Software application to request and control an autonomous vehicle service

A system, an apparatus or a process may be configured to implement an application that applies artificial intelligence and/or machine-learning techniques to predict an optimal course of action (or a subset of courses of action) for an autonomous vehicle system (e.g., one or more of a planner of an autonomous vehicle, a simulator, or a teleoperator) to undertake based on suboptimal autonomous vehicle performance and/or changes in detected sensor data (e.g., new buildings, landmarks, potholes, etc.). The application may determine a subset of trajectories based on a number of decisions and interactions when resolving an anomaly due to an event or condition. The application may use aggregated sensor data from multiple autonomous vehicles to assist in identifying events or conditions that might affect travel (e.g., using semantic scene classification). An optimal subset of trajectories may be formed based on recommendations responsive to semantic changes (e.g., road construction).

Machine-learning systems and techniques to optimize teleoperation and/or planner decisions

Method including receiving a teleoperation message via a communication link from an autonomous driverless vehicle, detecting data specifying an event associated with the autonomous vehicle like the degradation of a sensor, identifying one or more courses of action to perform responsive to the event, simulating the courses of action to calculate simulated values and generating visualization data to present information associated with the event to a display of a teleoperator computing device to allow selection of a desired course of action.

Teleoperation system and method for trajectory modification of autonomous vehicles

Methods and systems are provided for monitoring use of a vehicle having one or more autonomous (and/or semi-autonomous) operation features to determine and respond to incidents, such as collisions, thefts, or breakdowns. According to certain aspects, operating data from sensors within or near the vehicle may be used to determine when an incident has occurred and determine an appropriate response. The responses may include contacting a third party to provide assistance, such as local emergency services. In some embodiments, occurrence of the incident may be verified by automated communication with the vehicle operator.

Accident response using autonomous vehicle monitoring

Aspects of the disclosure relate to detecting and responding to objects in a vehicle's environment. For example, an object may be identified in a vehicle's environment, the object having a heading and location. A set of possible actions for the object may be generated using map information describing the vehicle's environment and the heading and location of the object. A set of possible future trajectories of the object may be generated based on the set of possible actions. A likelihood value of each trajectory of the set of possible future trajectories may be determined based on contextual information including a status of the detected object. A final future trajectory is determined based on the determined likelihood value for each trajectory of the set of possible future trajectories. The vehicle is then maneuvered in order to avoid the final future trajectory and the object.

Predicting trajectories of objects based on contextual information

A presence of precipitation is determined. At least one attribute of the precipitation is identified. At least one autonomous control action for a vehicle is determined based at least in part on the precipitation.

Autonomous vehicle precipitation detection

Control system having computer, processor, and memory, determines presence of precipitation / rain (e.g. using RADAR / LIDAR / sonar / camera), identifies an attribute of said precipitation (e.g. precipitation type / rate of rainfall / amount of rain), and determines an autonomous control action in dependence on the detection of rain (e.g. establishing vehicle speed / stopping distance / braking distance / rate of acceleration). Data may be collected by on-board sensors, or via remote server. A coefficient of friction may be calculated based on the detected precipitation attribute. Autonomous control action may also depend on type of roadway being traversed, or route topography. Claims are also included for a computer readable medium and a method of use.

Control of an autonomous vehicle

Es wird eine Anwesenheit von Niederschlag bestimmt. Es wird mindestens ein Attribut des Niederschlags identifiziert. Mindestens teilweise auf der Basis des Niederschlags wird mindestens eine autonome Steueraktion fur ein Fahrzeug bestimmt.

Niederschlagsdetektion in autonomen Fahrzeugen

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Autonomous vehicle precipitation detection

Control of an autonomous vehicle

Niederschlagsdetektion in autonomen Fahrzeugen