate of weights of the one or more lane segments is below a threshold value. The received request and the determined route are transmitted to the identified vehicle.

Abstract: Among other things, we describe techniques for implementing a vehicle response to sensor failure. In general, one innovative aspect of the subject matter described in this specification can be embodied in methods that include receiving information from a plurality of sensors coupled to a vehicle, determining that a level of confidence of the received information from at least one sensor of a first subset of sensors of the plurality of sensors is less than a first threshold, comparing a number of sensors in the first subset of sensors to a second threshold, and adjusting the driving capability of the vehicle to rely on information received from a second subset of sensors of the plurality of sensors, wherein the second subset of sensors excludes the at least one sensor of the first subset of sensors.

Abstract: Techniques are provided for autonomous vehicle operation using linear temporal logic. The techniques include using one or more processors of a vehicle to store a linear temporal logic expression defining an operating constraint for operating the vehicle. The vehicle is located at a first spatiotemporal location. The one or more processors are used to receive a second spatiotemporal location for the vehicle. The one or more processors are used to identify a motion segment for operating the vehicle from the first spatiotemporal location to the second spatiotemporal location. The one or more processors are used to determine a value of the linear temporal logic expression based on the motion segment. The one or more processors are used to generate an operational metric for operating the vehicle in accordance with the motion segment based on the determined value of the linear temporal logic expression.

Abstract: A system includes a tracking system, a controller-circuit, and a device. The tracking system is configured to detect and track an object, and includes one or more of a computer vision system, a radar system, and a LIDAR system. The controller-circuit is disposed in a host vehicle, and is configured to receive detection signals from the tracking system, process the detection signals, determine, whether an object is detected based on the processed detecting signals, and in accordance with a determination that an object is detected, output command signals. The device is adapted to be mounted to the host vehicle, and is configured to receive the command signals and thereby provide a dynamic visual indication adapted to change in accordance with orientation changes between the host vehicle and the object. The dynamic visual indication is viewable from outside of the host vehicle.