A method and system is provided for a semi-autonomous vehicle to have optional routes that vary in an amount of autonomous driving offered. The system performs processes including aggregating data from various sources to thereby position and orient the vehicle, assigning a autonomous driving value to different route segments, determining an optimal route by minimizing a distance between a start position and a destination position and maximizing the percent autonomous value assigned, and displaying the optimal route.

Automatic driving route planning application

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

Methods and systems for determining fault for an accident involving a vehicle having one or more autonomous and/or semi-autonomous operation features are provided. According to certain aspects, performance data indicative of the performance of the features may be used to determine fault for a vehicle accident, such as a collision, by allocating fault for the accident between a vehicle operator, the autonomous operation features, or a third party. The allocation of fault may be used to determine an adjustment to an insurance policy and/or adjust coverage levels for an insurance policy. The allocation of fault may further be used to adjust risk levels or profiles associated with the autonomous or semi-autonomous operation features, which may be applied to other vehicles having the same or similar features.

Gary Linscott

Papers

Software application to request and control an autonomous vehicle service