A control system an unmanned vehicle includes a first processing unit configured to execute a primary autopilot process for controlling the unmanned vehicle. The control system further includes a programmable logic array in operative communication with the first processing unit. The control system also includes a state machine configured in the programmable logic array. The state machine is configured to enable control of the unmanned vehicle according to a backup autopilot process in response to an invalid output of the first processing unit.

Disclosed in the present invention is a shuttle vehicle traveling and positioning control method based on encoder self-correction. Provided is a self-correction solution based on track positioning identifiers and external encoders. When a shuttle vehicle travels through each identifier, information is fed back and a servo target position is updated instantaneously, so as to eliminate, at any time, a cumulative error caused by skidding; and the shuttle vehicle realizes a full-closed-loop traveling and positioning control process under the guidance of position information which is corrected at any time. The shuttle vehicle traveling and positioning control method based on encoder self-correction comprises the following implementation stages: 1) performing customization and initialization; 2) performing self-learning; 3) performing self-correction; 4) updating a target position; and 5) handling a position offset.

Disclosed in the present invention is a shuttle vehicle traveling and positioning control method based on encoder self-correction. Provided is a self-correction solution based on track positioning identifiers and external encoders. When a shuttle vehicle travels through each identifier, information is fed back and a servo target position is updated instantaneously, so as to eliminate, at any time, a cumulative error caused by skidding; and the shuttle vehicle realizes a full-closed-loop traveling and positioning control process under the guidance of position information which is corrected at any time. The shuttle vehicle traveling and positioning control method based on encoder self-correction comprises the following implementation stages: 1) performing customization and initialization; 2) performing self-learning; 3) performing self-correction; 4) updating a target position; and 5) handling a position offset.

Aspects of the disclosure relate to controlling a vehicle in an autonomous driving mode. The system includes a plurality of sensors configured to generate sensor data. The system also includes a first computing system configured to generate trajectories using the sensor data and send the generated trajectories to a second computing system. The second computing system is configured to cause the vehicle to follow a receive trajectory. The system also includes a third computing system configured to, when there is a failure of the first computer system, generate and send trajectories to the second computing system based on whether a vehicle is located on a highway or a surface street.

Aspects of the disclosure relate to controlling a vehicle in an autonomous driving mode. The system includes a plurality of sensors configured to generate sensor data. The system also includes a first computing system configured to generate trajectories using the sensor data and send the generated trajectories to a second computing system. The second computing system is configured to cause the vehicle to follow a receive trajectory. The system also includes a third computing system configured to, when there is a failure of the first computer system, generate and send trajectories to the second computing system based on whether a vehicle is located on a highway or a surface street.

An automated driving system includes a security companion subsystem to access data generated at a compute subsystem of the automated driving system, which indicates a determination by the compute subsystem associated with an automated driving task. The security companion subsystem determines whether the determination is safe based on the data. The security companion subsystem is configured to realize a higher safety integrity level than the compute subsystem.

In a network of autonomous or semi-autonomous vehicles, an alert may be triggered when one of the vehicles switches from autonomous to manual mode. The alert may be communicated to nearby autonomous vehicles so that drivers of those vehicles may become aware of a potentially unpredictable manual driver nearby. Drivers of autonomous vehicles who may have become disengaged (e.g., sleeping, reading, talking, etc.) during autonomous driving may become re-engaged upon noticing the alert. A re-engaged driver may choose to switch his/her own vehicle from autonomous to manual mode in order to appropriately react to an unpredictable nearby manual driver. In additional or alternative embodiments, the alert may be triggered or intensified when indications of impairment of a nearby driver or malfunction of a nearby vehicle are detected.

In a network of autonomous or semi-autonomous vehicles, an alert may be triggered when one of the vehicles switches from autonomous to manual mode. The alert may be communicated to nearby autonomous vehicles so that drivers of those vehicles may become aware of a potentially unpredictable manual driver nearby. Drivers of autonomous vehicles who may have become disengaged (e.g., sleeping, reading, talking, etc.) during autonomous driving may become re-engaged upon noticing the alert. A re-engaged driver may choose to switch his/her own vehicle from autonomous to manual mode in order to appropriately react to an unpredictable nearby manual driver. In additional or alternative embodiments, the alert may be triggered or intensified when indications of impairment of a nearby driver or malfunction of a nearby vehicle are detected.

Techniques are disclosed for validating a vehicle trajectory using redundancy in hardware and software components. A computed vehicle trajectory may be validated independently via two separate SoCs by projecting the 3D computed vehicle trajectory onto a 2D image acquired by a vehicle camera. Each SoC may perform an independent trajectory validation with the use of a trained machine learning model such as a deep neural network (DNN). The DNNs implemented by each SoC may perform trajectory validation using a separate set of camera inputs for the mapping and validation process. The vehicle implements the vehicle trajectory for control functions only when the trajectory is validated by both SoC trajectory validators, thus providing a robust trajectory validation process that complies with regulatory requirements such as Automotive Safety Integrity Level (ASIL) level D.

Techniques are disclosed for validating a vehicle trajectory using redundancy in hardware and software components. A computed vehicle trajectory may be validated independently via two separate SoCs by projecting the 3D computed vehicle trajectory onto a 2D image acquired by a vehicle camera. Each SoC may perform an independent trajectory validation with the use of a trained machine learning model such as a deep neural network (DNN). The DNNs implemented by each SoC may perform trajectory validation using a separate set of camera inputs for the mapping and validation process. The vehicle implements the vehicle trajectory for control functions only when the trajectory is validated by both SoC trajectory validators, thus providing a robust trajectory validation process that complies with regulatory requirements such as Automotive Safety Integrity Level (ASIL) level D.