Abstract: There is provided a driver-support system for use with a human-operated material-transport vehicle, and methods for using the same. The system has at least one sensor, a human-vehicle interface, and a transceiver for communicating with a fleet-management system. The system also has a processor that is configured to provide a mapping application and a localization application based on information received from the sensor. The mapping application and localization application may be provided in a single localization-and-mapping (“SLAM”) application, which may obtain input from the sensor, for example, when the sensor is an optical sensor such as a LiDAR or video camera.

Abstract: Methods and systems are provided for traction detection and control of a self-driving vehicle. The self-driving vehicle has drive motors that drive drive-wheels according to a drive-motor speed. Traction detection and control can be obtained by measuring the vehicle speed with a sensor such as a LiDAR or video camera, and measuring the wheel speed of the drive wheels with a sensor such as a rotary encoder. The difference between the measured vehicle speed and the measured wheel speeds can be used to determine if a loss of traction has occurred in any of the wheels. If a loss of traction is detected, then a recovery strategy can be selected from a list of recovery strategies in order to reduce the effects of the loss of traction.

Abstract: Aspects of the disclosure provide controlling an autonomous vehicle. For example, a first trajectory generated by a first planning system may be received. A portion of the first trajectory may be input into a second planning system. A second trajectory generated by the second planning system based on the portion of the first trajectory may be received. The second trajectory may be used to determine whether to validate the first trajectory, and when the second trajectory is determined to be validated, the autonomous vehicle may be enabled to be controlled based on the first trajectory.

Abstract: A method includes obtaining a time-series of training samples that include one or more states, a ground truth value, an output value produced in the presence of the one or more states, and an actual error value that is defined as a difference between the ground truth value and the output value. The method also includes training a machine learning model using the time-series of training samples such that the machine learning model is configured to determine a condition-dependent error distribution for a current time step based on simulated states for the current time step.

Abstract: Systems and methods for providing inter-vehicle communication are disclosed. The method includes receiving, at a fleet management system, operating data from one or more self-driving vehicles via a communication network, and operating the fleet management system to determine a characteristic of a set of vehicles of one or more self-driving vehicles satisfies at least one communication condition. In response to determining the set of vehicles satisfies the at least one communication condition, the fleet management system can operate to select a stored data portion from a manager storage unit based at least on the characteristic of the set of vehicles; and transmit the data portion to the set of vehicles via the communication network. A method of providing inter-vehicle communication between one or more self-driving vehicles is also disclosed.

Abstract: A system for path control for a mobile unmanned vehicle in an environment is provided. The system includes: a sensor connected to the mobile unmanned vehicle; the mobile unmanned vehicle configured to initiate a first fail-safe routine responsive to detection of an object in a first sensor region adjacent to the sensor; and a processor connected to the mobile unmanned vehicle. The processor is configured to: generate a current path based on a map of the environment; based on the current path, issue velocity commands to cause the mobile unmanned vehicle to execute the current path; responsive to detection of an obstacle in a second sensor region, initiate a second fail-safe routine in the mobile unmanned vehicle to avoid entry of the obstacle into the first sensor region and initiation of the first fail-safe routine.

Abstract: Methods and systems are provided for traction detection and control of a self-driving vehicle. The self-driving vehicle has drive motors that drive drive-wheels according to a drive-motor speed. Traction detection and control can be obtained by measuring the vehicle speed with a sensor such as a LiDAR or video camera, and measuring the wheel speed of the drive wheels with a sensor such as a rotary encoder. The difference between the measured vehicle speed and the measured wheel speeds can be used to determine if a loss of traction has occurred in any of the wheels. If a loss of traction is detected, then a recovery strategy can be selected from a list of recovery strategies in order to reduce the effects of the loss of traction.

Abstract: Systems and methods for providing inter-vehicle communication are disclosed. The method includes receiving, at a fleet management system, operating data from one or more self-driving vehicles via a communication network, and operating the fleet management system to determine a characteristic of a set of vehicles of one or more self-driving vehicles satisfies at least one communication condition. In response to determining the set of vehicles satisfies the at least one communication condition, the fleet management system can operate to select a stored data portion from a manager storage unit based at least on the characteristic of the set of vehicles; and transmit the data portion to the set of vehicles via the communication network. A method of providing inter-vehicle communication between one or more self-driving vehicles is also disclosed.

Abstract: A system for path control for a mobile unmanned vehicle in an environment is provided. The system includes: a sensor connected to the mobile unmanned vehicle; the mobile unmanned vehicle configured to initiate a first fail-safe routine responsive to detection of an object in a first sensor region adjacent to the sensor; and a processor connected to the mobile unmanned vehicle. The processor is configured to: generate a current path based on a map of the environment; based on the current path, issue velocity commands to cause the mobile unmanned vehicle to execute the current path; responsive to detection of an obstacle in a second sensor region, initiate a second fail-safe routine in the mobile unmanned vehicle to avoid entry of the obstacle into the first sensor region and initiation of the first fail-safe routine.

Abstract: There is provided a driver-support system for use with a human-operated material-transport vehicle, and methods for using the same. The system has at least one sensor, a human-vehicle interface, and a transceiver for communicating with a fleet-management system. The system also has a processor that is configured to provide a mapping application and a localization application based on information received from the sensor. The mapping application and localization application may be provided in a single localization-and-mapping (“SLAM”) application, which may obtain input from the sensor, for example, when the sensor is an optical sensor such as a LiDAR or video camera.

