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Panoptic Perception for Autonomous Driving: A Survey

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arxiv 2408.15388 v1 pith:VD5CC7CM submitted 2024-08-27 cs.RO cs.CVcs.LG

Yunge Li , Lanyu Xu This is my paper · ORCID
classification cs.ROcs.CVcs.LG
keywords perceptionpanopticautonomousdrivingsurveyadvancementarchitectureschallenges
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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.

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Cited by 3 Pith papers

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score. Full citation record

  1. Real-World Perturbation Testing of Autonomous Driving Systems

    cs.SE 2026-07 conditional novelty 7.0 of 10

    Model-level and offline robustness metrics for 72 camera/LiDAR perturbations do not reliably predict closed-loop failures on a full-scale autonomous vehicle.

  2. Benchmarking Image Perturbations for Testing Automated Driving Assistance Systems

    cs.SE 2025-01 reject novelty 6.0 of 10

    A benchmark of 32 image perturbations on two ADAS shows most corruptions cause failures, and retraining on perturbed data improves robustness to simulated weather, but the retraining effect is confounded by new road data.

  3. OpenCat: Improving Interoperability of ADS Testing

    cs.SE 2025-02 conditional novelty 4.0 of 10

    OpenCat converts OpenDRIVE roads to Catmull-Rom splines; re-running SensoDat in Udacity with Dave-2 raises the pass rate from 61% to 98%, suggesting the benchmark is coupled to its original ADAS and simulator.

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