Apparatuses, systems, and methods for vehicle trajectory and collision prediction

1 . An apparatus for trajectory and collision prediction comprising a processor operable to:

generate, using a machine-learning (ML) model, a ML predicted trajectory of a vehicle and a ML model confidence of the ML predicted trajectory;

generate, using a heuristics model, a heuristic predicted trajectory of the vehicle;

determine whether the ML model confidence satisfies a metric;

in response to determining that the ML model confidence satisfies the metric, apply the ML predicted trajectory as a predicted trajectory;

in response to determining that the ML learning model confidence fails the metric, apply the heuristic predicted trajectory as the predicted trajectory;

generate collision confidence associated with the predicted trajectory, wherein the collision confidence comprises a blending collision confidence generated by blending a ML collision confidence and a heuristic collision confidence using weights based on the ML model confidence;

determine whether the collision confidence is beyond a collision threshold, and

in response to determining that the collision confidence is beyond the collision threshold, generate a trajectory planning and operate the vehicle based on the trajectory planning.

2 . The apparatus of claim 1 , wherein the processor is operable to further:

in response to determining that the collision confidence is beyond the collision threshold, inform the vehicle of a predicted collision.

3 . The apparatus of claim 2 , wherein the processor is operable to further instruct the vehicle to follow the trajectory planning.

4 . The apparatus of claim 1 , wherein the ML collision confidence is generated based on the ML model in response to determining that the ML model confidence satisfies the metric.

5 . The apparatus of claim 1 , wherein the heuristic collision confidence is generated based on the heuristic model in response to determining that the ML model confidence fails the metric.

6 . The apparatus of claim 1 , wherein the blending collision confidence is determined based on following equation:

blending collision confidence=ML model confidence×ML collision confidence+(1−ML model confidence)×heuristic collision confidence.

7 . The apparatus of claim 1 , wherein the ML model confidence is determined based on a probabilistic model or a distance-based model.

8 . The apparatus of claim 1 , wherein the ML predicted trajectory and the heuristic predicted trajectory are generated based on vehicle kinematics, vehicle controls, and road conditions.

9 . The apparatus of claim 8 , wherein the vehicle kinematics comprise a vehicle position, a vehicle velocity, and a vehicle acceleration.

10 . The apparatus of claim 8 , wherein the vehicle controls comprise vehicle steering, vehicle throttle, and brake inputs.

11 . The apparatus of claim 8 , wherein the road conditions comprise road surface conditions, road geometry, and traffic conditions.

12 . The apparatus of claim 8 , wherein the apparatus further comprises one or more vision sensors and speed sensors configured to collect information of the vehicle kinematics, the vehicle controls, and the road conditions.

13 . A method for trajectory and collision prediction comprising:

generating, using a machine-learning (ML) model, a ML predicted trajectory of a vehicle and a ML model confidence of the ML predicted trajectory;

generating, using a heuristics model, a heuristic predicted trajectory of the vehicle;

determining whether the ML model confidence satisfies a metric;

in response to determining that the ML model confidence satisfies the metric, applying the ML predicted trajectory as a predicted trajectory;

in response to determining that the ML learning model confidence fails the metric, applying the heuristic predicted trajectory as the predicted trajectory;

generating collision confidence associated with the predicted trajectory, wherein the collision confidence comprises a blending collision confidence generated by blending a ML collision confidence and a heuristic collision confidence using weights based on the ML model confidence;

determining whether the collision confidence is beyond a collision threshold, and

in response to determining that the collision confidence is beyond the collision threshold, generating a trajectory planning and operating the vehicle based on the trajectory planning.

14 . The method of claim 13 , wherein the method further comprises:

in response to determining that the collision confidence is beyond the collision threshold, informing the vehicle of a predicted collision, and instruct the vehicle to follow the trajectory planning.

15 . The method of claim 13 , wherein:

the ML collision confidence is generated based on the ML model in response to determining that the ML model confidence satisfies the metric; and

the heuristic collision confidence is generated based on the heuristic model in response to determining that the ML model confidence fails the metric.

16 . The method of claim 13 , wherein the blending collision confidence is determined based on following equation:

blending collision confidence=ML model confidence×ML collision confidence+(1−ML model confidence)×heuristic collision confidence.

17 . The method of claim 13 , wherein the ML model confidence is determined based on a probabilistic model or a distance-based model.

18 . The method of claim 13 , wherein the ML predicted trajectory and the heuristic predicted trajectory are generated based on vehicle kinematics, vehicle controls, and road conditions, wherein:

the vehicle kinematics comprise a vehicle position, a vehicle velocity, and a vehicle acceleration;

the vehicle controls comprise vehicle steering, vehicle throttle, and brake inputs;

the road conditions comprise road surface conditions, road geometry, and traffic conditions; and

information of the vehicle kinematics, the vehicle controls, and the road conditions are collected by one or more vision sensors and speed sensors.