Robotic process automation for achieving an optimized margin of vehicle operational safety
A system may collect human operator interactions with a vehicle control system interface operatively connected to a vehicle, and may collect vehicle response and operating conditions associated at least contemporaneously with the human operator interaction. Environmental information is collected contemporaneously with the human operator interaction. An artificial intelligence system is trained to control the vehicle with an optimized margin of safety while mimicking the human operator, the training including instructing the artificial intelligence system to take input from an environment data collection module about instances of environmental information associated with the contemporaneously collected vehicle response and operating conditions, where the optimized margin of safety is achieved by training the artificial intelligence system to control the vehicle based on a set of human operator interaction data collected from interactions of an expert human vehicle operator and a set of outcome data from a set of vehicle safety events.
1. A method for mimicking human operation of a vehicle by robotic process automation, the method comprising:
collecting human operator interactions with a vehicle control system interface operatively connected to the vehicle;
collecting vehicle response and operating conditions associated at least contemporaneously with the human operator interactions;
collecting environmental information associated at least contemporaneously with the human operator interactions and storing the environmental information in an environment data collection module;
training an artificial intelligence system to control the vehicle with an optimized margin of safety while mimicking the human operator, the training including instructing the artificial intelligence system to take an input from the environment data collection module about instances of the environmental information associated at least contemporaneously with the collected vehicle response and operating conditions, wherein the optimized margin of safety is achieved by training the artificial intelligence system to control the vehicle based on a set of human operator interaction data collected from interactions of an expert human vehicle operator and a set of outcome data from a set of vehicle safety events;
configuring a set of expert systems to provide outputs based on which a transportation system manages transportation-related parameters, wherein the transportation-related parameters facilitate operation of at least one of: a set of vehicles, a fleet of vehicles, or a transportation system user experience;
configuring a plurality of visual elements representing a set of attributes and parameters of the set of expert systems; and
manipulating at least one of the plurality of visual elements, thereby causing configuration of the set of expert systems.
2. The method of claim 1 , wherein the collecting of the human operator interactions includes capturing patterns of data including at least one of: braking patterns, a follow-behind distance, approach to curve acceleration patterns, lane preferences, or passing preferences.
3. The method of claim 1 , wherein the collecting of the vehicle response and operating conditions includes capturing data from a plurality of vehicle data systems to provide data streams indicating states and changes in state in at least one of: steering, braking, acceleration, forward looking images, or rear-looking images.
4. The method of claim 1 , wherein the training of the artificial intelligence system is further responsive to conditions proximal to the vehicle tracked contemporaneously to the human operator interactions.
5. The method of claim 1 , wherein the training of the artificial intelligence system is further responsive to a plurality of data feeds from remote sensors, and wherein the plurality of data feeds includes data collected by the remote sensors contemporaneously with the human operator interactions.
6. The method of claim 1 , wherein the set of expert systems includes at least one neural network for training the artificial intelligence system.
7. The method of claim 1 , wherein the set of expert systems includes a deep learning system.
8. The method of claim 1 , wherein the set of expert systems includes a random walk-based system.
9. The method of claim 1 , wherein the set of expert systems includes a genetic algorithm system.
10. The method of claim 1 , wherein the set of expert systems includes a convolutional neural network system.
11. The method of claim 1 , wherein the set of expert systems includes a self-organizing system.
12. The method of claim 1 , wherein the set of expert systems includes a hybrid artificial intelligence-based system.
13. The method of claim 1 , wherein the set of expert systems includes an acrylic graph-based system.
14. The method of claim 1 , wherein at least one visual element of the plurality of visual elements displays a plurality of models, and wherein the plurality of models is selectable for use in the set of expert systems.
15. The method of claim 1 , wherein at least one visual element of the plurality of visual elements displays a plurality of neural network categories, and wherein the plurality of neural network categories is selectable for use in the set of expert systems.
16. The method of claim 15 , wherein at least one of the plurality of neural network categories includes a convolutional neural network.
17. The method of claim 1 , wherein the plurality of visual elements includes one or more indicators of suitability of items represented by the plurality of visual elements for a given purpose.