Provided herein is a system and method implemented on a vehicle. The system comprises one or more sensors, one or more processors, and a memory storing instructions that, when executed by the one or more processors, causes the system to perform: obtaining data from the one or more sensors; comparing the obtained data from the one or more sensors with reference data; determining whether one or more characteristics of the obtained data deviate from corresponding characteristics of the reference data by more than a respective threshold; in response to determining that one or more characteristics of the data obtained deviate from corresponding characteristics of the reference data by more than a respective threshold, determining an action of the vehicle based on amounts of the one or more deviations; and performing the determined action.

Methods, systems, and apparatus, including computer programs encoded on a computer storage medium that generates lane path descriptors for use by autonomous vehicles. One of the methods includes receiving data that defines valid lane paths in a scene in an environment. Each valid lane path represents a path through the scene that can be traversed by a vehicle. User interface presentation data can be provided to a user device. The user interface can contain: (i) a first display area that displays a first visual representation of the sensor measurement; and (ii) a second display area that displays a second visual representation of the set of valid lane paths. User input modifying the second visual representation of the set of valid lane paths can be received; and in response to receiving the user input, the set of valid lane paths of the scene in the environment can be modified.

According to one aspect, methods to validate an autonomy system in conjunction with a teleoperations system are provided. A computing device obtains simulation data of an autonomous driving system of a vehicle. The simulation data includes first environment data representing an environment in which the vehicle is driven using the autonomous driving system. The computing device obtains teleoperations driving data of a teleoperations driving system by which the vehicle is remotely controlled with an aid of a human. The teleoperations driving data includes second environment data representing the environment in which the vehicle is driven using the teleoperations driving system. The computing device determines whether the autonomous driving system in conjunction with the teleoperations driving system meets a minimum deployment standard based on the simulation data and the teleoperations driving data.

According to one aspect, methods to validate an autonomy system in conjunction with a teleoperations system are provided. A computing device obtains simulation data of an autonomous driving system of a vehicle. The simulation data includes first environment data representing an environment in which the vehicle is driven using the autonomous driving system. The computing device obtains teleoperations driving data of a teleoperations driving system by which the vehicle is remotely controlled with an aid of a human. The teleoperations driving data includes second environment data representing the environment in which the vehicle is driven using the teleoperations driving system. The computing device determines whether the autonomous driving system in conjunction with the teleoperations driving system meets a minimum deployment standard based on the simulation data and the teleoperations driving data.

Methods and systems autonomously parking and retrieving vehicles are disclosed. Available parking spaces or parking facilities may be identified, and the vehicle may be navigated to an available space from a drop-off location without passengers. Special-purpose sensors, GPS data, or wireless signal triangulation may be used to identify vehicles and available parking spots. Upon a user request or a prediction of upcoming user demand, the vehicle may be retrieved autonomously from a parking space. Other vehicles may be autonomously moved to facilitate parking or retrieval.

Methods and systems for assessing, detecting, and responding to malfunctions involving components of autonomous vehicles and/or smart homes are described herein. Autonomous operation features and related components can be assessed using direct or indirect data regarding operation. Vehicle collision and/or smart home incident monitoring, damage detection, and responses are also described, with particular focus on the particular challenges associated with incident response for unoccupied vehicles and/or smart homes. Operating data associated with the autonomous vehicle and/or smart home may be received. Within the operating, an unusual condition indicative of a likelihood of incident may be detected. Based on the unusual condition, it may be determined that the incident occurred. Accordingly, a response to the incident may be determined. The response may be implemented by the autonomous vehicle and/or smart home.

A travel assistance system for an agricultural machine includes a traveling device included in an agricultural machine, an acquirer to acquire a travel route to be traveled by the agricultural machine and created on a field representing an agricultural field, a corrector to correct the travel route acquired by the acquirer, and a controller configured or programmed to control the traveling device based on the travel route corrected by the corrector. The travel route includes lines connected to each other and the corrector includes a creator to create an auxiliary line extending from an ending portion of a first line of the lines to a starting portion of a second line of the lines connected to the ending portion of the first line, the auxiliary line being inclined relative to the second one of the lines.

A travel assistance system for an agricultural machine includes a traveling device included in an agricultural machine, an acquirer to acquire a travel route to be traveled by the agricultural machine and created on a field representing an agricultural field, a corrector to correct the travel route acquired by the acquirer, and a controller configured or programmed to control the traveling device based on the travel route corrected by the corrector. The travel route includes lines connected to each other and the corrector includes a creator to create an auxiliary line extending from an ending portion of a first line of the lines to a starting portion of a second line of the lines connected to the ending portion of the first line, the auxiliary line being inclined relative to the second one of the lines.

Methods and systems for identifying autonomous vehicle users are described herein. An autonomous vehicle may receive a request to pick up a user at a starting location and transport the user to a destination location. Accordingly, the autonomous vehicle may travel to the starting location. Upon arriving at the starting location, the autonomous vehicle may detect whether a person approaching the vehicle is the user by detecting a biometric identifier for the person. The biometric identifier may then be compared to a biometric fingerprint for the user, and if there is a match, the autonomous vehicle may determine that the person is the user. As a result, the user may be allowed to enter the autonomous vehicle and/or the autonomous vehicle may begin travelling to the destination location. Otherwise, the person may be denied entry to the autonomous vehicle.

A motion planner of an autonomous vehicle's computer system uses reconfigurable collision detection architecture hardware to perform a collision assessment on a planning graph for the vehicle prior to execution of a motion plan. For edges on the planning graph, which represent transitions in states of the vehicle, the system sets a probability of collision with a dynamic object in the environment based at least in part on the collision assessment. Depending on whether the goal of the vehicle is to avoid or collide with a particular dynamic object in the environment, the system then performs an optimization to identify a path in the resulting planning graph with either a relatively high or relatively low potential of a collision with the particular dynamic object. The system then causes the actuator system of the vehicle to implement a motion plan with the applicable identified path based at least in part on the optimization.