Transient Fault Tolerant Semantic Segmentation for Autonomous Driving

• URL: http://arxiv.org/abs/2408.16952v1
• Date: Fri, 30 Aug 2024 00:27:46 GMT
• Title: Transient Fault Tolerant Semantic Segmentation for Autonomous Driving
• Authors: Leonardo Iurada, Niccolò Cavagnero, Fernando Fernandes Dos Santos, Giuseppe Averta, Paolo Rech, Tatiana Tommasi,
• Abstract summary: We introduce ReLUMax, a simple activation function designed to enhance resilience against transient faults. Our experiments demonstrate that ReLUMax effectively improves robustness, preserving performance and boosting prediction confidence.
• Score: 44.725591200232884
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Deep learning models are crucial for autonomous vehicle perception, but their reliability is challenged by algorithmic limitations and hardware faults. We address the latter by examining fault-tolerance in semantic segmentation models. Using established hardware fault models, we evaluate existing hardening techniques both in terms of accuracy and uncertainty and introduce ReLUMax, a novel simple activation function designed to enhance resilience against transient faults. ReLUMax integrates seamlessly into existing architectures without time overhead. Our experiments demonstrate that ReLUMax effectively improves robustness, preserving performance and boosting prediction confidence, thus contributing to the development of reliable autonomous driving systems.

Related papers

• ReliOcc: Towards Reliable Semantic Occupancy Prediction via Uncertainty Learning [26.369237406972577]
  Vision-centric semantic occupancy prediction plays a crucial role in autonomous driving. There is still few research effort to explore the reliability in predicting semantic occupancy from camera. We propose ReliOcc, a method designed to enhance the reliability of camera-based occupancy networks.
  arXiv  Detail & Related papers  (2024-09-26T16:33:16Z)
• DRIVE: Dependable Robust Interpretable Visionary Ensemble Framework in Autonomous Driving [1.4104119587524289]
  Recent advancements in autonomous driving have seen a paradigm shift towards end-to-end learning paradigms. These models often sacrifice interpretability, posing significant challenges to trust, safety, and regulatory compliance. We introduce DRIVE, a comprehensive framework designed to improve the dependability and stability of explanations in end-to-end unsupervised driving models.
  arXiv  Detail & Related papers  (2024-09-16T14:40:47Z)
• Deep autoregressive density nets vs neural ensembles for model-based offline reinforcement learning [2.9158689853305693]
  We consider a model-based reinforcement learning algorithm that infers the system dynamics from the available data and performs policy optimization on imaginary model rollouts. This approach is vulnerable to exploiting model errors which can lead to catastrophic failures on the real system. We show that better performance can be obtained with a single well-calibrated autoregressive model on the D4RL benchmark.
  arXiv  Detail & Related papers  (2024-02-05T10:18:15Z)
• RACER: Rational Artificial Intelligence Car-following-model Enhanced by Reality [51.244807332133696]
  This paper introduces RACER, a cutting-edge deep learning car-following model to predict Adaptive Cruise Control (ACC) driving behavior. Unlike conventional models, RACER effectively integrates Rational Driving Constraints (RDCs), crucial tenets of actual driving. RACER excels across key metrics, such as acceleration, velocity, and spacing, registering zero violations.
  arXiv  Detail & Related papers  (2023-12-12T06:21:30Z)
• Towards Robust Continual Learning with Bayesian Adaptive Moment Regularization [51.34904967046097]
  Continual learning seeks to overcome the challenge of catastrophic forgetting, where a model forgets previously learnt information. We introduce a novel prior-based method that better constrains parameter growth, reducing catastrophic forgetting. Results show that BAdam achieves state-of-the-art performance for prior-based methods on challenging single-headed class-incremental experiments.
  arXiv  Detail & Related papers  (2023-09-15T17:10:51Z)
• Reliability in Semantic Segmentation: Are We on the Right Track? [15.0189654919665]
  We analyze a broad variety of models, spanning from older ResNet-based architectures to novel transformers. We find that while recent models are significantly more robust, they are not overall more reliable in terms of uncertainty estimation. This is the first study on modern segmentation models focused on both robustness and uncertainty estimation.
  arXiv  Detail & Related papers  (2023-03-20T17:38:24Z)
• Scalable Vehicle Re-Identification via Self-Supervision [66.2562538902156]
  Vehicle Re-Identification is one of the key elements in city-scale vehicle analytics systems. Many state-of-the-art solutions for vehicle re-id mostly focus on improving the accuracy on existing re-id benchmarks and often ignore computational complexity. We propose a simple yet effective hybrid solution empowered by self-supervised training which only uses a single network during inference time.
  arXiv  Detail & Related papers  (2022-05-16T12:14:42Z)
• On Efficient Uncertainty Estimation for Resource-Constrained Mobile Applications [0.0]
  Predictive uncertainty supplements model predictions and enables improved functionality of downstream tasks. We tackle this problem by building upon Monte Carlo Dropout (MCDO) models using the Axolotl framework. We conduct experiments on (1) a multi-class classification task using the CIFAR10 dataset, and (2) a more complex human body segmentation task.
  arXiv  Detail & Related papers  (2021-11-11T22:24:15Z)
• Efficient and Robust LiDAR-Based End-to-End Navigation [132.52661670308606]
  We present an efficient and robust LiDAR-based end-to-end navigation framework. We propose Fast-LiDARNet that is based on sparse convolution kernel optimization and hardware-aware model design. We then propose Hybrid Evidential Fusion that directly estimates the uncertainty of the prediction from only a single forward pass.
  arXiv  Detail & Related papers  (2021-05-20T17:52:37Z)
• Progressive Self-Guided Loss for Salient Object Detection [102.35488902433896]
  We present a progressive self-guided loss function to facilitate deep learning-based salient object detection in images. Our framework takes advantage of adaptively aggregated multi-scale features to locate and detect salient objects effectively.
  arXiv  Detail & Related papers  (2021-01-07T07:33:38Z)

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.