INTERACTION Dataset: An INTERnational, Adversarial and Cooperative moTION Dataset in Interactive Driving Scenarios with Semantic Maps
arXiv:1910.03088
Abstract
Behavior-related research areas such as motion prediction/planning, representation/imitation learning, behavior modeling/generation, and algorithm testing, require support from high-quality motion datasets containing interactive driving scenarios with different driving cultures. In this paper, we present an INTERnational, Adversarial and Cooperative moTION dataset (INTERACTION dataset) in interactive driving scenarios with semantic maps. Five features of the dataset are highlighted. 1) The interactive driving scenarios are diverse, including urban/highway/ramp merging and lane changes, roundabouts with yield/stop signs, signalized intersections, intersections with one/two/all-way stops, etc. 2) Motion data from different countries and different continents are collected so that driving preferences and styles in different cultures are naturally included. 3) The driving behavior is highly interactive and complex with adversarial and cooperative motions of various traffic participants. Highly complex behavior such as negotiations, aggressive/irrational decisions and traffic rule violations are densely contained in the dataset, while regular behavior can also be found from cautious car-following, stop, left/right/U-turn to rational lane-change and cycling and pedestrian crossing, etc. 4) The levels of criticality span wide, from regular safe operations to dangerous, near-collision maneuvers. Real collision, although relatively slight, is also included. 5) Maps with complete semantic information are provided with physical layers, reference lines, lanelet connections and traffic rules. The data is recorded from drones and traffic cameras. Statistics of the dataset in terms of number of entities and interaction density are also provided, along with some utilization examples in a variety of behavior-related research areas. The dataset can be downloaded via https://interaction-dataset.com.
References in corpus (3)
Cited by in corpus (58)
- TNT: Target-driveN Trajectory Prediction
- Social Interactions for Autonomous Driving: A Review and Perspectives
- Pedestrian Models for Autonomous Driving Part II: High-Level Models of Human Behavior
- Pedestrian Trajectory Prediction in Pedestrian-Vehicle Mixed Environments: A Systematic Review
- Injecting Knowledge in Data-driven Vehicle Trajectory Predictors
- Occupancy Flow Fields for Motion Forecasting in Autonomous Driving
- Choose Your Simulator Wisely: A Review on Open-source Simulators for Autonomous Driving
- THOMAS: Trajectory Heatmap Output with learned Multi-Agent Sampling
- Socially-Compatible Behavior Design of Autonomous Vehicles with Verification on Real Human Data
- Social-WaGDAT: Interaction-aware Trajectory Prediction via Wasserstein Graph Double-Attention Network
- On Social Interactions of Merging Behaviors at Highway On-Ramps in Congested Traffic
- Advanced computer vision for extracting georeferenced vehicle trajectories from drone imagery
- Overview of Tools Supporting Planning for Automated Driving
- WiSARD: A Labeled Visual and Thermal Image Dataset for Wilderness Search and Rescue
- Deep Learning for Vision-based Prediction: A Survey
- Game-theoretic Modeling of Traffic in Unsignalized Intersection Network for Autonomous Vehicle Control Verification and Validation
- Ad-datasets: a meta-collection of data sets for autonomous driving
- Heterogeneous Edge-Enhanced Graph Attention Network For Multi-Agent Trajectory Prediction
- Calibration of Human Driving Behavior and Preference Using Naturalistic Traffic Data
- Scenario-Transferable Semantic Graph Reasoning for Interaction-Aware Probabilistic Prediction
- nuScenes Knowledge Graph -- A comprehensive semantic representation of traffic scenes for trajectory prediction
- A Driver-Vehicle Model for ADS Scenario-based Testing
- Efficient Speed Planning for Autonomous Driving in Dynamic Environment with Interaction Point Model
- Improving the Generalizability of Trajectory Prediction Models with Frenet-Based Domain Normalization
- The Importance of Balanced Data Sets: Analyzing a Vehicle Trajectory Prediction Model based on Neural Networks and Distributed Representations
- Relation-based Motion Prediction using Traffic Scene Graphs
- SceNDD: A Scenario-based Naturalistic Driving Dataset
- DiPA: Probabilistic Multi-Modal Interactive Prediction for Autonomous Driving
- Diverse Auto-Curriculum is Critical for Successful Real-World Multiagent Learning Systems
- Scene Transformer: A unified architecture for predicting multiple agent trajectories
- Autonomous Driving at Intersections: A Critical-Turning-Point Approach for Left Turns
- Differentiable Particle Filtering without Modifying the Forward Pass
- A Scenario-Based Platform for Testing Autonomous Vehicle Behavior Prediction Models in Simulation
- Decision-Making Technology for Autonomous Vehicles Learning-Based Methods, Applications and Future Outlook
- Hierarchical Motion Encoder-Decoder Network for Trajectory Forecasting
- Semantic 3D Grid Maps for Autonomous Driving
- Motion Perceiver: Real-Time Occupancy Forecasting for Embedded Systems
- Generalizability Analysis of Graph-based Trajectory Predictor with Vectorized Representation
- Automatic Traffic Scenario Conversion from OpenSCENARIO to CommonRoad
- Spatio-Temporal Graph Dual-Attention Network for Multi-Agent Prediction and Tracking
- MAP-Former: Multi-Agent-Pair Gaussian Joint Prediction
- Reliving the Dataset: Combining the Visualization of Road Users' Interactions with Scenario Reconstruction in Virtual Reality
- Lifelong Vehicle Trajectory Prediction Framework Based on Generative Replay
- Predicting Vehicles Trajectories in Urban Scenarios with Transformer Networks and Augmented Information
- Graph and Recurrent Neural Network-based Vehicle Trajectory Prediction For Highway Driving
- Causal Transfer for Imitation Learning and Decision Making under Sensor-shift
- MS-Net: A Multi-Path Sparse Model for Motion Prediction in Multi-Scenes
- IDE-Net: Interactive Driving Event and Pattern Extraction from Human Data
- Expressing Diverse Human Driving Behavior with Probabilistic Rewards and Online Inference
- A Safe Hierarchical Planning Framework for Complex Driving Scenarios based on Reinforcement Learning
- Prediction-Based Reachability for Collision Avoidance in Autonomous Driving
- Iterative Imitation Policy Improvement for Interactive Autonomous Driving
- CAROM -- Vehicle Localization and Traffic Scene Reconstruction from Monocular Cameras on Road Infrastructures
- Driver Behavior Modelling at the Urban Intersection via Canonical Correlation Analysis
- On complementing end-to-end human behavior predictors with planning
- Negotiation-Aware Reachability-Based Safety Verification for AutonomousDriving in Interactive Scenarios
- Interaction-Aware Behavior Planning for Autonomous Vehicles Validated with Real Traffic Data
- Efficient Sampling-Based Maximum Entropy Inverse Reinforcement Learning with Application to Autonomous Driving