mlsanityData to 7 Oct 2026

SOTIF Entropy: Online SOTIF Risk Quantification and Mitigation for Autonomous Driving

arXiv 2211.04009 ↗First posted 8 Nov 2022cs.AIcited by 5

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Abstract (arXiv)

Autonomous driving confronts great challenges in complex traffic scenarios, where the risk of Safety of the Intended Functionality (SOTIF) can be triggered by the dynamic operational environment and system insufficiencies. The SOTIF risk is reflected not only intuitively in the collision risk with objects outside the autonomous vehicles (AVs), but also inherently in the performance limitation risk of the implemented algorithms themselves. How to minimize the SOTIF risk for autonomous driving is currently a critical, difficult, and unresolved issue. Therefore, this paper proposes the "Self-Surveillance and Self-Adaption System" as a systematic approach to online minimize the SOTIF risk, which aims to provide a systematic solution for monitoring, quantification, and mitigation of inherent and external risks. The core of this system is the risk monitoring of the implemented artificial intelligence algorithms within the AV. As a demonstration of the Self-Surveillance and Self-Adaption System, the risk monitoring of the perception algorithm, i.e., YOLOv5 is highlighted. Moreover, the inherent perception algorithm risk and external collision risk are jointly quantified via SOTIF entropy, which is then propagated downstream to the decision-making module and mitigated. Finally, several challenging scenarios are demonstrated, and the Hardware-in-the-Loop experiments are conducted to verify the efficiency and effectiveness of the system. The results demonstrate that the Self-Surveillance and Self-Adaption System enables dependable online monitoring, quantification, and mitigation of SOTIF risk in real-time critical traffic environments.

Key facts. Cited by 5 papers in the map (data through 7 Oct 2026). Builds on 26 of its 73 references that are in the map. First posted on arXiv 8 Nov 2022.

Builds on

26 of 73 references.

References · 26

  1. 2015Dropout as a Bayesian Approximation: Representing Model Uncertainty in Deep Learningcited by 4,416
  2. 2016Simple and Scalable Predictive Uncertainty Estimation using Deep Ensemblescited by 3,888
  3. 2017What Uncertainties Do We Need in Bayesian Deep Learning for Computer Vision?cited by 2,335
  4. 2019Deep Evidential Regressioncited by 354
  5. 2014Stochastic Gradient Hamiltonian Monte Carlocited by 341
  6. 2017Concrete Dropoutcited by 227
  7. 2015A Theoretically Grounded Application of Dropout in Recurrent Neural Networkscited by 161
  8. 2018Evaluating Bayesian Deep Learning Methods for Semantic Segmentationcited by 143
  9. 2020A Review and Comparative Study on Probabilistic Object Detection in Autonomous Drivingcited by 115
  10. 2019Gaussian YOLOv3: An Accurate and Fast Object Detector Using Localization Uncertainty for Autonomous Drivingcited by 105
  11. 2022CODA: A Real-World Road Corner Case Dataset for Object Detection in Autonomous Drivingcited by 100
  12. 2017Uncertainty-Aware Reinforcement Learning for Collision Avoidancecited by 86
14 more
  1. 2018Lightweight Probabilistic Deep Networkscited by 77
  2. 2019Sampling-free Epistemic Uncertainty Estimation Using Approximated Variance Propagationcited by 74
  3. 2018Safe Reinforcement Learning with Model Uncertainty Estimatescited by 70
  4. 2019BayesOD: A Bayesian Approach for Uncertainty Estimation in Deep Object Detectorscited by 68
  5. 2018Evaluating Merging Strategies for Sampling-based Uncertainty Techniques in Object Detectioncited by 51
  6. 2015Bootstrapped Thompson Sampling and Deep Explorationcited by 40
  7. 2017Uncertainty-Aware Learning from Demonstration using Mixture Density Networks with Sampling-Free Variance Modelingcited by 25
  8. 2021Heterogeneous-Agent Trajectory Forecasting Incorporating Class Uncertaintycited by 19
  9. 2019Towards a Framework to Manage Perceptual Uncertainty for Safe Automated Drivingcited by 12
  10. 2020Stochastic-YOLO: Efficient Probabilistic Object Detection under Dataset Shiftscited by 9
  11. 2022PeSOTIF: a Challenging Visual Dataset for Perception SOTIF Problems in Long-tail Traffic Scenarioscited by 8
  12. 2021Motion Planning for Autonomous Vehicles in the Presence of Uncertainty Using Reinforcement Learningcited by 6
  13. 2020Road Damage Detection using Deep Ensemble Learningcited by 5
  14. 2019Dirichlet uncertainty wrappers for actionable algorithm accuracy accountability and auditabilitycited by 3

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Cited by year

Citing papers per year: 2024: 1; 2025: 4.

5 papers.

  1. 2025Reinforced Refinement with Self-Aware Expansion for End-to-End Autonomous Drivingcited by 19
  2. 2025DriveSOTIF: Advancing Perception SOTIF Through Multimodal Large Language Modelscited by 4
  3. 2024Are you a robot? Detecting Autonomous Vehicles from Behavior Analysiscited by 2
  4. 2025Stereo-based 3D Anomaly Object Detection for Autonomous Driving: A New Dataset and Baselinecited by 2
  5. 2025A Systematic Literature Review on Safety of the Intended Functionality for Automated Driving Systemscited by 0