Probabilistic driving behavior modeling system for a vehicle

A method of determining a next state of a vehicle by a hybrid probabilistic driving behavior modeling system for a vehicle, the method comprising:

• determining, by a longitudinal driving model stored by one or more controllers, a probabilistic longitudinal velocity of the vehicle with respect to a current weather condition based on a car-following model, a semantic rule system, and a speed and visibility model, wherein the current weather condition indicates an adverse weather condition impacting driving conditions for the vehicle;
• determining, by a probabilistic lateral driving model stored by the one or more controllers, one or more lane choices for the vehicle with respect to the current weather condition based on a route plan of the vehicle and perception data indicative of an environment surrounding the vehicle;
• selecting, by the one or more controllers, a final lane choice from the one or more lane choices, wherein the final lane choice includes a maximum probability of being selected at the probabilistic longitudinal velocity when compared to remaining lane choices that are part of the one or more lane choices;
• determining a next state of the vehicle based on the final lane choice and the probabilistic longitudinal velocity;
• receiving a road segment that is part of the route plan, wherein the road segment includes an intersection;
• determining the vehicle is able to execute a maneuver at the intersection based on the perception data;
• and
• in response to determining the vehicle is able to execute the maneuver, instructing the vehicle to execute the maneuver by assisting with steering, braking, and accelerating.

A hybrid probabilistic driving behavior modeling system for a vehicle, the hybrid probabilistic driving behavior modeling system comprising:

• one or more controllers executing instructions to:
• determine, by a longitudinal driving model stored by the one or more controllers, a probabilistic longitudinal velocity of the vehicle with respect to a current weather condition based on a car-following model, a semantic rule system, and a speed and visibility model, wherein the current weather condition indicates an adverse weather condition impacting driving conditions for the vehicle, and wherein the probabilistic longitudinal velocity is determined by:
• determining, by the car-following model, a velocity of the vehicle by calculating a first maximum velocity and a second maximum velocity;
• selecting a minimum value between the first maximum velocity and the second maximum velocity;
• determining, by the semantic rule system, a constrained velocity that limits the velocity of the vehicle determined by the car-following model based on a plurality of high-level rules, wherein the plurality of high-level rules limit the velocity of the vehicle determined by the car-following model based on one or more driving conditions that are created by a specific adverse weather condition indicated by the current weather condition;
• and
• determining, by the speed and visibility model of the one or more controllers, the probabilistic longitudinal velocity of the vehicle with respect to the current weather condition by limiting the constrained velocity based on a visibility distance;
• determine, by a probabilistic lateral driving model stored by the one or more controllers, one or more lane choices for the vehicle with respect to the current weather condition based on a route plan of the vehicle and perception data indicative of an environment surrounding the vehicle;
• select a final lane choice from the one or more lane choices, wherein the final lane choice includes a maximum probability of being selected at the probabilistic longitudinal velocity when compared to remaining lane choices that are part of the one or more lane choices;
• determine a next state of the vehicle based on the final lane choice and the probabilistic longitudinal velocity;
• receive a road segment that is part of the route plan, wherein the road segment includes an intersection;
• determine the vehicle is able to execute a maneuver at the intersection based on the perception data;
• and
• in response to determining the vehicle is able to execute the maneuver, instruct the vehicle to execute the maneuver by assisting with steering, braking, and accelerating.