Panoptic Perception for Autonomous Driving: A Survey

TL;DR

This survey comprehensively reviews panoptic perception models in autonomous driving, highlighting their architectures, performance, challenges, and future research directions to guide researchers in the field.

Contribution

It provides a detailed overview and comparison of existing panoptic perception models, emphasizing their inputs, architectures, and performance metrics.

Findings

Panoptic perception models vary in input types and architectures.

Current models face challenges in real-time responsiveness and resource efficiency.

Future research should focus on improving model robustness and computational efficiency.

Abstract

Panoptic perception represents a forefront advancement in autonomous driving technology, unifying multiple perception tasks into a singular, cohesive framework to facilitate a thorough understanding of the vehicle's surroundings. This survey reviews typical panoptic perception models for their unique inputs and architectures and compares them to performance, responsiveness, and resource utilization. It also delves into the prevailing challenges faced in panoptic perception and explores potential trajectories for future research. Our goal is to furnish researchers in autonomous driving with a detailed synopsis of panoptic perception, positioning this survey as a pivotal reference in the ever-evolving landscape of autonomous driving technologies.