Vehicle control system and method of use
First Claim
1. A system for controlling a vehicle navigating a roadway, comprising:
- a perception module comprising a sensor subsystem, wherein the sensor subsystem generates sensor data, and wherein the perception module outputs a cost map of the area proximal the vehicle, and traffic data associated with traffic objects proximal the vehicle based on an analysis of the sensor data;
a behavior planning module that receives the cost map and the traffic data from the perception module and generates planner primitives based on the cost map and traffic data, wherein the behavior planning module comprises a decision-making block that consists essentially of a trained machine-learning model;
a training module that receives the cost map and the traffic data from the perception module, receives driver input from a vehicle operator, and trains the behavior planning module based on the driver input, the cost map, and the traffic data;
a local planning module comprising a set of task blocks, each of the set of task blocks consisting essentially of an explicitly-programmed set of rules, wherein the local planning module receives the cost map from the perception module and the planner primitives from the behavior planning module, selects a task block based on the planner primitives, and uses the selected task block to generate control commands based on the cost map; and
a control module comprising an actuation subsystem, wherein the control module receives the control commands from the local planning module and controls the actuation subsystem based on the control commands.
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Accused Products
Abstract
A system for controlling a vehicle navigating a roadway, including a perception module that generates sensor data and outputs a cost map and traffic data associated with traffic objects, a behavior planning module that receives the cost map and the traffic data from the perception module and generates planner primitives, a training module that receives the cost map and the traffic data from the perception module, receives driver input from a vehicle operator, and trains the behavior planning module, a local planning module comprising a set of task blocks that receives the cost map from the perception module and the planner primitives from the behavior planning module, selects a task block, and generates control commands using the selected task block; and a control module comprising an actuation subsystem, wherein the control module receives the control commands from the local planning module and controls the actuation subsystem.
20 Citations
20 Claims
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1. A system for controlling a vehicle navigating a roadway, comprising:
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a perception module comprising a sensor subsystem, wherein the sensor subsystem generates sensor data, and wherein the perception module outputs a cost map of the area proximal the vehicle, and traffic data associated with traffic objects proximal the vehicle based on an analysis of the sensor data; a behavior planning module that receives the cost map and the traffic data from the perception module and generates planner primitives based on the cost map and traffic data, wherein the behavior planning module comprises a decision-making block that consists essentially of a trained machine-learning model; a training module that receives the cost map and the traffic data from the perception module, receives driver input from a vehicle operator, and trains the behavior planning module based on the driver input, the cost map, and the traffic data; a local planning module comprising a set of task blocks, each of the set of task blocks consisting essentially of an explicitly-programmed set of rules, wherein the local planning module receives the cost map from the perception module and the planner primitives from the behavior planning module, selects a task block based on the planner primitives, and uses the selected task block to generate control commands based on the cost map; and a control module comprising an actuation subsystem, wherein the control module receives the control commands from the local planning module and controls the actuation subsystem based on the control commands. - View Dependent Claims (2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12)
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13. A method for controlling a vehicle, comprising:
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continuously sampling, at a sensor subsystem of the vehicle, sensor data comprising an image stream, a localization signal, and operational data; transmitting the image stream, the localization signal, and the operational data to a remote teleoperation interface associated with a teleoperator; receiving, at a behavior planning module of the vehicle, a first directive from the teleoperator by way of the remote teleoperation interface, wherein the behavior planning module consists essentially of a trained machine-learning module; generating a planner primitive at the behavior planning module based on the directive; selecting, at a local planning module of the vehicle, a task block based on the planner primitive, wherein the task block consists essentially of an explicitly programmed set of rules; controlling the vehicle, at a control module of the vehicle, based on the selected task block in combination with the sensor data; receiving a second directive from the teleoperator, in response to the vehicle entering a geographic region having a predetermined characteristic; transferring planning authority to the behavior planning module of the vehicle, in response to receiving the second directive; automatically generating a second planner primitive at the behavior planning module based on the sensor data; automatically selecting a second task block, based on the second planner primitive; controlling the vehicle based on the selected second task block in combination with the sensor data; and automatically transferring planning authority to the teleoperator, in response to the vehicle reaching a predetermined geographic location. - View Dependent Claims (14, 15, 16, 17, 18, 19, 20)
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Specification