Newness and distinctiveness is claimed in the features of ornamentation as shown inside the broken line circle in the accompanying representation.

Display screen or portion thereof with graphical user interface

Object detection, as of one the most fundamental and challenging problems in computer vision, has received great attention in recent years. Its development in the past two decades can be regarded as an epitome of computer vision history. If we think of today's object detection as a technical aesthetics under the power of deep learning, then turning back the clock 20 years we would witness the wisdom of cold weapon era. This paper extensively reviews 400+ papers of object detection in the light of its technical evolution, spanning over a quarter-century's time (from the 1990s to 2019). A number of topics have been covered in this paper, including the milestone detectors in history, detection datasets, metrics, fundamental building blocks of the detection system, speed up techniques, and the recent state of the art detection methods. This paper also reviews some important detection applications, such as pedestrian detection, face detection, text detection, etc, and makes an in-deep analysis of their challenges as well as technical improvements in recent years.

Object Detection in 20 Years: A Survey

According to an aspect of the disclosure, a home energy management system and method includes a network device disposed at a residence and a wireless home energy network capable of establishing communication with the network device. A server is disposed remotely from the residence and capable of generating a control action report to control the network devices. A controller is located at the residence and in communication with the server and configured to establish the wireless home energy network; initiate a plurality of operating status requests of the network device; receive device data in response to at least one of the operating status requests; generate a site report including the device data; initiate a communication of the site report to the remote server; and detect an availability of the control action report at the remote server in conjunction with the communication of the site report.

Energy management system and method

Aspects of the invention relate generally to autonomous vehicles. Specifically, the features described may be used alone or in combination in order to improve the safety, use, driver experience, and performance of these vehicles.

System and method for predicting behaviors of detected objects

A method for controlling a vehicle which includes obtaining, via at least one detecting device, behavior information of a driver in the vehicle, and transitioning control statuses of the vehicle according to the driver behavior information.

Method and system for controlling a vehicle

A passenger in an automated vehicle may relinquish control of the vehicle to a control computer when the control computer has determined that it may maneuver the vehicle safely to a destination. The passenger may relinquish or regain control of the vehicle by applying different degrees of pressure, for example, on a steering wheel of the vehicle. The control computer may convey status information to a passenger in a variety of ways including by illuminating elements of the vehicle. The color and location of the illumination may indicate the status of the control computer, for example, whether the control computer has been armed, is ready to take control of the vehicle, or is currently controlling the vehicle.

User interface for displaying internal state of autonomous driving system

Methods and systems for adaptive methods for transitioning control to the driver are described. A computing device controlling a vehicle autonomously may be configured to receive a request for a transition of the vehicle from autonomous mode to manual mode through an indication by the driver. The computing device may determine the state of the vehicle based on parameters related to the autonomous operation of the vehicle. Based on the state of the vehicle and the indication, the computing device may determine instructions corresponding to the transition of control, which may include a strategy for the transition and duration of time corresponding to the transition of control. The computing device may provide the instructions to perform the transition of control of the vehicle from autonomous mode to manual mode.

Systems and methods for transitioning control of an autonomous vehicle to a driver

The outdoor perception problem is a major challenge for driver-assistance and autonomous vehicle systems. While these systems can often employ active sensors such as sonar, radar, and lidar to perceive their surroundings, the state of standard traffic lights can only be perceived visually. By using a prior map, a perception system can anticipate and predict the locations of traffic lights and improve detection of the light state. The prior map also encodes the control semantics of the individual lights. This paper presents methods for automatically mapping the three dimensional positions of traffic lights and robustly detecting traffic light state onboard cars with cameras. We have used these methods to map more than four thousand traffic lights, and to perform onboard traffic light detection for thousands of drives through intersections.

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Traffic light mapping and detection

Systems and methods are provided for controlling a vehicle A safe envelope driving pattern is determined to control the vehicle in an autonomous mode User identification data and sensor data are received from one or more sensors associated with the vehicle A driver-specific driving pattern is determined based on the received sensor data and the user identification data Operation of the vehicle is controlled in the autonomous mode based on the identification of the user in the driver's seat, the safe envelope driving pattern, and the user-specific driving pattern

Driving pattern recognition and safety control

Aspects of the disclosure relate generally to determining whether an autonomous vehicle should be driven in an autonomous or semiautonomous mode (where steering, acceleration, and braking are controlled by the vehicle's computer). For example, a computer may maneuver a vehicle in an autonomous or a semiautonomous mode. The computer may continuously receive data from one or more sensors. This data may be processed to identify objects and the characteristics of the objects. The detected objects and their respective characteristics may be compared to a traffic pattern model and detailed map information. If the characteristics of the objects deviate from the traffic pattern model or detailed map information by more than some acceptable deviation threshold value, the computer may generate an alert to inform the driver of the need to take control of the vehicle or the computer may maneuver the vehicle in order to avoid any problems.

Christopher Paul Urmson

Papers

User interface for displaying internal state of autonomous driving system

Systems and methods for transitioning control of an autonomous vehicle to a driver

Traffic light mapping and detection

Driving pattern recognition and safety control

Determining when to drive autonomously