mlsanityData to 7 Oct 2026

Formalizing and Evaluating Requirements of Perception Systems for Automated Vehicles using Spatio-Temporal Perception Logic

arXiv 2206.14372 ↗First posted 29 Jun 2022cs.RO cs.CV cs.FLcited by 5

Not on the map. Why?

Abstract (arXiv)

Automated vehicles (AV) heavily depend on robust perception systems. Current methods for evaluating vision systems focus mainly on frame-by-frame performance. Such evaluation methods appear to be inadequate in assessing the performance of a perception subsystem when used within an AV. In this paper, we present a logic -- referred to as Spatio-Temporal Perception Logic (STPL) -- which utilizes both spatial and temporal modalities. STPL enables reasoning over perception data using spatial and temporal operators. One major advantage of STPL is that it facilitates basic sanity checks on the functional performance of the perception system, even without ground-truth data in some cases. We identify a fragment of STPL which is efficiently monitorable offline in polynomial time. Finally, we present a range of specifications for AV perception systems to highlight the types of requirements that can be expressed and analyzed through offline monitoring with STPL.

Key facts. Cited by 5 papers in the map (data through 7 Oct 2026). Builds on 21 of its 94 references that are in the map. First posted on arXiv 29 Jun 2022.

Builds on

21 of 94 references.

References · 21

  1. 2019nuScenes: A multimodal dataset for autonomous drivingcited by 4,485
  2. 2018BDD100K: A Diverse Driving Dataset for Heterogeneous Multitask Learningcited by 1,282
  3. 2019A Survey of Autonomous Driving: Common Practices and Emerging Technologiescited by 373
  4. 2017Playing for Benchmarkscited by 169
  5. 2020A Survey of Algorithms for Black-Box Safety Validation of Cyber-Physical Systemscited by 71
  6. 2016SqueezeDet: Unified, Small, Low Power Fully Convolutional Neural Networks for Real-Time Object Detection for Autonomous Drivingcited by 55
  7. 2020Formal Scenario-Based Testing of Autonomous Vehicles: From Simulation to the Real Worldcited by 38
  8. 2017Compositional Falsification of Cyber-Physical Systems with Machine Learning Componentscited by 31
  9. 2019Requirements-driven Test Generation for Autonomous Vehicles with Machine Learning Componentscited by 17
  10. 2021PerceMon: Online Monitoring for Perception Systemscited by 11
  11. 2020Monitoring and Diagnosability of Perception Systemscited by 7
  12. 2020DeepCrashTest: Turning Dashcam Videos into Virtual Crash Tests for Automated Driving Systemscited by 6
9 more
  1. 2020Predictive Monitoring with Logic-Calibrated Uncertainty for Cyber-Physical Systemscited by 6
  2. 2020VidCEP: Complex Event Processing Framework to Detect Spatiotemporal Patterns in Video Streamscited by 5
  3. 2018Spatial Logics and Model Checking for Medical Imaging (Extended Version)cited by 4
  4. 2014A Formal Methods Approach to Pattern Synthesis in Reaction Diffusion Systemscited by 3
  5. 2016A Survey of Qualitative Spatial and Temporal Calculi -- Algebraic and Computational Propertiescited by 3
  6. 2020A graph-based spatial temporal logic for knowledge representation and automated reasoning in cognitive robotscited by 3
  7. 2021Querying Labelled Data with Scenario Programs for Sim-to-Real Validationcited by 3
  8. 2016An Efficient Algorithm for Monitoring Practical TPTL Specificationscited by 2
  9. 2015Proof Theory of a Multi-Lane Spatial Logiccited by 1

Built on by

Cited by year (2026 to 7 Oct)

Citing papers per year: 2024: 1; 2025: 1; 2026: 3.

5 papers.