Abstract: The various embodiments described herein generally relate to systems and methods for monitoring an operation of one or more self-driving vehicles. The method involves operating a vehicle processor of a self-driving vehicle to: collect, during operation, operation data associated with the operation of the self-driving vehicle; detect, from the operation data, a trigger condition is satisfied during the operation; determine, for the trigger condition, a pre-condition period and a post-condition period, the pre-condition period defining a time period before the trigger condition occurred and the post-condition period defining a time period following the trigger condition; retrieve, from a vehicle data storage, the subset of the operation data collected by the self-driving vehicle during the pre-condition period and the post-condition period; and transmit the subset of the operation data to the data analysis system.

Abstract: Systems, methods and apparatus are provided for handling operational constraints for unmanned vehicles. The system includes: a plurality of mobile unmanned vehicles for deployment in an environment; a computing device connected to the plurality of unmanned vehicles via a network, the computing device storing, in a memory, a plurality of operational constraints; each operational constraint including (i) a type identifier, (ii) an indication of a region of the environment, and (iii) a property defining a constraint on the operation of the unmanned vehicles within the region. The computing device is configured to: receive a request from one of the mobile unmanned vehicles, the request identifying an operational constraint; responsive to receiving the request, retrieve an operational constraint from the memory based on the request; and send the retrieved operational constraint to the one of the mobile unmanned vehicles.

Abstract: Methods and systems are provided for traction detection and control of a self-driving vehicle. The self-driving vehicle has drive motors that drive drive-wheels according to a drive-motor speed. Traction detection and control can be obtained by measuring the vehicle speed with a sensor such as a LiDAR or video camera, and measuring the wheel speed of the drive wheels with a sensor such as a rotary encoder. The difference between the measured vehicle speed and the measured wheel speeds can be used to determine if a loss of traction has occurred in any of the wheels. If a loss of traction is detected, then a recovery strategy can be selected from a list of recovery strategies in order to reduce the effects of the loss of traction.

Abstract: There is provided a driver-support system for use with a human-operated material-transport vehicle, and methods for using the same. The system has at least one sensor, a human-vehicle interface, and a transceiver for communicating with a fleet-management system. The system also has a processor that is configured to provide a mapping application and a localization application based on information received from the sensor. The mapping application and localization application may be provided in a single localization-and-mapping (“SLAM”) application, which may obtain input from the sensor, for example, when the sensor is an optical sensor such as a LiDAR or video camera.

Abstract: Systems, methods and apparatus are provided for handling operational constraints for unmanned vehicles. The system includes: a plurality of mobile unmanned vehicles for deployment in an environment; a computing device connected to the plurality of unmanned vehicles via a network, the computing device storing, in a memory, a plurality of operational constraints; each operational constraint including (i) a type identifier, (ii) an indication of a region of the environment, and (iii) a property defining a constraint on the operation of the unmanned vehicles within the region. The computing device is configured to: receive a request from one of the mobile unmanned vehicles, the request identifying an operational constraint; responsive to receiving the request, retrieve an operational constraint from the memory based on the request; and send the retrieved operational constraint to the one of the mobile unmanned vehicles.

Abstract: Systems, methods and apparatus are provided for handling operational constraints for unmanned vehicles. The system includes: a plurality of mobile unmanned vehicles for deployment in an environment; a computing device connected to the plurality of unmanned vehicles via a network, the computing device storing, in a memory, a plurality of operational constraints; each operational constraint including (i) a type identifier, (ii) an indication of a region of the environment, and (iii) a property defining a constraint on the operation of the unmanned vehicles within the region. The computing device is configured to: receive a request from one of the mobile unmanned vehicles, the request identifying an operational constraint; responsive to receiving the request, retrieve an operational constraint from the memory based on the request; and send the retrieved operational constraint to the one of the mobile unmanned vehicles.

Abstract: A system for path control for a mobile unmanned vehicle in an environment is provided. The system includes: a sensor connected to the mobile unmanned vehicle; the mobile unmanned vehicle configured to initiate a first fail-safe routine responsive to detection of an object in a first sensor region adjacent to the sensor; and a processor connected to the mobile unmanned vehicle. The processor is configured to: generate a current path based on a map of the environment; based on the current path, issue velocity commands to cause the mobile unmanned vehicle to execute the current path; responsive to detection of an obstacle in a second sensor region, initiate a second fail-safe routine in the mobile unmanned vehicle to avoid entry of the obstacle into the first sensor region and initiation of the first fail-safe routine.

Abstract: Methods and systems are provided for traction detection and control of a self-driving vehicle. The self-driving vehicle has drive motors that drive drive-wheels according to a drive-motor speed. Traction detection and control can be obtained by measuring the vehicle speed with a sensor such as a LiDAR or video camera, and measuring the wheel speed of the drive wheels with a sensor such as a rotary encoder. The difference between the measured vehicle speed and the measured wheel speeds can be used to determine if a loss of traction has occurred in any of the wheels. If a loss of traction is detected, then a recovery strategy can be selected from a list of recovery strategies in order to reduce the effects of the loss of traction.

Abstract: Systems and methods for obstacle avoidance with a self-driving vehicle are provided. The system comprises a processor connected to the self-driving vehicle and a sensor in communication with the processor. The sensor is configured to detect objects. The processor is configured to receive a measurement of the self-driving vehicle's speed, and define a sensor region based on the speed. The processor can determine that an object detected by the sensor is within the sensor region, and then initiate a fail-safe routine. The sensor region may be defined based on a range parameter. The sensor region may be defined based on the stopping distance of the vehicle. The sensor region may be redefined when the vehicle's speed changes.

Abstract: There is provided a driver-support system for use with a human-operated material-transport vehicle, and methods for using the same. The system has at least one sensor, a human-vehicle interface, and a transceiver for communicating with a fleet-management system. The system also has a processor that is configured to provide a mapping application and a localization application based on information received from the sensor. The mapping application and localization application may be provided in a single localization-and-mapping (“SLAM”) application, which may obtain input from the sensor, for example, when the sensor is an optical sensor such as a LiDAR or video camera.