VEHICLE COLLISION ALERT SYSTEM AND METHOD FOR DIRECTING COLLISION AVOIDANCE ACTION

1 . An impairment analysis (IA) computer system for detecting a driver or vehicle impairment, the IA computer system associated with a host vehicle, the IA computer system comprising a plurality of sensors, and at least one processor in communication with the plurality of sensors and at least one memory device, the at least one processor is programmed to:

interrogate a target vehicle via the plurality of sensors by scanning at least one of the target vehicle and a target driver of the target vehicle;

receive sensor data including at least one of target driver data and target vehicle condition data;

analyze the sensor data to determine whether at least one of lane drift and vehicle speed deviation for the target vehicle exceeds a respective threshold by applying a baseline model to the sensor data, wherein the baseline model includes baseline conditions for at least one of lane position and speed;

detect an impairment of at least one of the target driver and the target vehicle based upon the analysis;

transmit the detected impairment of the at least one of the target driver and the target vehicle to a remote-computing device to update an insurance policy of an insurance holder; and

direct collision avoidance action based upon the detection.

2 . The system of claim 1 , wherein the at least one processor is further programmed to store the respective threshold of the at least one of lane drift and vehicle speed deviation in the at least one memory device, and wherein the respective threshold is based upon safe driving parameters associated with the sensor data received by the plurality of sensors.

3 . The system of claim 1 , wherein the at least one processor is further programmed to direct the collision avoidance action by generating an alert at the host vehicle, and wherein the alert is at least one of an auditory alert, a visual alert, and a haptic alert.

4 . The system of claim 1 , wherein the at least one processor is further programmed to direct the collision avoidance action by automatically engaging an automated safety system of the host vehicle, and wherein the automated safety system is at least one of an automated braking system and an automated steering system.

5 . The system of claim 1 , wherein the at least one processor is further programmed to direct the collision avoidance action by generating a recommendation at the host vehicle to engage an automated safety system of the host vehicle, wherein engaging the automated safety system reduces a probability of colliding with the target vehicle.

6 . The system of claim 1 , wherein the at least one processor is further programmed to direct the collision avoidance action by automatically engaging at least one of an autonomous vehicle control system and semi-autonomous vehicle control system of the host vehicle.

7 . The system of claim 1 , wherein the at least one processor is further programmed to direct the collision avoidance action by generating a recommendation at the host vehicle to engage at least one of an autonomous vehicle control system and a semi-autonomous vehicle control system of the host vehicle.

8 . The system of claim 1 , wherein the host vehicle operates in at least one of an autonomous control mode and a semi-autonomous control mode, and wherein the at least one processor is further programmed to direct the collision avoidance action by directing a vehicle control system of the host vehicle to automatically steer the host vehicle away from a path of the target vehicle.

9 . A computer-implemented method for detecting an impairment, the method implemented using an impairment analysis (IA) computing device associated with a host vehicle, the IA computing device including at least one processor in communication with at least one memory device, the method comprising:

interrogating, by the IA computing device, a target vehicle by using a plurality of sensors included on the host vehicle to scan at least one of the target vehicle and a target driver;

receiving, by the IA computing device, sensor data including at least one of target driver data and target vehicle condition data;

analyzing, by the IA computing device, the sensor data to detect at least one of lane departure and speed deviation for the target vehicle by applying a baseline model to the sensor data, wherein the baseline model includes baseline conditions for at least one of lane position and speed;

determining, by the IA computing device, that the at least one of lane departure and speed deviation for the target vehicle is above a respective predetermined threshold;

transmitting the determined at least one of lane departure and speed deviation to a remote-computing device to update an insurance policy of an insurance holder; and

directing, by the IA computing device, collision avoidance action based upon the determination.

10 . The computer-implemented method of claim 9 , wherein the respective predetermined threshold of the at least one of lane departure and speed deviation for the target vehicle is stored in the at least one memory device, and wherein the respective predetermined threshold is based upon safe driving parameters associated with the sensor data received by the plurality of sensors.

11 . The computer-implemented method of claim 9 , wherein directing, by the IA computing device, the collision avoidance action comprises generating an alert at the host vehicle, and wherein the alert is at least one of an auditory alert, a visual alert, and a haptic alert.

12 . The computer-implemented method of claim 9 , wherein directing, by the IA computing device, the collision avoidance action comprises automatically engaging an automated safety system of the host vehicle, and wherein the automated safety system is at least one of an automated braking system and an automated steering system.

13 . The computer-implemented method of claim 9 , wherein directing, by the IA computing device, the collision avoidance action comprises generating a recommendation at the host vehicle to engage an automated safety system of the host vehicle, and wherein engaging the automated safety system reduces a probability of colliding with the target vehicle.

14 . The computer-implemented method of claim 9 , wherein directing, by the IA computing device, the collision avoidance action comprises automatically engaging at least one of an autonomous vehicle control system and a semi-autonomous vehicle control system of the host vehicle.

15 . The computer-implemented method of claim 9 , wherein directing, by the IA computing device, the collision avoidance action comprises generating a recommendation at the host vehicle to engage in at least one of an autonomous vehicle control system and a semi-autonomous vehicle control system.

16 . The computer-implemented method of claim 9 wherein directing, by the IA computing device, the collision avoidance action comprises:

generating, by the IA computing device, a recommendation to engage an automated safety system of the target vehicle, and wherein the target vehicle is capable of receiving the recommendation from the IA computing device; and

transmitting the recommendation to the target vehicle.

17 . The computer-implemented method of claim 9 , wherein the host vehicle operates in at least one of an autonomous control mode and a semi-autonomous control mode, and wherein directing, by the IA computing device, the collision avoidance action comprises prompting a vehicle control system of the host vehicle to automatically steer the host vehicle away from a path of the target vehicle.

18 . An impairment analysis (IA) computer system for alerting a target driver of a target vehicle to a driving hazard posed by a host vehicle operated by a host driver, the IA computer system associated with the host vehicle, the IA computer system including at least one processor in communication with at least one memory device, the at least one processor is programmed to:

gather sensor data at the host vehicle, wherein the sensor data includes data associated with the host vehicle and the host driver, and wherein the sensor data is collected by a plurality of sensors included on the host vehicle;

analyze the sensor data by applying a baseline model to the sensor data by applying a baseline model to the sensor data, wherein the baseline model includes baseline conditions for at least one of lane position and speed;

determine, based upon the analysis, that the host vehicle poses a risk to the target vehicle; and

output an alert message to the target vehicle, wherein the alert message includes sensor data enabling the target vehicle to determine corrective action.