Systems and methods for increasing the safety of voice conversations between drivers and remote parties
A system for increasing the safety of voice conversations between drivers and remote parties, wherein the system includes an in-vehicle subsystem, wherein the in-vehicle subsystem is configured to communicate wirelessly with a remote subsystem accessible to the remote parties in an initial state, a plurality of sensors, and a computing apparatus, wherein the computing apparatus is configured to received monitoring data from the plurality of sensors, determine risk levels as a function of the monitoring data, and alter a state of the communication between the in-vehicle and the remote subsystem as an automatic safety response according to the risk levels, wherein altering the state of the communication includes switching the initial state to a second state as a function of a transition between a first risk level and a second risk level, and transmitting an alert to the remote subsystem as a function of the automatic safety response.
1 . A system for increasing a safety of voice conversations between drivers and remote parties, wherein the system comprises:
an in-vehicle subsystem, wherein the in-vehicle subsystem is configured to:
communicate wirelessly with a remote subsystem accessible to the remote parties in an initial state;
a plurality of sensors, wherein the plurality of sensors is configured to generate monitoring data; and
a computing apparatus, wherein the computing apparatus is configured to:
receive the monitoring data from the plurality of sensors;
determine a plurality of risk levels comprising a risk level scale as a function of the monitoring data; and
alter a state of the communication between the in-vehicle subsystem and the remote subsystem as an automatic safety response according to the plurality of risk levels, wherein altering the state of the communication comprises:
switching the initial state to a second state as a function of a transition between a first risk level of the plurality of risk levels and a second risk level of the plurality of risk levels;
transmitting an alert to the remote subsystem as a function of the automatic safety response;
operating a dialog interaction engine configured to generate audio prompts to a driver using speech synthesis to drive a speaker array and evaluate responsiveness of the driver using speech recognition and natural language processing, wherein the dialog interaction engine is trained on driver-interaction context data, wherein the evaluated responsiveness recognizes at least a degree of alertness of the driver; and
automatically force a conversation by the remote subsystem to the driver if the degree of alertness is high based on the risk level scale.
2 . The system of claim 1 , wherein the initial state of the communication between the in-vehicle subsystem and the remote subsystem comprises allowing and suspending a remote voice conversation.
3 . The system of claim 2 , wherein altering the state of the communication between the in-vehicle subsystem and the remote subsystem further comprises altering the initial state of the communication using a state machine model, comprising:
activating a digital assistant's dialog flow as a function of the monitoring data; and
switching the second state to a third state as a function of the transition between the second risk level of the plurality of risk levels and a third risk level of the plurality of risk levels.
4 . The system of claim 3 , wherein the third state of the communication between the in-vehicle subsystem and the remote subsystem comprises:
overriding the digital assistant's dialog flow; and
terminating the remote voice conversation.
5 . The system of claim 4 , wherein switching the second state to the third state comprises:
comparing the second risk level of the plurality of risk levels to a second range; and
switching the second state to the third state as a function of the comparison.
6 . The system of claim 1 , wherein the computing apparatus comprises a multimodal neural network, wherein the multimodal neural network is configured to receive as input a combination of text-based digital assistant interactions and a plurality of monitoring data and output textual answers.
7 . The system of claim 1 , wherein switching the initial state to the second state comprises:
comparing the first risk level of the plurality of risk levels to a first range; and
switching the initial state to the second state as a function of the comparison.
8 . The system of claim 1 , wherein determining the plurality of risk levels comprises:
generating a risk machine learning model using training data, wherein the training data comprises a plurality of monitoring data as input correlated to a plurality of risk levels as output; and
determining the plurality of risk levels using the risk machine learning model.
9 . The system of claim 1 , wherein determining the plurality of risk levels further comprises:
transmitting, to the remote subsystem, the monitoring data and an estimated risk level; and
receiving, from the remote subsystem, complementary information to refine and determine a combined risk level.
10 . The system of claim 1 , wherein transmitting the alert comprises notifying emergency services using traffic monitoring services as a function of the alert.
11 . A method for increasing a safety of voice conversations between drivers and remote parties, wherein the method comprises:
generating, by a plurality of sensors, monitoring data;
receiving, by a computing apparatus, the monitoring data from the plurality of sensors;
determining, by the computing apparatus, a plurality of risk levels comprising a risk level scale as a function of the monitoring data;
altering, by the computing apparatus, a state of a communication between an in-vehicle and a remote subsystem as an automatic safety response according to the plurality of risk levels, wherein altering the state of the communication comprises:
switching an initial state to a second state as a function of a transition between a first risk level of the plurality of risk levels and a second risk level of the plurality of risk levels; and
transmitting an alert to the remote subsystem as a function of the automatic safety response;
operating, by the computing apparatus, a dialog interaction engine configured to generate audio prompts to a driver using speech synthesis to drive a speaker array and evaluate responsiveness of the driver using speech recognition and natural language processing, wherein the dialog interaction engine is trained on driver-interaction context data, wherein the evaluated responsiveness recognizes at least a degree of alertness of the driver; and
automatically force a conversation by the remote subsystem to the driver if the degree of alertness is high based on the risk level scale.
12 . The method of claim 11 , wherein the initial state of the communication between the in-vehicle subsystem and the remote subsystem comprises allowing and suspending a remote voice conversation.
13 . The method of claim 12 , wherein altering the state of the communication between the in-vehicle subsystem and the remote subsystem further comprises altering the initial state of the communication using a state machine model, comprising:
activating a digital assistant's dialog flow as a function of the monitoring data; and
switching the second state to a third state as a function of the transition between the second risk level of the plurality of risk levels and a third risk level of the plurality of risk levels.
14 . The method of claim 13 , wherein the third state of the communication between the in-vehicle subsystem and the remote subsystem comprises:
overriding the digital assistant's dialog flow; and
terminating the remote voice conversation.
15 . The method of claim 14 , wherein the computing apparatus comprises a multimodal neural network.
16 . The method of claim 14 , wherein switching the second state to the third state comprises:
comparing the second risk level of the plurality of risk levels to a second range; and
switching the second state to the third state as a function of the comparison.
17 . The method of claim 11 , wherein switching the initial state to the second state comprises:
comparing the first risk level of the plurality of risk levels to a first range; and
switching the initial state to the second state as a function of the comparison.
18 . The method of claim 11 , wherein determining the plurality of risk levels comprises:
generating a risk machine learning model using training data, wherein the training data comprises a plurality of monitoring data as input correlated to a plurality of risk levels as output; and
determining the plurality of risk levels using the risk machine learning model.
19 . The method of claim 11 , wherein determining the plurality of risk levels further comprises:
transmitting, to the remote subsystem, the monitoring data and an estimated risk level; and
receiving, from the remote subsystem, complementary information to refine and determine a combined risk level.
20 . The method of claim 11 , wherein transmitting the alert comprises notifying emergency services using traffic monitoring services as a function of the alert.