Urban Deceleration Behavior Modes Under Scene Context: An Early-Kinematic Classifier from Argoverse 2 Multi-Agent Trajectories
arXiv:2607.00027v1 Announce Type: cross Abstract: Urban deceleration is one of the most empirically studied yet least taxonomically organized behaviors in car-following research. Recent perception-equipped autonomous-vehicle datasets enable trajectory-anchored mode discovery. We extract 1,219 sustained deceleration events from 234 urban driving logs of the Argoverse 2 Sensor dataset, encode each event in a 19-dimensional kinematic feature vector, discover behavioral modes via K-means clustering with bootstrap stability analysis, and quantify modulation by eleven scene-context variables. A Hist
The proliferation of advanced perception-equipped autonomous vehicle datasets like Argoverse 2 provides the necessary granular data for empirical study of complex urban driving behaviors, enabling detailed analysis now.
Understanding and classifying complex urban driving behaviors, particularly deceleration, is crucial for the safe and efficient development of autonomous driving systems and for optimizing traffic flow in smart cities.
The ability to accurately classify urban deceleration behavior modes based on early kinematic features and scene context changes how autonomous vehicles will predict and react to traffic scenarios, improving safety and decision-making.
- · Autonomous Vehicle Developers
- · Smart City Planners
- · AI/ML Data Scientists
- · Automotive OEMs
- · Companies with less sophisticated behavior prediction models
- · Insurance companies reliant on human-error statistics
Improved safety and predictability of autonomous vehicle operations, reducing accidents and traffic congestion.
Faster adoption and regulatory approval of higher-level autonomous driving features due to enhanced reliability in complex urban environments.
The integration of such sophisticated behavioral models into broader traffic management systems, enabling predictive urban planning and dynamic infrastructure adjustments.
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