Method and a system of determining a trajectory for an autonomous vehicle

A system for planning motion of a Self-Driving Car (SDC), the SDC having a plurality of sensors configured to generate sensed data representative of surroundings of the SDC, the system comprising at least one processor communicatively coupled to the SDC and configured to execute a plurality of Machine-Learning (ML) models for detecting objects in the surroundings of the SDC, the plurality of ML models being sequentially connected therebetween, such that an output of a first ML model of the plurality of ML models is used as an input to a second ML model, sequentially following after the first ML model in the plurality of ML models,

• each one of the plurality of ML models having been trained to detect the objects in the surroundings of the SDC based on the sensed data representative thereof;
• a given sequential ML model being configured to detect the objects with a respective value of an object detection precision metric,
• the respective value of the object detection precision metric associated with the given sequential ML model being higher than any one of those that are associated with ML models of the plurality of ML models preceding the given sequential ML model;
• the system further comprising at least one non-transitory computer-readable memory comprising executable instructions that, when executed by the at least one processor, cause the system to:
• receive the sensed data representative of the surroundings of the SDC;
• feed the sensed data to the plurality of the ML models, by:
• feeding the sensed data to the first ML model, thereby causing the first ML model to generate a first prediction of (i) an object class of at least one object in the surroundings of the SDC and (ii) a location of the at least one object;
• and
• in response to a first time for generating the first prediction by the first ML model being higher than a first predetermined time period threshold assigned to the first ML model for generating the first prediction:
• planning the motion of the SDC based on the first prediction;
• and
• in response to the first time for generating the first prediction by the first ML model being equal to or lower than the first predetermined time period threshold:
• feeding the first prediction along with the sensed data to the second ML model, thereby causing the second ML model to generate a second prediction of (i) the object class of the at least one object in the surroundings of the SDC and (ii) the location of the at least one object;
• and
• planning the motion of the SDC based on the second prediction.