According to various embodiments, systems, methods, and media for evaluating an open space planner in an autonomous vehicle are disclosed. In one embodiment, an exemplary method includes receiving, at a profiling application, a record file recorded by the ADV while driving in an open space using the open space planner, and a configuration file specifying parameters of the ADV; extracting planning messages and prediction messages from the record file, each extracted message being associated with the open space planner. The method further includes generating features from the planning message and the prediction messages in view of the specified parameters of the ADV; and calculating statistical metrics from the features. The statistical metrics are then provided to an automatic tuning framework for tuning the open space planner.

An autonomous robotic vehicle is capable of detecting, identifying, and locating the source of gas leaks such as methane. Because of the number of operating components within the vehicle, it may also be considered a robotic system. The robotic vehicle can be remotely operated or can move autonomously within a jobsite. The vehicle selectively deploys a source detection device that precisely locates the source of a leak. The vehicle relays data to stakeholders and remains powered that enables operation of the vehicle over an extended period. Monitoring and control of the vehicle is enabled through a software interface viewable to a user on a mobile communications device or personal computer.

Systems, methods, and computer program products to enhance the situational competency and/or the safe operation of a vehicle, when operating at least partially in an autonomous mode, as a support vehicle for one or more on-road persons engaged in a training or competitive cycling, running, and/or walking activity on a predetermined travel route at a predetermined pace.

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.

Methods and systems for controlling an autonomous vehicle. The method includes receiving sensor data from a plurality of sensors, determining, a plurality of probability hypotheses based upon the sensor data, and receiving metadata from at least one sensor of the plurality of sensors. An integrity level of at least one of the plurality of probability hypotheses is determined based upon the received metadata and at least one action is determined based upon the determined integrity level and at least one probability hypothesis of the plurality of probability hypotheses. The at least one action is then initiated by an electronic controller for the vehicle.

A device may receive emergency data, traffic data, network performance data, crime data, and gunshot data associated with a geographical area and may identify a location within the geographical area based on the emergency data, the traffic data, the network performance data, the crime data, and the gunshot data. The device may determine, based on the emergency data, the traffic data, the network performance data, the crime data, and the gunshot data for the location, a risk level for the location and may identify an autonomous vehicle based on the risk level, the traffic data, and the network performance data for the location. The device may determine a route for the autonomous vehicle to the location based on the traffic data and the network performance data for the location, and may perform actions based on the autonomous vehicle and the route.

A test system includes a master electronic control module (ECM) configured to receive user input for performing a test action. The master ECM determines one or more subsystem ECMs associated with the requested test action and a sequence of operations to be controlled by the subsystem ECMs to perform the requested test action. The master ECM provides instructions to the subsystem ECMs to perform the operations, along with parameters for those operations. The master ECM may determine whether a test action is appropriate to perform, based on sensor data, before instructing subsystem ECMs to perform the operations of the test action.

A method for performing automatic valet parking, which includes selecting a road scenario applicable to a roadway; notifying a driver to release manual control elements of a motor vehicle and to leave the motor vehicle; checking whether the control elements have been released and the driver has left the motor vehicle and, in this case, entering an EXPLORE mode in which the motor vehicle is slowly driven autonomously and searches for a free car space or a parking space using the vehicle's own environmental sensors, before the motor vehicle is placed in a parking position; and then to change from the EXPLORE mode to a PARKING mode in which the motor vehicle is parked in the car space or in the parking space from the parking position by means of the longitudinal and lateral controllers and using the environmental data previously obtained from the environmental sensors in the EXPLORE mode.

According to an aspect of an embodiment, operations may comprise receiving a plurality of frame sets generated while navigating a local environment, receiving an occupancy map (OMap) representation of the local environment, for each of the plurality of frame sets, generating, using the OMap representation, one or more instances each comprising a spatial cluster of neighborhood 3D points generated from a 3D sensor scan of the local environment, and classifying each of the instances as dynamic or static, tracking instances classified as dynamic across the plurality of frame sets using a tracking algorithm, assigning a single instance ID to tracked instances classified as dynamic across the plurality of frame sets, estimating a bounding box for each of the instances in each of the plurality of frame sets, and employing the instances as ground truth data in a training of one or more deep learning classifiers.

An operation device for an autonomous vehicle includes a touch panel configured to display at least one of a start button and a deceleration button, a notification button, and a tab switch on the same screen, the autonomous vehicle being autonomously drivable, the start button being a button for starting driving of the autonomous vehicle in an autonomous drive mode, the deceleration button being a button for decelerating the autonomous vehicle during the autonomous drive mode, the notification button being a button for performing notification to an outside of the autonomous vehicle, and the tab switch being a switch for displaying or enlarging an equipment control button group for controlling equipment mounted on the autonomous vehicle.