This disclosure presents an assisted driving vehicle system, including autonomous, semi-autonomous, and technology assisted vehicles, that can share sensor data among two or more controllers. A sensor can have one communication channel to a controller, thereby saving cabling and circuitry costs. The data from the sensor can be sent from one controller to another controller to enable redundancy and backup in case of a system failure. Sensor data from more than one sensor can be aggregated at one controller prior to the aggregated sensor data being communicated to another controller thereby saving bandwidth and reducing transmission times. The sharing of sensor data can be enabled through the use of a sensor data distributor, such as a converter, repeater, or a serializer/deserializer set located as part of the controller and communicatively coupled to another such device in another controller using a data interface communication channel.

An autonomous vehicle uses a secondary vehicle control system to supplement a primary vehicle control system to perform a controlled stop if an adverse event is detected in the primary vehicle control system. The secondary vehicle control system may use a redundant lateral velocity determined by a different sensor from that used by the primary vehicle control system to determine lateral velocity for use in controlling the autonomous vehicle to perform the controlled stop.

The present disclosure relates generally to autonomous machines (AMs) and more particularly to techniques for intelligently planning, managing and performing various tasks using AMs. A control system (referred to as a fleet management system or FMS) is disclosed for managing a set of resources at a site, which may include AMs. The FMS is configured to control and manage the AMs at the site such that tasks are performed autonomously by the AMs. An AM may directly communicate with another AM located on the site to complete a task without requiring to be in constant communication with the FMS during the performance of the task. The FMS is configured to use various optimization techniques to allocate resources (e.g., AMs) for performing tasks at the site. The resource allocation is performed so as to maximize the use of available AMs while ensuring that the tasks get performed in a timely manner.

A system and method for an on-demand shuttle, bus, or taxi service able to operate on private and public roads provides situational awareness and confidence displays. The shuttle may include ISO 26262 Level 4 or Level 5 functionality and can vary the route dynamically on-demand, and/or follow a predefined route or virtual rail. The shuttle is able to stop at any predetermined station along the route. The system allows passengers to request rides and interact with the system via a variety of interfaces, including without limitation a mobile device, desktop computer, or kiosks. Each shuttle preferably includes an in-vehicle controller, which preferably is an AI Supercomputer designed and optimized for autonomous vehicle functionality, with computer vision, deep learning, and real time ray tracing accelerators. An AI Dispatcher performs AI simulations to optimize system performance according to operator-specified system parameters.

An Urban Intermodal Freight System is capable of transporting large volumes and tonnage of freight by containerized or other means on a mass transit rail system. It captures excess capacity in the existing mass transit rail infrastructure to move packages, parcels, and freight by using miniaturized intermodal cargo containers that are designed to integrate seamlessly with the existing transit infrastructure, while displacing delivery trucks from increasingly crowded city streets. By enabling inbound trucks to transfer their cargo to the Urban Intermodal Freight System at a city's outskirts, freight is delivered without trucks entering congested downtown areas, greatly alleviating traffic congestion, delays, greenhouse gas emissions and other negative environmental impacts. The Linear Loading Dock and Conveyor System may have other useful applications, for example to access a facility, building or vehicle, or in other circumstances where off street truck parking or loading docks are not available.

A system and a method are disclosed for a vehicle control and interface system configured to facilitate control of different vehicles through universal mechanisms. The vehicle control and interface system can be integrated with different types of vehicles (e.g., rotorcraft, fixed-wing aircraft, motor vehicles, watercraft, etc.) in order to facilitate operation of the different vehicles using universal vehicle control inputs. In particular, the vehicle control and interface system converts universal vehicle control inputs describing a requested trajectory of a vehicle received from one or more universal vehicle control interfaces into commands for specific actuators of the vehicle configured to adjust a current trajectory of the vehicle to the requested trajectory. In order to convert the universal vehicle control inputs to actuator commands the vehicle control and interface system processes the universal vehicle control inputs using a universal vehicle control router.

Disclosed are devices, systems and methods for capturing an image. In one aspect an electronic camera apparatus includes an image sensor with a plurality of pixel regions. The apparatus further includes an exposure controller. The exposure controller determines, for each of the plurality of pixel regions, a corresponding exposure duration and a corresponding exposure start time. Each pixel region begins to integrate incident light starting at the corresponding exposure start time and continues to integrate light for the corresponding exposure duration. In some example embodiments, at least two of the corresponding exposure durations or at least two of the corresponding exposure start times are different in the image.

An autonomous vehicle incorporating a multimodal multi-technique signal fusion system is described herein. The signal fusion system is configured to receive at least one sensor signal that is output by at least one sensor system (multimodal), such as at least one image sensor signal from at least one camera. The at least one sensor signal is provided to a plurality of object detector modules of different types (multi-technique), such as an absolute detector module and a relative activation detector module, that generate independent directives based on the at least one sensor signal. The independent directives are fused by a signal fusion module to output a fused directive for controlling the autonomous vehicle.

A path providing device configured to provide a path information to a vehicle includes a communication unit configured to receive, from a server, map information including a plurality of layers of data, an interface unit configured to receive sensing information from one or more sensors disposed at the vehicle, and a processor. The processor is configured to determine an optimal path for guiding the vehicle from an identified lane, generate autonomous driving visibility information based on the sensing information and the determined optimal path, update the optimal path based on dynamic information related to a movable object located on the optimal path and the autonomous driving visibility information, receive different types of sensor data from a plurality of sensors, and update at least one of the autonomous driving visibility information or the optimal path based on information generated by combining at least two types of sensor data.

The present teaching relates to method, system, medium, and implementations for sensor calibration. A request is received from an ego vehicle in motion for assistance in collaborative calibration, when the ego vehicle determines that a sensor deployed thereon needs to be calibrated. The request includes at least one of a first position of the ego vehicle and a first configuration of the sensor with respect to the ego vehicle. An assisting vehicle is identified based on the first position, the first configuration, and a second position associated with the assisting vehicle and an assistance plan is generated in response to the request indicative of the assisting vehicle to travel to the ego vehicle to facilitate the calibration of the sensor by collaborating with the moving ego vehicle. Such generated assistance plan is then sent out to initiate the collaborative calibration.