Improved mobile robots and systems and methods thereof, described herein, can enhance security and monitoring services of grounds and property. And, such mobile robots and systems and methods thereof can enhance policing as well as customer service and help desk functionality. In some embodiments, the mobile robots and systems and methods thereof can enhance exploration, such as space exploration.

Systems and techniques are described for implementing autonomous control of powered earth-moving vehicles, including to automatically determine and control movement around a site having potential obstacles. For example, the systems/techniques may determine and implement autonomous operations of powered earth-moving vehicle(s) (e.g., obtain/integrate data from sensors of multiple types on a powered earth-moving vehicle, and use it to determine and control movement of the powered earth-moving vehicle around a site), including in some situations to implement coordinated actions of multiple powered earth-moving vehicles and/or of a powered earth-moving vehicle with one or more other types of construction vehicles. The described techniques may further include determining current location and positioning of the powered earth-moving vehicle on the site, determining a target destination location and/or path of the powered earth-moving vehicle, identifying and classifying obstacles (if any) along a desired path or otherwise between current and destination locations, and implementing actions to address any such obstacles.

A vehicle has a plurality of control apparatuses, a user input, a geographic position component, an object detection apparatus, memory, and a display. A processor is also included and is programmed to receive the destination information, identify a route, and determine the current geographic location of the vehicle. The processor is also programmed to identify an object and object type based on object information received from the object detection apparatus and to determine at least one warning characteristic of the identified object based on at least one of: the object type, a detected proximity of the detected object to the vehicle, the location of the detected object relative to predetermined peripheral areas of the vehicle, the current geographic location of the vehicle, and the route. The processor is also configured to select and display on the display an object warning image based on the at least one warning characteristic.

A vehicle has a plurality of control apparatuses, a user input, a geographic position component, an object detection apparatus, memory, and a display. A processor is also included and is programmed to receive the destination information, identify a route, and determine the current geographic location of the vehicle. The processor is also programmed to identify an object and object type based on object information received from the object detection apparatus and to determine at least one warning characteristic of the identified object based on at least one of: the object type, a detected proximity of the detected object to the vehicle, the location of the detected object relative to predetermined peripheral areas of the vehicle, the current geographic location of the vehicle, and the route. The processor is also configured to select and display on the display an object warning image based on the at least one warning characteristic.

Disclosed are systems and methods for autonomous vehicles and vehicles with automatic driver-assistance systems (ADAS) to automatically detect an imminent collision, determine whether the collision is avoidable or unavoidable, and plot a course minimizing the hazard using an artificial intelligence (AI) model. For example, a collision is avoidable if the vehicle can avoid it by steering, braking, and/or accelerating in a particular sequence. The AI model finds the best sequence for collision avoidance, and if that is not possible, it finds the best sequence for minimizing the harm. The harm is based on an estimated number of fatalities, injuries, and property damage predicted to be caused in the collision. The AI-based situation analysis and sequence selection are directly applicable to human-driven vehicles with an emergency-intervention ADAS system, as well as fully autonomous vehicles. With fast electronic reflexes and multi-sensor situation awareness, the AI model can save lives on the highway.

The invention provides an autonomous surface treatment vehicle, e.g. a floor cleaner, with an autonomy system navigating according to a map, a scanning sensor to detect a position of an obstacle within a scanning zone and generate a detection signal. A safety system is arranged to generate a safety stop in case the detection signal indicates an obstacle within a safety zone. The safety system can enter a special mode of operation, e.g. upon request from the autonomy system, where a special safety zone selection algorithm selects the safety zone e.g. from a special set of pre-determined safety zones. Especially, such special mode can provide safety zones with a relaxed speed limit in combination with a restricted direction limit, so as to provide a faster driving near a wall or similar known obstacle.

The invention provides an autonomous surface treatment vehicle, e.g. a floor cleaner, with an autonomy system navigating according to a map, a scanning sensor to detect a position of an obstacle within a scanning zone and generate a detection signal. A safety system is arranged to generate a safety stop in case the detection signal indicates an obstacle within a safety zone. The safety system can enter a special mode of operation, e.g. upon request from the autonomy system, where a special safety zone selection algorithm selects the safety zone e.g. from a special set of pre-determined safety zones. Especially, such special mode can provide safety zones with a relaxed speed limit in combination with a restricted direction limit, so as to provide a faster driving near a wall or similar known obstacle.

According to certain aspects, a computer-implemented method for operating an autonomous or semi-autonomous vehicle may be provided. With the customer's permission, an identity of a vehicle operator may be identified and a vehicle operator profile may be retrieved. Operating data regarding autonomous operation features operating the vehicle may be received from vehicle-mounted sensors. When a request to disable an autonomous feature is received, a risk level for the autonomous feature is determined and compared with a driver behavior setting for the autonomous feature stored in the vehicle operator profile. Based upon the risk level comparison, the autonomous vehicle retains control of vehicle or the autonomous feature is disengaged depending upon which is the safer driverthe autonomous vehicle or the vehicle human occupant. As a result, unsafe disengagement of self-driving functionality for autonomous vehicles may be alleviated. Insurance discounts may be provided for autonomous vehicles having this safety functionality.

The technology relates to controlling a vehicle in an autonomous driving mode. For example, sensor data identifying a plurality of objects may be received. Pairs of objects of the plurality of objects may be identified. For each identified pair of objects of the plurality of objects, a similarity value which indicates whether the objects of that identified pair of objects can be responded to by the vehicle as a group may be determined. The objects of one of the identified pairs of objects may be clustered together based on the similarity score. The vehicle may be controlled in the autonomous mode by responding to each object in the cluster in a same way.

The technology relates to controlling a vehicle in an autonomous driving mode. For example, sensor data identifying a plurality of objects may be received. Pairs of objects of the plurality of objects may be identified. For each identified pair of objects of the plurality of objects, a similarity value which indicates whether the objects of that identified pair of objects can be responded to by the vehicle as a group may be determined. The objects of one of the identified pairs of objects may be clustered together based on the similarity score. The vehicle may be controlled in the autonomous mode by responding to each object in the cluster in a same way.