Anomalous Behavior Detection in Trajectory Data of Older Drivers

• URL: http://arxiv.org/abs/2311.17822v1
• Date: Wed, 29 Nov 2023 17:22:28 GMT
• Title: Anomalous Behavior Detection in Trajectory Data of Older Drivers
• Authors: Seyedeh Gol Ara Ghoreishi, Sonia Moshfeghi, Muhammad Tanveer Jan, Joshua Conniff, KwangSoo Yang, Jinwoo Jang, Borko Furht, Ruth Tappen, David Newman, Monica Rosselli, Jiannan Zhai
• Abstract summary: We propose an Edge-Attributed Matrix that can represent the key properties of temporally-detailed trajectory datasets. Experiments using real-world datasets demonstrated that our approach identifies abnormal driving behaviors.
• Score: 0.8426358786287627
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Given a road network and a set of trajectory data, the anomalous behavior detection (ABD) problem is to identify drivers that show significant directional deviations, hardbrakings, and accelerations in their trips. The ABD problem is important in many societal applications, including Mild Cognitive Impairment (MCI) detection and safe route recommendations for older drivers. The ABD problem is computationally challenging due to the large size of temporally-detailed trajectories dataset. In this paper, we propose an Edge-Attributed Matrix that can represent the key properties of temporally-detailed trajectory datasets and identify abnormal driving behaviors. Experiments using real-world datasets demonstrated that our approach identifies abnormal driving behaviors.

Related papers

• Exploiting T-norms for Deep Learning in Autonomous Driving [60.205021207641174]
  We show how it is possible to define memory-efficient t-norm-based losses, allowing for exploiting t-norms for the task of event detection in autonomous driving.
  arXiv  Detail & Related papers  (2024-02-17T18:51:21Z)
• G-MEMP: Gaze-Enhanced Multimodal Ego-Motion Prediction in Driving [71.9040410238973]
  We focus on inferring the ego trajectory of a driver's vehicle using their gaze data. Next, we develop G-MEMP, a novel multimodal ego-trajectory prediction network that combines GPS and video input with gaze data. The results show that G-MEMP significantly outperforms state-of-the-art methods in both benchmarks.
  arXiv  Detail & Related papers  (2023-12-13T23:06:30Z)
• 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)
• Detecting Socially Abnormal Highway Driving Behaviors via Recurrent Graph Attention Networks [4.526932450666445]
  This work focuses on detecting abnormal driving behaviors from trajectories produced by highway video surveillance systems. We propose an autoencoder with a Recurrent Graph Attention Network that can capture the highway driving behaviors contextualized on the surrounding cars. Our model is scalable to large freeways with thousands of cars.
  arXiv  Detail & Related papers  (2023-04-23T01:32:47Z)
• Anomaly Detection in Driving by Cluster Analysis Twice [0.0]
  This study proposes a method namely Anomaly Detection in Driving by Cluster Analysis Twice (ADDCAT) An event is said to be an anomaly if it never fits with the major cluster, which is considered as the pattern of normality in driving. This method provides a way to detect anomalies in driving with no prior training processes and huge computational costs needed.
  arXiv  Detail & Related papers  (2022-12-15T09:53:49Z)
• Driving Anomaly Detection Using Conditional Generative Adversarial Network [26.45460503638333]
  This study proposes an unsupervised method to quantify driving anomalies using a conditional generative adversarial network (GAN) The approach predicts upcoming driving scenarios by conditioning the models on the previously observed signals. The results are validated with perceptual evaluations, where annotators are asked to assess the risk and familiarity of the videos detected with high anomaly scores.
  arXiv  Detail & Related papers  (2022-03-15T22:10:01Z)
• CODA: A Real-World Road Corner Case Dataset for Object Detection in Autonomous Driving [117.87070488537334]
  We introduce a challenging dataset named CODA that exposes this critical problem of vision-based detectors. The performance of standard object detectors trained on large-scale autonomous driving datasets significantly drops to no more than 12.8% in mAR. We experiment with the state-of-the-art open-world object detector and find that it also fails to reliably identify the novel objects in CODA.
  arXiv  Detail & Related papers  (2022-03-15T08:32:56Z)
• 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)
• Graph Convolutional Networks for traffic anomaly [4.172516437934823]
  Event detection has been an important task in transportation, whose task is to detect points in time when large events disrupts a large portion of the urban traffic network. To fully capture the spatial and temporal traffic patterns remains a challenge, yet serves a crucial role for effective anomaly detection. We formulate the problem in a novel way, as detecting anomalies in a set of directed weighted graphs representing the traffic conditions at each time interval.
  arXiv  Detail & Related papers  (2020-12-25T22:36:22Z)
• Driver Anomaly Detection: A Dataset and Contrastive Learning Approach [17.020790792750457]
  We propose a contrastive learning approach to learn a metric to differentiate normal driving from anomalous driving. Our method reaches 0.9673 AUC on the test set, demonstrating the effectiveness of the contrastive learning approach on the anomaly detection task.
  arXiv  Detail & Related papers  (2020-09-30T13:23:21Z)
• SoDA: Multi-Object Tracking with Soft Data Association [75.39833486073597]
  Multi-object tracking (MOT) is a prerequisite for a safe deployment of self-driving cars. We propose a novel approach to MOT that uses attention to compute track embeddings that encode dependencies between observed objects.
  arXiv  Detail & Related papers  (2020-08-18T03:40:25Z)

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.