myEye2Wheeler: A Two-Wheeler Indian Driver Real-World Eye-Tracking Dataset

• URL: http://arxiv.org/abs/2502.12723v1
• Date: Tue, 18 Feb 2025 10:39:00 GMT
• Title: myEye2Wheeler: A Two-Wheeler Indian Driver Real-World Eye-Tracking Dataset
• Authors: Bhaiya Vaibhaw Kumar, Deepti Rawat, Tanvi Kandalla, Aarnav Nagariya, Kavita Vemuri,
• Abstract summary: This paper presents the myEye2Wheeler dataset, a unique resource of real-world gaze behaviour of two-wheeler drivers.<n>Our dataset offers a critical lens into the unique visual attention patterns and insights into the decision-making of Indian two-wheeler drivers.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: This paper presents the myEye2Wheeler dataset, a unique resource of real-world gaze behaviour of two-wheeler drivers navigating complex Indian traffic. Most datasets are from four-wheeler drivers on well-planned roads and homogeneous traffic. Our dataset offers a critical lens into the unique visual attention patterns and insights into the decision-making of Indian two-wheeler drivers. The analysis demonstrates that existing saliency models, like TASED-Net, perform less effectively on the myEye-2Wheeler dataset compared to when applied on the European 4-wheeler eye tracking datasets (DR(Eye)VE), highlighting the need for models specifically tailored to the traffic conditions. By introducing the dataset, we not only fill a significant gap in two-wheeler driver behaviour research in India but also emphasise the critical need for developing context-specific saliency models. The larger aim is to improve road safety for two-wheeler users and lane-planning to support a cost-effective mode of transport.

Related papers

• Data Limitations for Modeling Top-Down Effects on Drivers' Attention [12.246649738388388]
  Driving is a visuomotor task, i.e., there is a connection between what drivers see and what they do. Some models of drivers' gaze account for top-down effects of drivers' actions. The majority learn only bottom-up correlations between human gaze and driving footage.
  arXiv  Detail & Related papers  (2024-04-12T18:23:00Z)
• 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)
• AIDE: A Vision-Driven Multi-View, Multi-Modal, Multi-Tasking Dataset for Assistive Driving Perception [26.84439405241999]
  We present an AssIstive Driving pErception dataset (AIDE) that considers context information both inside and outside the vehicle. AIDE facilitates holistic driver monitoring through three distinctive characteristics. Two fusion strategies are introduced to give new insights into learning effective multi-stream/modal representations.
  arXiv  Detail & Related papers  (2023-07-26T03:12:05Z)
• OpenLane-V2: A Topology Reasoning Benchmark for Unified 3D HD Mapping [84.65114565766596]
  We present OpenLane-V2, the first dataset on topology reasoning for traffic scene structure. OpenLane-V2 consists of 2,000 annotated road scenes that describe traffic elements and their correlation to the lanes. We evaluate various state-of-the-art methods, and present their quantitative and qualitative results on OpenLane-V2 to indicate future avenues for investigating topology reasoning in traffic scenes.
  arXiv  Detail & Related papers  (2023-04-20T16:31:22Z)
• IDD-3D: Indian Driving Dataset for 3D Unstructured Road Scenes [79.18349050238413]
  Preparation and training of deploy-able deep learning architectures require the models to be suited to different traffic scenarios. An unstructured and complex driving layout found in several developing countries such as India poses a challenge to these models. We build a new dataset, IDD-3D, which consists of multi-modal data from multiple cameras and LiDAR sensors with 12k annotated driving LiDAR frames.
  arXiv  Detail & Related papers  (2022-10-23T23:03:17Z)
• COOPERNAUT: End-to-End Driving with Cooperative Perception for Networked Vehicles [54.61668577827041]
  We introduce COOPERNAUT, an end-to-end learning model that uses cross-vehicle perception for vision-based cooperative driving. Our experiments on AutoCastSim suggest that our cooperative perception driving models lead to a 40% improvement in average success rate.
  arXiv  Detail & Related papers  (2022-05-04T17:55:12Z)
• Fine-Grained Vehicle Perception via 3D Part-Guided Visual Data Augmentation [77.60050239225086]
  We propose an effective training data generation process by fitting a 3D car model with dynamic parts to vehicles in real images. Our approach is fully automatic without any human interaction. We present a multi-task network for VUS parsing and a multi-stream network for VHI parsing.
  arXiv  Detail & Related papers  (2020-12-15T03:03:38Z)
• VehicleNet: Learning Robust Visual Representation for Vehicle Re-identification [116.1587709521173]
  We propose to build a large-scale vehicle dataset (called VehicleNet) by harnessing four public vehicle datasets. We design a simple yet effective two-stage progressive approach to learning more robust visual representation from VehicleNet. We achieve the state-of-art accuracy of 86.07% mAP on the private test set of AICity Challenge.
  arXiv  Detail & Related papers  (2020-04-14T05:06:38Z)

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.