Abstract: An autonomous vehicle having sensors advantageously varied in capabilities, advantageously positioned, and advantageously impervious to environmental conditions. A system executing on the autonomous vehicle that can receive a map including, for example, substantially discontinuous surface features along with data from the sensors, create an occupancy grid based upon the map and the data, and change the configuration of the autonomous vehicle based upon the type of surface on which the autonomous vehicle navigates. The device can safely navigate surfaces and surface features, including traversing discontinuous surfaces and other obstacles.

Abstract: System and method for determining, in real time, a location and probability of the state of a traffic light in order to navigate safely through an intersection. The system can receive both image data in real-time and previously-formulated map data. The map data can be used to determine the historical coordinates of a traffic light, and those coordinates can be transformed to the camera image frame of reference. The image at the transformed historical coordinates can be fed to a first machine learning detection model that can provide bounding boxes where traffic lights could be. The traffic light image data can be fed to a second machine learning model that can infer the probability of traffic light states.

Abstract: An autonomous vehicle having sensors advantageously varied in capabilities, advantageously positioned, and advantageously impervious to environmental conditions. A system executing on the autonomous vehicle that can receive a map including, for example, substantially discontinuous surface features along with data from the sensors, create an occupancy grid based upon the map and the data, and change the configuration of the autonomous vehicle based upon the type of surface on which the autonomous vehicle navigates. The device can safely navigate surfaces and surface features, including traversing discontinuous surfaces and other obstacles.

Abstract: Substantially discontinuous surface feature traversal feature of the present teachings can leverage a transport device (TD), for example, but not limited to, an autonomous device or a semi-autonomous device, to navigate in environments that can include features such as substantially discontinuous surface features. The substantially discontinuous surface feature traversal feature can enable the TD to travel on an expanded variety of surfaces. In particular, substantially discontinuous surface features can be accurately identified and labeled so that the TD can automatically maintain the performance of the TD during ingress and egress of the substantially discontinuous surface feature.