METEOR: A Dense, Heterogeneous, and Unstructured Traffic Dataset with Rare Behaviors

Chandra, Rohan; Wang, Xijun; Mahajan, Mridul; Kala, Rahul; Palugulla, Rishitha; Naidu, Chandrababu; Jain, Alok; Manocha, Dinesh

doi:10.1109/icra48891.2023.10161281

Cited by 4 publications

(2 citation statements)

References 15 publications

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“…As shown in the figure, our proposed model was trained on the Meteor dataset [27]. Synchronized videos for inside and outside the cabin collected from several drivers [28] were used to check the relation between the driver state and the detected event.…”

Section: Correlation Analysis Between External Events and The Driver ...mentioning

confidence: 99%

“…We used the METEOR dataset [27], which consists of 1250 one-minute videos. The dataset contains frame-wise annotations for agents and their behaviours on the road in diverse traffic scenarios including rainy weather, nighttime driving, driving in rural areas with unmarked roads, and high-density traffic scenarios.…”

Section: Used Dataset For External Events Classificationmentioning

confidence: 99%

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A Machine Learning-Based Correlation Analysis between Driver Behaviour and Vital Signs: Approach and Case Study

Othman,

Hamoud,

Kashevnik

et al. 2023

Sensors

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Driving behaviour analysis has drawn much attention in recent years due to the dramatic increase in the number of traffic accidents and casualties, and based on many studies, there is a relationship between the driving environment or behaviour and the driver’s state. To the best of our knowledge, these studies mostly investigate relationships between one vital sign and the driving circumstances either inside or outside the cabin. Hence, our paper provides an analysis of the correlation between the driver state (vital signs, eye state, and head pose) and both the vehicle maneuver actions (caused by the driver) and external events (carried out by other vehicles or pedestrians), including the proximity to other vehicles. Our methodology employs several models developed in our previous work to estimate respiratory rate, heart rate, blood pressure, oxygen saturation, head pose, eye state from in-cabin videos, and the distance to the nearest vehicle from out-cabin videos. Additionally, new models have been developed using Convolutional Neural Network (CNN) and Bidirectional Long Short-Term Memory (BiLSTM) to classify the external events from out-cabin videos, as well as a Decision Tree classifier to detect the driver’s maneuver using accelerometer and gyroscope sensor data. The dataset used includes synchronized in-cabin/out-cabin videos and sensor data, allowing for the estimation of the driver state, proximity to other vehicles and detection of external events, and driver maneuvers. Therefore, the correlation matrix was calculated between all variables to be analysed. The results indicate that there is a weak correlation connecting both the maneuver action and the overtaking external event on one side and the heart rate and the blood pressure (systolic and diastolic) on the other side. In addition, the findings suggest a correlation between the yaw angle of the head and the overtaking event and a negative correlation between the systolic blood pressure and the distance to the nearest vehicle. Our findings align with our initial hypotheses, particularly concerning the impact of performing a maneuver or experiencing a cautious event, such as overtaking, on heart rate and blood pressure due to the agitation and tension resulting from such events. These results can be the key to implementing a sophisticated safety system aimed at maintaining the driver’s stable state when aggressive external events or maneuvers occur.

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Section: Correlation Analysis Between External Events and The Driver ...mentioning

confidence: 99%

Section: Used Dataset For External Events Classificationmentioning

confidence: 99%

A Machine Learning-Based Correlation Analysis between Driver Behaviour and Vital Signs: Approach and Case Study

Othman,

Hamoud,

Kashevnik

et al. 2023

Sensors

View full text Add to dashboard Cite

show abstract

Driving Through Graphs: a Bipartite Graph for Traffic Scene Analysis

Humnabadkar,

Sikdar,

Zhang

et al. 2024

2024 IEEE International Conference on Image Processing (ICIP)

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We introduce a novel approach for traffic scene analysis in driving videos by exploring spatio-temporal relationships captured by a temporal frame-to-frame (f2f) bipartite graph, eliminating the need for complex image-level highdimensional feature extraction. Instead, we rely on object detectors that provide bounding box information. The proposed graph approach efficiently connects objects across frames where nodes represent essential object attributes, and edges signify interactions based on simple spatial metrics such as distance and angles between objects. A key innovation is the integration of dynamic edge attributes, computed using Multilayer Perceptrons (MLP) by exploring this spatial metric. These attributes enhance our Interaction-aware Graph Neural Networks (IA-GNNs) framework by adapting the PageRankdriven approximate personalized propagation of neural predictions (APPNP) scheme and graph attention mechanism in a novel way. This has significantly improved our model's ability to understand spatio-temporal interactions of multiple objects in traffic scenarios. We have rigorously evaluated our approach on two benchmark datasets, METEOR and IN-TERACTION, demonstrating its accuracy in analyzing traffic scenarios. This streamlined, graph-based strategy marks a significant shift towards more efficient and insightful traffic scene analysis using video data.

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IDD-X: A Multi-View Dataset for Ego-relative Important Object Localization and Explanation in Dense and Unstructured Traffic

Parikh,

Saluja,

Jawahar

et al. 2024

2024 IEEE International Conference on Robotics and Automation (ICRA)

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METEOR: A Dense, Heterogeneous, and Unstructured Traffic Dataset with Rare Behaviors

Cited by 4 publications

References 15 publications

A Machine Learning-Based Correlation Analysis between Driver Behaviour and Vital Signs: Approach and Case Study

A Machine Learning-Based Correlation Analysis between Driver Behaviour and Vital Signs: Approach and Case Study

Driving Through Graphs: a Bipartite Graph for Traffic Scene Analysis

IDD-X: A Multi-View Dataset for Ego-relative Important Object Localization and Explanation in Dense and Unstructured Traffic

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