A fleet of delivery robots, each including: a chassis; a storage compartment within which items are stored for transportation; a set of wheels coupled to the chassis; at least one sensor; a processor electronically coupled to the control system and the at least one sensor; and a tangible, non-transitory, machine readable medium storing instructions that when executed by the processor effectuates operations including: capturing, with the at least one sensor, data of an environment and data indicative of movement of the respective delivery robot; generating or updating, with the processor, a first map of the environment based on at least a portion of the captured data; inferring, with the processor, a current location of the respective delivery robot; and actuating, with the processor, the respective delivery robot to execute a delivery task including transportation of at least one item from a first location to a second location.

A hardware processor of a mobile object executes the program stored in a storage device to acquire information indicating a road situation in a traveling direction of a mobile object; to recognize whether the mobile object is moving on a roadway or a predetermined region different from the roadway; to recognize presence of a contact portion between the sidewalk and the predetermined region in the traveling direction of the mobile object; to control the speed of the mobile object at least partially, and limit a speed at which the mobile object is moving on the roadway to a first speed and limit a speed at which the mobile object is moving on a sidewalk to a second speed slower than the first speed; and to bring a speed of the mobile object closer to the second speed when the mobile object is moving on the roadway, the contact portion is recognized within a predetermined range from the mobile object, and the road situation is in a predetermined state.

An information processing apparatus that estimates an instruction position for a moving body used by a user acquires utterance information regarding the instruction position including a visual mark from a communication device used by the user. The information processing apparatus acquires a captured image captured by the moving body and determines an object region in the captured image corresponding to the visual mark included in the utterance information. The information apparatus estimates the instruction position based on the object region.

Included is a method for collaboration between a first robotic chassis and a second robotic chassis, including: executing, with the processor of the first robotic chassis, a first part of a task; transmitting, with the processor of the first robotic chassis, a signal to a processor of the second robotic chassis upon completion of the first part of the task; and executing, with the processor of the second robotic chassis, a second part of the task upon receiving the signal transmitted from the processor of the first robotic chassis; wherein the first robotic chassis and the second robotic chassis provide differing services.

Embodiments herein can determine an optimal route for an autonomous electric vehicle. The system may score viable routes between the start and end locations of a trip using a numeric or other scale that denotes how viable the route is for autonomy. The score is adjusted using a variety of factors where a learning process leverages both offline and online data. The scored routes are not based simply on the shortest distance between the start and end points but determine the best route based on the driving context for the vehicle and the user.

Embodiments herein relate to an autonomous vehicle or self-driving vehicle. The system can determine a collision avoidance path by: 1) predicting the behavior/trajectory of other moving objects (and identifying stationary objects); 2) given the driving trajectory (issued by autonomous driving system) or predicted driving trajectory (human), establishing the probability for a collision that can be calculated between the vehicle and one or more objects; and 3) finding a path to minimize the collision probability.

A vehicle is provided that determines a first triggering event in predetermined triggering event information has occurred, in response to a first event, automatically forwards a first communication to a selected third party vendor to preorder a product or service of the selected third party vendor in connection with a transaction with the user, determines a second triggering event in the triggering event information has occurred, and in response to the later second event, automatically sends an authorization to complete the transaction by providing financial information to the selected third party vendor.

Embodiments herein can determine an optimal route for an autonomous electric vehicle. The system may score viable routes between the start and end locations of a trip using a numeric or other scale that denotes how viable the route is for autonomy. The score is adjusted using a variety of factors where a learning process leverages both offline and online data. The scored routes are not based simply on the shortest distance between the start and end points but determine the best route based on the driving context for the vehicle and the user.

Systems of an electrical vehicle and the operations thereof are provided that use identifiers and sensed power source parameters to determine whether a rule, such as a warranty or licensing requirement, has been violated.

An autonomous driving method includes: determining a risk of a target vehicle based on either one or both of a driving characteristic and an appearance characteristic of the target vehicle; and autonomously controlling a host vehicle based on the risk.