Tusimple, Inc.

Liu Liu

Wutu Lin

Xing Sun

Yufei Zhao

Zijie Xuan

Kai-Chieh Ma

Xiaoling Han

Yi Wang

Ruiliang Zhang

Lindong Sun

Jun Chen

Yilun Chen

Guangyu Li

Yuwei Wang

Xingdong Li

Xiaomin Zhang

Charles A Price

Che Kun Law

Ke Quan

Jiaqi Liu

A system and method for autonomous vehicle control to minimize energy cost are disclosed. A particular embodiment includes: generating a plurality of potential routings and related vehicle motion control operations for an autonomous vehicle to cause the autonomous vehicle to transit from a current position to a desired destination; generating predicted energy consumption rates for each of the potential routings and related vehicle motion control operations using a vehicle energy consumption model; scoring each of the plurality of potential routings and related vehicle motion control operations based on the corresponding predicted energy consumption rates; selecting one of the plurality of potential routings and related vehicle motion control operations having a score within an acceptable range; and outputting a vehicle motion control output representing the selected one of the plurality of potential routings and related vehicle motion control operations.

System and method for autonomous vehicle control to minimize energy cost

A system and method for generating simulated vehicles with configured behaviors for analyzing autonomous vehicle motion planners are disclosed. A particular embodiment includes: obtaining configuration instructions and data for each of a plurality of simulated vehicles, a specific driving behavior for each of the plurality of simulated vehicles corresponding to perception data obtained from perception data sensors; and generating a plurality of trajectories and acceleration profiles to transition each of the plurality of simulated vehicles from a current position and speed to a corresponding target position and target speed, the target position and the target speed corresponding to the specific driving behavior for each of the plurality of simulated vehicles.

System and method for generating simulated vehicles with configured behaviors for analyzing autonomous vehicle motion planners

A system and method for providing multiple agents for decision making, trajectory planning, and control for autonomous vehicles are disclosed. A particular embodiment includes: partitioning a multiple agent autonomous vehicle control module for an autonomous vehicle into a plurality of subsystem agents, the plurality of subsystem agents including a deep computing vehicle control subsystem and a fast response vehicle control subsystem; receiving a task request from a vehicle subsystem; determining if the task request is appropriate for the deep computing vehicle control subsystem or the fast response vehicle control subsystem based on content of the task request or a context of the autonomous vehicle; dispatching the task request to the deep computing vehicle control subsystem or the fast response vehicle control subsystem based on the determination; causing execution of the deep computing vehicle control subsystem or the fast response vehicle control subsystem by use of a data processor to produce a vehicle control output; and providing the vehicle control output to a vehicle control subsystem of the autonomous vehicle.

System and method for providing multiple agents for decision making, trajectory planning, and control for autonomous vehicles

A system and method for real world autonomous vehicle trajectory simulation may include: receiving training data from a data collection system; obtaining ground truth data corresponding to the training data; performing a training phase to train a plurality of trajectory prediction models; and performing a simulation or operational phase to generate a vicinal scenario for each simulated vehicle in an iteration of a simulation. Vicinal scenarios may correspond to different locations, traffic patterns, or environmental conditions being simulated. Vehicle intention data corresponding to a data representation of various types of simulated vehicle or driver intentions.

System and method for real world autonomous vehicle trajectory simulation

A system and method for real world autonomous vehicle perception simulation are disclosed. A particular embodiment includes: configuring a sensor noise modeling module to produce simulated sensor errors or noise data with a configured degree, extent, and timing of simulated sensor errors or noise based on a set of modifiable parameters; using the simulated sensor errors or noise data to generate simulated perception data by simulating errors related to constraints of one or more of a plurality of sensors, and by simulating noise in data provided by a sensor processing module corresponding to one or more of the plurality of sensors; and providing the simulated perception data to a motion planning system for the autonomous vehicle.

Perception simulation for improved autonomous vehicle control

Devices, systems and methods for remote safe driving of an autonomous vehicle are described. One exemplary method includes selecting, based on a classification of driver behavior, at least one of a set of driving actions and transmitting, to a vehicle over a secure communication channel, the at least one of the set of driving actions.

Remote safe driving methods and systems

An autonomous vehicle simulation system for analyzing motion planners is disclosed. A particular embodiment includes: receiving map data corresponding to a real world driving environment; obtaining perception data and configuration data including pre-defined parameters and executables defining a specific driving behavior for each of a plurality of simulated dynamic vehicles; generating simulated perception data for each of the plurality of simulated dynamic vehicles based on the map data, the perception data, and the configuration data; receiving vehicle control messages from an autonomous vehicle control system; and simulating the operation and behavior of a real world autonomous vehicle based on the vehicle control messages received from the autonomous vehicle control system.

