Self-awareness in intelligent vehicles: Feature based dynamic Bayesian models for abnormality detection

• URL: http://arxiv.org/abs/2010.15441v1
• Date: Thu, 29 Oct 2020 09:29:47 GMT
• Title: Self-awareness in intelligent vehicles: Feature based dynamic Bayesian models for abnormality detection
• Authors: Divya Thekke Kanapram, Pablo Marin-Plaza, Lucio Marcenaro, David Martin, Arturo de la Escalera and Carlo Regazzoni
• Abstract summary: This paper aims to introduce a novel method to develop self-awareness in autonomous vehicles. Time-series data from the vehicles are used to develop the data-driven Dynamic Bayesian Network (DBN) models. An initial level collective awareness model that can perform joint anomaly detection in co-operative tasks is proposed.
• Score: 4.251384905163326
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: The evolution of Intelligent Transportation Systems in recent times necessitates the development of self-awareness in agents. Before the intensive use of Machine Learning, the detection of abnormalities was manually programmed by checking every variable and creating huge nested conditions that are very difficult to track. This paper aims to introduce a novel method to develop self-awareness in autonomous vehicles that mainly focuses on detecting abnormal situations around the considered agents. Multi-sensory time-series data from the vehicles are used to develop the data-driven Dynamic Bayesian Network (DBN) models used for future state prediction and the detection of dynamic abnormalities. Moreover, an initial level collective awareness model that can perform joint anomaly detection in co-operative tasks is proposed. The GNG algorithm learns the DBN models' discrete node variables; probabilistic transition links connect the node variables. A Markov Jump Particle Filter (MJPF) is applied to predict future states and detect when the vehicle is potentially misbehaving using learned DBNs as filter parameters. In this paper, datasets from real experiments of autonomous vehicles performing various tasks used to learn and test a set of switching DBN models.

Related papers

• AIDE: An Automatic Data Engine for Object Detection in Autonomous Driving [68.73885845181242]
  We propose an Automatic Data Engine (AIDE) that automatically identifies issues, efficiently curates data, improves the model through auto-labeling, and verifies the model through generation of diverse scenarios. We further establish a benchmark for open-world detection on AV datasets to comprehensively evaluate various learning paradigms, demonstrating our method's superior performance at a reduced cost.
  arXiv  Detail & Related papers  (2024-03-26T04:27:56Z)
• Unsupervised Domain Adaptation for Self-Driving from Past Traversal Features [69.47588461101925]
  We propose a method to adapt 3D object detectors to new driving environments. Our approach enhances LiDAR-based detection models using spatial quantized historical features. Experiments on real-world datasets demonstrate significant improvements.
  arXiv  Detail & Related papers  (2023-09-21T15:00:31Z)
• Anomaly Detection in Multi-Agent Trajectories for Automated Driving [2.5211566369910967]
  Similar to humans, automated vehicles are supposed to perform anomaly detection. Our innovation is the ability to jointly learn multiple trajectories of a dynamic number of agents.
  arXiv  Detail & Related papers  (2021-10-15T08:07:31Z)
• DAE : Discriminatory Auto-Encoder for multivariate time-series anomaly detection in air transportation [68.8204255655161]
  We propose a novel anomaly detection model called Discriminatory Auto-Encoder (DAE) It uses the baseline of a regular LSTM-based auto-encoder but with several decoders, each getting data of a specific flight phase. Results show that the DAE achieves better results in both accuracy and speed of detection.
  arXiv  Detail & Related papers  (2021-09-08T14:07:55Z)
• Improving Variational Autoencoder based Out-of-Distribution Detection for Embedded Real-time Applications [2.9327503320877457]
  Out-of-distribution (OD) detection is an emerging approach to address the challenge of detecting out-of-distribution in real-time. In this paper, we show how we can robustly detect hazardous motion around autonomous driving agents. Our methods significantly improve detection capabilities of OoD factors to unique driving scenarios, 42% better than state-of-the-art approaches. Our model also generalized near-perfectly, 97% better than the state-of-the-art across the real-world and simulation driving data sets experimented.
  arXiv  Detail & Related papers  (2021-07-25T07:52:53Z)
• A Driving Behavior Recognition Model with Bi-LSTM and Multi-Scale CNN [59.57221522897815]
  We propose a neural network model based on trajectories information for driving behavior recognition. We evaluate the proposed model on the public BLVD dataset, achieving a satisfying performance.
  arXiv  Detail & Related papers  (2021-03-01T06:47:29Z)
• A Novel Anomaly Detection Algorithm for Hybrid Production Systems based on Deep Learning and Timed Automata [73.38551379469533]
  DAD:DeepAnomalyDetection is a new approach for automatic model learning and anomaly detection in hybrid production systems. It combines deep learning and timed automata for creating behavioral model from observations. The algorithm has been applied to few data sets including two from real systems and has shown promising results.
  arXiv  Detail & Related papers  (2020-10-29T08:27:43Z)
• Self-awareness in Intelligent Vehicles: Experience Based Abnormality Detection [4.721146043492144]
  This paper introduces a novel method to detect abnormalities based on internal cross-correlation parameters of the vehicle. It is possible to train a Dynamic Bayesian Network (DBN) model to automatically evaluate and detect when the vehicle is potentially misbehaving.
  arXiv  Detail & Related papers  (2020-10-28T16:08:54Z)
• Collective Awareness for Abnormality Detection in Connected Autonomous Vehicles [4.659696262995864]
  This article presents a novel approach to develop an initial level of collective awareness in a network of intelligent agents. A specific collective self awareness functionality is considered, namely, agent centered detection of abnormal situations. The impact is also evaluated by the communication channel used by the network to share the data sensed in a distributed way by each agent of the network.
  arXiv  Detail & Related papers  (2020-10-28T12:11:36Z)
• Unsupervised Anomaly Detection with Adversarial Mirrored AutoEncoders [51.691585766702744]
  We propose a variant of Adversarial Autoencoder which uses a mirrored Wasserstein loss in the discriminator to enforce better semantic-level reconstruction. We put forward an alternative measure of anomaly score to replace the reconstruction-based metric. Our method outperforms the current state-of-the-art methods for anomaly detection on several OOD detection benchmarks.
  arXiv  Detail & Related papers  (2020-03-24T08:26:58Z)

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.