Constructing a Highly Interactive Vehicle Motion Dataset

Research in the areas related to driving behavior, e. g. , behavior modeling and prediction, requires datasets with highly interactive vehicle motions. Existing public vehicle motion datasets emphasize increasing the number of vehicles and time duration, but behavior-related researchers are suffering from two factors. First, strong interactions among vehicles are not well addressed and datasets are of relatively low-density to observe meaningful interactions. Second, most of the existing datasets are missing the map information with reference paths which is essential for driving-behavior-related research. To address this issue, a dataset with highly interactive vehicle motions is constructed in this paper. A variety of challenging driving scenarios such as unsignalized intersections and roundabouts are included. Reference paths are also constructed from motion data along with high-definition maps so that key features can be generated for both prediction and planning algorithms. Moreover, we propose a set of metrics to extract the interactive motions in different maps, including the minimum difference of time to collision point (MDTTC) and duration of waiting period. Such metrics are used to quantity the interaction density of the dataset. We also give several representative results on prediction and motion generation utilizing the constructed dataset to demonstrate how the dataset can facilitate research in the area of driving behavior.

Authors

• Wei Zhan
• Liting Sun
• Di Wang 0028
• Yinghan Jin
• Masayoshi Tomizuka

Keywords

• Measurement
• Pipelines
• Predictive models
• Prediction algorithms
• Feature extraction
• Data processing
• Planning
• Driver behavior
• Intelligent robots
• Vehicle Motion
• Motion Dataset
• Motion Data
• Roundabout
• Waiting Period
• Reference Path
• Behavioral Analysis
• Latent Space
• Interaction Behavior
• Map Construction
• Paired Datasets
• Occurrence Of Interactions
• Complete Reference
• Motion Prediction
• Speed Profile
• Onboard Sensors
• Baseline Reference
• Vehicle Behavior
• Merging Point
• Future Motion
• Longitudinal Motion
• Bezier Curve
• Inverse Reinforcement Learning
• Conditional Variational Autoencoder
• Deep Generative Models
• Human-like Behavior