Autonomous vehicle simulation system for analyzing motion planners

Disclosed are devices, systems and methods for securing wireless communications between a remote monitor center and a vehicle by using redundancy measures to increase the robustness of the system. In some embodiments, a system may include redundant communication channels, deploy redundant hardware and software stacks to enable switching to a backup in an emergency situation, and employ hypervisors at both the remote monitor center and the vehicle to monitor hardware and software resources and perform integrity checks. In other embodiments, message digests based on a cryptographic hash function and a plurality of predetermined commands are generated at both the remote monitor center and the vehicle, and compared to ensure the continuing integrity of the wireless communication system.

Security architecture for a real-time remote vehicle monitoring system

An apparatus is provided to comprise: at least one processor and at least one memory including computer program code, the at least one memory and the computer program code configured to, with the at least one processor, cause the apparatus to: receive, from an electronic control unit of a vehicle, a test result comprising a result of a self test performed by the electronic control unit; analyze the received test result to determine a vehicle status, the vehicle status comprising a status indicator and status information to be included in a report; periodically transmit, to a remote system according to a first time period, a report including the vehicle status, a duration of the first time period being dependent on the vehicle status; receive, from the remote system, a control command; and in response to the control command being received, implement the control command.

Monitoring system for autonomous vehicle operation

Described are devices, systems and methods for real-time remote control of vehicles with high redundancy. In some embodiments, two copies of at least one control command are received using two different wireless communication protocols, and are compared. The at least one control command is executed when the two copies are in agreement, but is rejected when the two copies differ. In other embodiments, additional wireless communication protocols may exist to provide a redundant mode of communication when one of the two different wireless communication protocols are unavailable. In yet other embodiments, redundant GPS units may be used to determine availability of any of the communication protocols, and relevant control commands may be downloaded in advance to circumvent a lack of coverage.

Real-time remote control of vehicles with high redundancy

A system and method for implementing a neural network based vehicle dynamics model are disclosed. A particular embodiment includes: training a machine learning system with a training dataset corresponding to a desired autonomous vehicle simulation environment; receiving vehicle control command data and vehicle status data, the vehicle control command data not including vehicle component types or characteristics of a specific vehicle; by use of the trained machine learning system, the vehicle control command data, and vehicle status data, generating simulated vehicle dynamics data including predicted vehicle acceleration data; providing the simulated vehicle dynamics data to an autonomous vehicle simulation system implementing the autonomous vehicle simulation environment; and using data produced by the autonomous vehicle simulation system to modify the vehicle status data for a subsequent iteration.

Neural network based vehicle dynamics model

The system and method make it feasible to develop an autonomous vehicle control system for complex vehicles, such as for cargo trucks and other large payload vehicles. The method and system commence by first obtaining 3-dimensional data for one or more sections of roadway. Once the 3-dimensional roadway data is obtained, that data is used to run computer simulations of a computer model of a specific vehicle being controlled by a generic vehicle control algorithm or system. The generic vehicle control algorithm is optimized by running the simulations utilizing the 3-dimensional roadway data until an acceptable performance result is achieved. Once an acceptable simulation is executed using the generic vehicle control algorithm, the control algorithm/system is used to run one or more real-world driving tests on the roadway for which the 3-dimensional data was obtained. Finally, the computer model for the vehicle is modified.

Method and system for modeling autonomous vehicle behavior

A system and method for using human driving patterns to manage speed control for autonomous vehicles are disclosed. A particular embodiment includes: generating data corresponding to desired human driving behaviors; training a human driving model module using a reinforcement learning process and the desired human driving behaviors; receiving a proposed vehicle speed control command; determining if the proposed vehicle speed control command conforms to the desired human driving behaviors by use of the human driving model module; and validating or modifying the proposed vehicle speed control command based on the determination.

System and method for using human driving patterns to manage speed control for autonomous vehicles

A system and method for using human driving patterns to detect and correct abnormal driving behaviors of autonomous vehicles are disclosed. A particular embodiment includes: generating data corresponding to a normal driving behavior safe zone; receiving a proposed vehicle control command; comparing the proposed vehicle control command with the normal driving behavior safe zone; and issuing a warning alert if the proposed vehicle control command is outside of the normal driving behavior safe zone. Another embodiment includes modifying the proposed vehicle control command to produce a modified and validated vehicle control command if the proposed vehicle control command is outside of the normal driving behavior safe zone.

System and method for using human driving patterns to detect and correct abnormal driving behaviors of autonomous vehicles

A prediction-based system and method for trajectory planning of autonomous vehicles are disclosed. A particular embodiment is configured to: receive training data and ground truth data from a training data collection system, the training data including perception data and context data corresponding to human driving behaviors; perform a training phase for training a trajectory prediction module using the training data; receive perception data associated with a host vehicle; and perform an operational phase for extracting host vehicle feature data and proximate vehicle context data from the perception data, generating a proposed trajectory for the host vehicle, using the trained trajectory prediction module to generate predicted trajectories for each of one or more proximate vehicles near the host vehicle based on the proposed host vehicle trajectory, determining if the proposed trajectory for the host vehicle will conflict with any of the predicted trajectories of the proximate vehicles, and modifying the proposed trajectory for the host vehicle until conflicts are eliminated.

Growing community of inventors

San Diego, CA, United States of America

Liu Liu