Incorporating System-level Safety Requirements in Perception Models via Reinforcement Learning

• URL: http://arxiv.org/abs/2412.02951v1
• Date: Wed, 04 Dec 2024 01:40:54 GMT
• Title: Incorporating System-level Safety Requirements in Perception Models via Reinforcement Learning
• Authors: Weisi Fan, Jesse Lane, Qisai Liu, Soumik Sarkar, Tichakorn Wongpiromsarn,
• Abstract summary: We propose a training paradigm that augments the perception component with an understanding of system-level safety objectives. We show that models trained with this approach outperform baseline perception models in terms of system-level safety.
• Score: 7.833541053347799
• License:
• Abstract: Perception components in autonomous systems are often developed and optimized independently of downstream decision-making and control components, relying on established performance metrics like accuracy, precision, and recall. Traditional loss functions, such as cross-entropy loss and negative log-likelihood, focus on reducing misclassification errors but fail to consider their impact on system-level safety, overlooking the varying severities of system-level failures caused by these errors. To address this limitation, we propose a novel training paradigm that augments the perception component with an understanding of system-level safety objectives. Central to our approach is the translation of system-level safety requirements, formally specified using the rulebook formalism, into safety scores. These scores are then incorporated into the reward function of a reinforcement learning framework for fine-tuning perception models with system-level safety objectives. Simulation results demonstrate that models trained with this approach outperform baseline perception models in terms of system-level safety.

Related papers

• Towards a Framework for Deep Learning Certification in Safety-Critical Applications Using Inherently Safe Design and Run-Time Error Detection [0.0]
  We consider real-world problems arising in aviation and other safety-critical areas, and investigate their requirements for a certified model. We establish a new framework towards deep learning certification based on (i) inherently safe design, and (ii) run-time error detection.
  arXiv  Detail & Related papers  (2024-03-12T11:38:45Z)
• Enhancing Security in Federated Learning through Adaptive Consensus-Based Model Update Validation [2.28438857884398]
  This paper introduces an advanced approach for fortifying Federated Learning (FL) systems against label-flipping attacks. We propose a consensus-based verification process integrated with an adaptive thresholding mechanism. Our results indicate a significant mitigation of label-flipping attacks, bolstering the FL system's resilience.
  arXiv  Detail & Related papers  (2024-03-05T20:54:56Z)
• Analyzing Adversarial Inputs in Deep Reinforcement Learning [53.3760591018817]
  We present a comprehensive analysis of the characterization of adversarial inputs, through the lens of formal verification. We introduce a novel metric, the Adversarial Rate, to classify models based on their susceptibility to such perturbations. Our analysis empirically demonstrates how adversarial inputs can affect the safety of a given DRL system with respect to such perturbations.
  arXiv  Detail & Related papers  (2024-02-07T21:58:40Z)
• STEAM & MoSAFE: SOTIF Error-and-Failure Model & Analysis for AI-Enabled Driving Automation [4.820785104084241]
  This paper defines the SOTIF Temporal Error and Failure Model (STEAM) as a refinement of the SOTIF cause-and-effect model. Second, this paper proposes the Model-based SOTIF Analysis of Failures and Errors (MoSAFE) method, which allows instantiating STEAM based on system-design models.
  arXiv  Detail & Related papers  (2023-12-15T06:34:35Z)
• Recursively Feasible Probabilistic Safe Online Learning with Control Barrier Functions [60.26921219698514]
  We introduce a model-uncertainty-aware reformulation of CBF-based safety-critical controllers. We then present the pointwise feasibility conditions of the resulting safety controller. We use these conditions to devise an event-triggered online data collection strategy.
  arXiv  Detail & Related papers  (2022-08-23T05:02:09Z)
• Risk-Driven Design of Perception Systems [47.787943101699966]
  It is important that we design perception systems to minimize errors that reduce the overall safety of the system. We develop a risk-driven approach to designing perception systems that accounts for the effect of perceptual errors on the performance of the fully-integrated, closed-loop system. We evaluate our techniques on a realistic vision-based aircraft detect and avoid application and show that risk-driven design reduces collision risk by 37% over a baseline system.
  arXiv  Detail & Related papers  (2022-05-21T21:14:56Z)
• Joint Differentiable Optimization and Verification for Certified Reinforcement Learning [91.93635157885055]
  In model-based reinforcement learning for safety-critical control systems, it is important to formally certify system properties. We propose a framework that jointly conducts reinforcement learning and formal verification.
  arXiv  Detail & Related papers  (2022-01-28T16:53:56Z)
• Reliability Assessment and Safety Arguments for Machine Learning Components in Assuring Learning-Enabled Autonomous Systems [19.65793237440738]
  We present an overall assurance framework for Learning-Enabled Systems (LES) We then introduce a novel model-agnostic Reliability Assessment Model (RAM) for ML classifiers. We discuss the model assumptions and the inherent challenges of assessing ML reliability uncovered by our RAM.
  arXiv  Detail & Related papers  (2021-11-30T14:39:22Z)
• SafeAMC: Adversarial training for robust modulation recognition models [53.391095789289736]
  In communication systems, there are many tasks, like modulation recognition, which rely on Deep Neural Networks (DNNs) models. These models have been shown to be susceptible to adversarial perturbations, namely imperceptible additive noise crafted to induce misclassification. We propose to use adversarial training, which consists of fine-tuning the model with adversarial perturbations, to increase the robustness of automatic modulation recognition models.
  arXiv  Detail & Related papers  (2021-05-28T11:29:04Z)
• Evaluating the Safety of Deep Reinforcement Learning Models using Semi-Formal Verification [81.32981236437395]
  We present a semi-formal verification approach for decision-making tasks based on interval analysis. Our method obtains comparable results over standard benchmarks with respect to formal verifiers. Our approach allows to efficiently evaluate safety properties for decision-making models in practical applications.
  arXiv  Detail & Related papers  (2020-10-19T11:18:06Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.