Robotic process automation for achieving an optimized margin of vehicle operational safety
A system may include an operator data collection module to capture human operator interactions with a vehicle control system interface and may include a vehicle data collection module to capture vehicle response and operating conditions associated at least contemporaneously with the human operator interaction. An artificial intelligence system learns to control the vehicle with an optimized margin of safety while mimicking the human operator, where the artificial intelligence system is responsive to the robotic process automation system and the artificial intelligence system is to detect data indicative of at least one of a plurality of the instances of environmental information associated with the contemporaneously captured vehicle response and operating conditions. The optimized margin of safety is to be achieved by training the artificial intelligence system to control the vehicle based on the set of human operator interaction data collected at the robotic process automation system.
1. A transportation system for optimizing a margin of safety when mimicking human operation of a vehicle, the transportation system comprising:
a robotic process automation system comprising:
an operator data collection module to capture human operator interaction data with a vehicle control system interface;
a vehicle data collection module to capture vehicle response and operating conditions associated at least contemporaneously with the human operator interaction; and
an environment data collection module to capture instances of environmental information associated at least contemporaneously with the human operator interactions; and
an artificial intelligence system to learn to control the vehicle with an optimized margin of safety while mimicking the human operator, wherein the artificial intelligence system is responsive to the robotic process automation system, wherein the artificial intelligence system is to detect data indicative of at least one of a plurality of the instances of environmental information associated with the contemporaneously captured vehicle response and operating conditions, wherein the optimized margin of safety is to be achieved by training the artificial intelligence system to control the vehicle based on the human operator interaction data collected at the robotic process automation system, and controlling the vehicle with the optimized margin of safety.
2. The transportation system of claim 1 , wherein the robotic process automation system further comprises a feedback module configured to provide corrective suggestions to the human operator based on an assessment by the artificial intelligence system of the human operator interactions and vehicle responses.
3. The transportation system of claim 1 , wherein the operator data collection module includes a camera system configured to capture visual data of the human operator interactions with the vehicle control system interface.
4. The transportation system of claim 1 , wherein the vehicle data collection module is further configured to capture data related to powertrain performance of the vehicle during the human operator interaction.
5. The transportation system of claim 1 , wherein the environment data collection module includes sensors for detecting road surface conditions and adjusting driving parameters of the vehicle to maintain the optimized margin of safety.
6. The transportation system of claim 1 , wherein the artificial intelligence system comprises a neural network trained to recognize patterns in the human operator interactions and to predict potential safety risks based on the patterns.
7. The transportation system of claim 1 , wherein the artificial intelligence system is further configured to adjust operational parameters of the vehicle in real-time to maintain the optimized margin of safety during autonomous operation.
8. The transportation system of claim 1 , wherein the artificial intelligence system utilizes machine learning algorithms wherein the machine learning algorithms are trained on a dataset including historical vehicle operation data and environmental conditions.
9. The transportation system of claim 1 , wherein the robotic process automation system is further configured to integrate data from a navigation system of the vehicle to anticipate changes in the environment and to adjust operation of the vehicle accordingly.
10. The transportation system of claim 1 , wherein the robotic process automation system includes an interface for the human operator to review and adjust operational parameters used by the artificial intelligence system for controlling the vehicle.
11. A method of robotic process automation for achieving an optimized margin of vehicle operational safety, the method comprising:
tracking expert human vehicle control interactions with a vehicle control-facilitating interface;
recording the tracked expert human vehicle control interactions in a robotic process automation system training data structure;
tracking vehicle operational state information of a vehicle; recording vehicle operational state information in the robotic process automation system training data structure;
training, via at least one neural network, the vehicle to operate with the optimized margin of vehicle operational safety in a manner consistent with the expert human vehicle control interactions based on the expert human vehicle control interactions and the vehicle operational state information in the robotic process automation system training data structure; and
controlling at least one aspect of the vehicle with a trained artificial intelligence system.
12. The method of claim 11 , wherein the vehicle control-facilitating interface comprises at least one of: a touch screen, a virtual assistant, an entertainment system interface, a communication interface, or a navigation interface.
13. The method of claim 11 , wherein the expert human vehicle control interactions include at least one of: braking patterns, follow-behind distance, approach to curve acceleration patterns, lane preferences, or passing preferences.
14. The method of claim 11 , wherein the vehicle operational state information includes data streams indicating states and changes in state in at least one of: steering, braking, acceleration, forward looking images, or rear-looking images.
15. The method of claim 11 , wherein the at least one neural network comprises a convolutional neural network.
16. The method of claim 11 , wherein the training further comprises applying deep learning to optimize the margin of vehicle operational safety by structured variation in the controlling of the at least one aspect of the vehicle and processing feedback from the controlling of the at least one aspect of the vehicle with machine learning.
17. The method of claim 11 , wherein a robotic process automation system includes an operator data collection module to capture human operator interaction with a vehicle control system interface, a vehicle data collection module to capture vehicle response and operating conditions associated at least contemporaneously with the human operator interaction, and an environment data collection module to capture instances of environmental information associated at least contemporaneously with the human operator interactions.
18. The method of claim 11 , wherein a robotic process automation system facilitates automation of a decision-making workflow employed by the artificial intelligence system.
19. The method of claim 11 , wherein a robotic process automation system facilitates automation of a remote control workflow, wherein the artificial intelligence system employs the automated remote control workflow to remotely control the vehicle.