RISEE: A Highly Interactive Naturalistic Driving Trajectories Dataset with Human Subjective Risk Perception and Eye-tracking Information

• URL: http://arxiv.org/abs/2507.19490v2
• Date: Tue, 29 Jul 2025 01:44:16 GMT
• Title: RISEE: A Highly Interactive Naturalistic Driving Trajectories Dataset with Human Subjective Risk Perception and Eye-tracking Information
• Authors: Xinzheng Wu, Junyi Chen, Peiyi Wang, Shunxiang Chen, Haolan Meng, Yong Shen,
• Abstract summary: Most existing datasets primarily focus on vehicle motion states and trajectories, human-related information.<n>This paper constructs the Risk-Informed Subjective Evaluation and Eye-tracking (RISEE) dataset.<n>RISEE dataset specifically contains human subjective evaluations and eye-tracking data apart from regular naturalistic driving trajectories.
• Score: 4.153091882015747
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• Abstract: In the research and development (R&D) and verification and validation (V&V) phases of autonomous driving decision-making and planning systems, it is necessary to integrate human factors to achieve decision-making and evaluation that align with human cognition. However, most existing datasets primarily focus on vehicle motion states and trajectories, neglecting human-related information. In addition, current naturalistic driving datasets lack sufficient safety-critical scenarios while simulated datasets suffer from low authenticity. To address these issues, this paper constructs the Risk-Informed Subjective Evaluation and Eye-tracking (RISEE) dataset which specifically contains human subjective evaluations and eye-tracking data apart from regular naturalistic driving trajectories. By leveraging the complementary advantages of drone-based (high realism and extensive scenario coverage) and simulation-based (high safety and reproducibility) data collection methods, we first conduct drone-based traffic video recording at a highway ramp merging area. After that, the manually selected highly interactive scenarios are reconstructed in simulation software, and drivers' first-person view (FPV) videos are generated, which are then viewed and evaluated by recruited participants. During the video viewing process, participants' eye-tracking data is collected. After data processing and filtering, 3567 valid subjective risk ratings from 101 participants across 179 scenarios are retained, along with 2045 qualified eye-tracking data segments. The collected data and examples of the generated FPV videos are available in our website.

Related papers

• Context-based Motion Retrieval using Open Vocabulary Methods for Autonomous Driving [0.5249805590164902]
  We propose a novel context-aware motion retrieval framework to support targeted evaluation of autonomous driving systems in diverse, human-centered scenarios.<n>Our approach outperforms state-of-the-art models by up to 27.5% accuracy in motion-context retrieval, when evaluated on the WayMoCo dataset.
  arXiv  Detail & Related papers  (2025-08-01T12:41:52Z)
• SafeAug: Safety-Critical Driving Data Augmentation from Naturalistic Datasets [7.865191493201841]
  We propose a novel framework to augment the safety-critical driving data from the naturalistic dataset to address this issue.<n>In this framework, we first detect vehicles using YOLOv5, followed by depth estimation and 3D transformation to simulate vehicle proximity and critical driving scenarios better.<n>Compared to the simulated or artificially generated data, our augmentation methods can generate safety-critical driving data with minimal compromise on image authenticity.
  arXiv  Detail & Related papers  (2025-01-03T23:46:29Z)
• Traffic and Safety Rule Compliance of Humans in Diverse Driving Situations [48.924085579865334]
  Analyzing human data is crucial for developing autonomous systems that replicate safe driving practices. This paper presents a comparative evaluation of human compliance with traffic and safety rules across multiple trajectory prediction datasets.
  arXiv  Detail & Related papers  (2024-11-04T09:21:00Z)
• D2E-An Autonomous Decision-making Dataset involving Driver States and Human Evaluation [6.890077875318333]
  Driver to Evaluation dataset (D2E) is an autonomous decision-making dataset. It contains data on driver states, vehicle states, environmental situations, and evaluation scores from human reviewers. D2E contains over 1100 segments of interactive driving case data covering from human driver factor to evaluation results.
  arXiv  Detail & Related papers  (2024-04-12T21:29:18Z)
• Leveraging Driver Field-of-View for Multimodal Ego-Trajectory Prediction [69.29802752614677]
  RouteFormer is a novel ego-trajectory prediction network combining GPS data, environmental context, and the driver's field-of-view.<n>To tackle data scarcity and enhance diversity, we introduce GEM, a dataset of urban driving scenarios enriched with synchronized driver field-of-view and gaze data.
  arXiv  Detail & Related papers  (2023-12-13T23:06:30Z)
• JRDB-Traj: A Dataset and Benchmark for Trajectory Forecasting in Crowds [79.00975648564483]
  Trajectory forecasting models, employed in fields such as robotics, autonomous vehicles, and navigation, face challenges in real-world scenarios. This dataset provides comprehensive data, including the locations of all agents, scene images, and point clouds, all from the robot's perspective. The objective is to predict the future positions of agents relative to the robot using raw sensory input data.
  arXiv  Detail & Related papers  (2023-11-05T18:59:31Z)
• UniSim: A Neural Closed-Loop Sensor Simulator [76.79818601389992]
  We present UniSim, a neural sensor simulator that takes a single recorded log captured by a sensor-equipped vehicle. UniSim builds neural feature grids to reconstruct both the static background and dynamic actors in the scene. We incorporate learnable priors for dynamic objects, and leverage a convolutional network to complete unseen regions.
  arXiv  Detail & Related papers  (2023-08-03T17:56:06Z)
• DeepAccident: A Motion and Accident Prediction Benchmark for V2X Autonomous Driving [76.29141888408265]
  We propose a large-scale dataset containing diverse accident scenarios that frequently occur in real-world driving. The proposed DeepAccident dataset includes 57K annotated frames and 285K annotated samples, approximately 7 times more than the large-scale nuScenes dataset.
  arXiv  Detail & Related papers  (2023-04-03T17:37:00Z)
• Generating and Characterizing Scenarios for Safety Testing of Autonomous Vehicles [86.9067793493874]
  We propose efficient mechanisms to characterize and generate testing scenarios using a state-of-the-art driving simulator. We use our method to characterize real driving data from the Next Generation Simulation (NGSIM) project. We rank the scenarios by defining metrics based on the complexity of avoiding accidents and provide insights into how the AV could have minimized the probability of incurring an accident.
  arXiv  Detail & Related papers  (2021-03-12T17:00:23Z)
• 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)
• Vehicle-Human Interactive Behaviors in Emergency: Data Extraction from Traffic Accident Videos [0.0]
  Currently, studying the vehicle-human interactive behavior in the emergency needs a large amount of datasets in the actual emergent situations that are almost unavailable. This paper provides a new yet convenient way to extract the interactive behavior data (i.e., the trajectories of vehicles and humans) from actual accident videos. The main challenge for data extraction from real-time accident video lies in the fact that the recording cameras are un-calibrated and the angles of surveillance are unknown.
  arXiv  Detail & Related papers  (2020-03-02T22:17:46Z)

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.