Abstract: A system and method for customizing a driving behavior of an autonomous vehicle is disclosed. The method includes receiving, by a controller of the autonomous vehicle, at least one user preference. The at least one user preference relates to a preferred driving behavior. The method also includes modifying a pre-programmed driving behavior of the autonomous vehicle based on the received at least one user preference. The method also includes instructing the autonomous vehicle to drive according to the modified driving behavior.

Abstract: An autonomous vehicle, a system and method of operating the autonomous vehicle. An environmental sensor obtains one or more parameters of external agents of the vehicle. A processor of the vehicle obtains a route having a destination at the autonomous vehicle, builds a Markov state model of the route that includes a plurality of states for the autonomous vehicle and one or more parameters of the external agents, generates a plurality of driving policies for navigating the route, selects a policy for navigating the route from the plurality of driving policies using a Markov Decision Process, and executes the selected policy at the autonomous vehicle to navigate the vehicle along the route towards the destination.

Abstract: Systems and methods are provided for controlling a vehicle. In one embodiment, a method includes receiving vehicle state data, high-definition map data, and vehicle object environment data, generating a trajectory path that is optimal with respect to the received data, determining whether to update the trajectory path using Bézier curves based on the received data, performing an assessment of the trajectory using properties of Bézier curves, and generating an updated trajectory.

Abstract: Presented are scenario-planning and route-generating distributed computing systems, methods for operating/constructing such systems, and vehicles with scenario-plan selection and real-time trajectory planning capabilities. A method for controlling operation of a motor vehicle includes determining vehicle state data, such as a current position and velocity of the vehicle, and path plan data, such as an origin and desired destination of the vehicle. A remote computing node off-board from the motor vehicle generates a list of trajectory plan candidates based on the vehicle state data, the path plan data, and current road scenario data. The remote computing node then calculates a respective travel cost for each candidate in the trajectory plan candidates list, and sorts the list from lowest to highest travel cost. The candidate with the lowest travel cost is transmitted to a resident vehicle controller. The vehicle controller executes an automated driving operation based on the received trajectory plan candidate.

Abstract: The present application generally relates to a method and apparatus for generating an action policy for controlling an autonomous vehicle. In particular, the system performs a deep learning algorithm in order to determine the action policy and an automatically generated curriculum system to determine a number of increasingly difficult tasks in order to refine the action policy.

Abstract: Systems and method are provided for evaluating control features of an autonomous vehicle for development or validation purposes. A real-world sensor data set is generated by an autonomous vehicle having sensors. A sensing and perception module generates perturbations of the real-world sensor data set. A generator module generates a 3-dimensional object data set from the real-world sensor data set. A planning and behavior module generates perturbations of the 3-dimensional object data set. A testing module tests a control feature such as an algorithm or software using the 3-dimensional object data set. A control module executes command outputs from the control feature for evaluation.

Abstract: Systems and methods are provided for controlling a vehicle. Control signals are generated at a high-level controller based on one or more sources of input data, comprising at least one of: sensors that provide sensor output information, map data and goals. The high-level controller comprises first controller modules comprising: an input processing module, a projection module, a memories module, a world model module, and a decision processing module that comprises a control model executor module. The control signals are processed at a low-level controller to generate commands that control a plurality of vehicle actuators of the vehicle in accordance with the control signals to execute one or more scheduled actions to be performed to automate driving tasks.

Abstract: Systems and method are provided for controlling a vehicle.

Abstract: A system and method for assisting vehicle parking is disclosed. The method includes transmitting, by an electronic controller of a vehicle, a parking space request. The parking space request is transmitted to a reservation system. The reservation system determines whether a parking space is available to fulfill the parking space request. The determining is based on availability information that is received from a parking infrastructure. The method also includes receiving an indication. The indication indicates whether a parking spot has been reserved to fulfill the parking space request.

Abstract: A vehicle, system and method of operating the vehicle. In one exemplary embodiment, a method of operating an autonomous vehicle is disclosed. An environmental sensor obtains one or more parameters of external agents of the vehicle. A processor of the vehicle obtains a route having a destination at the autonomous vehicle, builds a Markov state model of the route that includes a plurality of states for the autonomous vehicle and one or more parameters of the external agents, generates a plurality of driving policies for navigating the route, selects a policy for navigating the route from the plurality of driving policies using a Markov Decision Process, and executes the selected policy at the autonomous vehicle to navigate the vehicle along the route towards the destination.

Abstract: A system and method to perform autonomous operation of a vehicle include obtaining one or more image frames for a time instance t from corresponding one or more sensors. Processing the one or more image frames includes performing convolutional processing to obtain a multi-dimensional matrix xt. The method includes operating on the multi-dimensional matrix xt to obtain output ht. The operating includes using an output ht-1 of the operating for a previous time instance t?1. The method also includes post-processing the output ht to obtain one or more control signals to affect operation of the vehicle.

Abstract: Systems and methods are provided for controlling a vehicle. In one embodiment, a method includes receiving vehicle state data, high-definition map data, and vehicle object environment data, generating a trajectory path that is optimal with respect to the received data, determining whether to update the trajectory path using Bézier curves based on the received data, performing an assessment of the trajectory using properties of Bézier curves, and generating an updated trajectory.

Abstract: Methods and apparatus are disclosed for providing autonomous driving system functions. The apparatus includes a communication system configured to receive traffic information from an external entity, a guidance system configured to provide guiding signals for guiding a vehicle, and a vehicle control system configured to generate control signals for controlling the vehicle based on the guiding signals. The guidance system is configured to determine a lane guidance signal based on the received traffic information and to instruct the vehicle control system to change a lane of a road having multiple lanes for the same driving direction. The guidance system is configured to map the road by a state machine having individual states representing lanes of a road and longitudinal segments of the road, such that a route of the vehicle is determined as a sequence of states of the state machine.