Abstract: 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.

Abstract: 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 instructions and 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 instructions and 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.

Abstract: A system and method for generating simulated vehicles with configured behaviors for analyzing autonomous vehicle motion planners are disclosed. A particular embodiment includes: receiving perception data from a plurality of perception data sensors; obtaining configuration instructions and data including pre-defined parameters and executables defining a specific driving behavior for each of a plurality of simulated dynamic vehicles; generating a target position and target speed for each of the plurality of simulated dynamic vehicles, the generated target positions and target speeds being based on the perception data and the configuration instructions and data; and generating a plurality of trajectories and acceleration profiles to transition each of the plurality of simulated dynamic vehicles from a current position and speed to the corresponding target position and target speed.

Abstract: A system and method for real world autonomous vehicle trajectory simulation are disclosed.

Abstract: A system and method for providing multiple agents for decision making, trajectory planning, and control for autonomous vehicles are disclosed.

Abstract: 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.

Abstract: A data-driven prediction-based system and method for trajectory planning of autonomous vehicles are disclosed. A particular embodiment includes: generating a first suggested trajectory for an autonomous vehicle; generating predicted resulting trajectories of proximate agents using a prediction module; scoring the first suggested trajectory based on the predicted resulting trajectories of the proximate agents; generating a second suggested trajectory for the autonomous vehicle and generating corresponding predicted resulting trajectories of proximate agents, if the score of the first suggested trajectory is below a minimum acceptable threshold; and outputting a suggested trajectory for the autonomous vehicle wherein the score corresponding to the suggested trajectory is at or above the minimum acceptable threshold.

Abstract: A prediction-based system and method for trajectory planning of autonomous vehicles are disclosed.

Abstract: A prediction-based system and method for trajectory planning of autonomous vehicles are disclosed.

Abstract: 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.

Abstract: 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.

Abstract: A system and method for adaptive cruise control with proximate vehicle detection are disclosed. The example embodiment can be configured for: receiving input object data from a subsystem of a host vehicle, the input object data including distance data and velocity data relative to detected target vehicles; detecting the presence of any target vehicles within a sensitive zone in front of the host vehicle, to the left of the host vehicle, and to the right of the host vehicle; determining a relative speed and a separation distance between each of the detected target vehicles relative to the host vehicle; and generating a velocity command to adjust a speed of the host vehicle based on the relative speeds and separation distances between the host vehicle and the detected target vehicles to maintain a safe separation between the host vehicle and the target vehicles.

Abstract: A system and method for adaptive cruise control for defensive driving are disclosed. A particular embodiment includes: receiving input object data from a subsystem of an autonomous vehicle, the input object data including distance data and velocity data relative to a lead vehicle; generating a weighted distance differential corresponding to a weighted difference between an actual distance between the autonomous vehicle and the lead vehicle and a desired distance between the autonomous vehicle and the lead vehicle; generating a weighted velocity differential corresponding to a weighted difference between a velocity of the autonomous vehicle and a velocity of the lead vehicle; combining the weighted distance differential and the weighted velocity differential with the velocity of the lead vehicle to produce a velocity command for the autonomous vehicle; and controlling the autonomous vehicle to conform to the velocity command.

Abstract: 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.

Abstract: A system and method for adaptive cruise control for low speed following are disclosed. A particular embodiment includes: receiving input object data from a subsystem of an autonomous vehicle, the input object data including distance data and velocity data relative to a lead vehicle; generating a weighted distance differential corresponding to a weighted difference between an actual distance between the autonomous vehicle and the lead vehicle and a desired distance between the autonomous vehicle and the lead vehicle; generating a weighted velocity differential corresponding to a weighted difference between a velocity of the autonomous vehicle and a velocity of the lead vehicle; combining the weighted distance differential and the weighted velocity differential with the velocity of the lead vehicle to produce a velocity command for the autonomous vehicle; adjusting the velocity command using a dynamic gain; and controlling the autonomous vehicle to conform to the adjusted velocity command.

Abstract: A system and method for large scale distributed simulation for realistic multiple-agent interactive environments are disclosed. A particular embodiment includes: generating a vicinal scenario for each simulated vehicle in an iteration of a simulation, the vicinal scenarios corresponding to different locations, traffic patterns, or environmental conditions being simulated; assigning a processing task to at least one of a plurality of distributed computing devices to generate vehicle trajectories for each of a plurality of simulated vehicles of the simulation based on the vicinal scenario; and updating a state and trajectory of each of the plurality of simulated vehicles based on processed data received from the plurality of distributed computing devices.

Abstract: A system and method for real world autonomous vehicle perception simulation are disclosed. A particular embodiment includes: receiving perception data from a plurality of sensors of an autonomous vehicle; configuring the perception simulation operation based on a comparison of the perception data against ground truth data; generating simulated perception data by simulating errors related to the physical constraints of one or more of the 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.

Abstract: A system and method for path planning of autonomous vehicles based on gradient are disclosed. A particular embodiment includes: generating and scoring a first suggested trajectory for an autonomous vehicle; generating a trajectory gradient based on the first suggested trajectory; generating and scoring a second suggested trajectory for the autonomous vehicle, the second suggested trajectory being based on the first suggested trajectory and a human driving model; and outputting the second suggested trajectory if the score corresponding to the second suggested trajectory is within a score differential threshold relative to the score corresponding to the first suggested trajectory.