Right-of-way-based semantic coverage and automatic labeling for trajectory generation in autonomous systems

An apparatus comprising:

• one or more hardware processors to:
• receive a set of trajectories generated by at least one trajectory generation source, the set of trajectories generated for a scene having an autonomous vehicle (AV) interaction with a road agent;
• label a ground truth trajectory from the set of trajectories with a ground truth label;
• classify each remaining trajectory of the set of trajectories as at least one of an assert trajectory or a yield trajectory;
• for an assert group comprising the assert trajectories, assign an auxiliary assert label to an assert trajectory having a highest selection score from a trajectory selection source;
• for a yield group comprising the yield trajectories, assign an auxiliary yield label to a yield trajectory having a highest selection score from the trajectory selection source;
• and
• utilize the ground truth label, the auxiliary assert label, and the auxiliary yield label to train a trajectory generation model, wherein training of the trajectory generation model comprises using mode selectors corresponding to each label to provide secondary supervision trajectories when computing a loss for the trajectory generation model.

A non-transitory computer-readable medium having stored thereon instructions that, when executed by one or more processors, cause the one or more processors to:

• receive a set of trajectories generated by at least one trajectory generation source, the set of trajectories generated for a scene having an autonomous vehicle (AV) interaction with a road agent;
• label a ground truth trajectory from the set of trajectories with a ground truth label;
• classify each remaining trajectory of the set of trajectories as at least one of an assert trajectory or a yield trajectory;
• for an assert group comprising the assert trajectories, assign an auxiliary assert label to an assert trajectory having a highest selection score from a trajectory selection source;
• for a yield group comprising the yield trajectories, assign an auxiliary yield label to a yield trajectory having a highest selection score from the trajectory selection source;
• and
• utilize the ground truth label, the auxiliary assert label, and the auxiliary yield label to train a trajectory generation model, wherein training of the trajectory generation model comprises using mode selectors corresponding to each label to provide secondary supervision trajectories when computing a loss for the trajectory generation model.