# A Stochastic Hybrid Framework for Driver Behavior Modeling Based on Hierarchical Dirichlet Process

arXiv: https://arxiv.org/abs/1711.05655 · HTML: https://mlsanity.com/paper/1711.05655/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 15 Nov 2017.

## Built on by (most cited first, 4 of 4)

- [Social Coordination and Altruism in Autonomous Driving](https://mlsanity.com/paper/2107.00200/) (2021)
- [Robustness and Adaptability of Reinforcement Learning based Cooperative Autonomous Driving in Mixed-autonomy Traffic](https://mlsanity.com/paper/2202.00881/) (2022)
- [Altruistic Maneuver Planning for Cooperative Autonomous Vehicles Using Multi-agent Advantage Actor-Critic](https://mlsanity.com/paper/2107.05664/) (2021)
- [Towards Learning Generalizable Driving Policies from Restricted Latent Representations](https://mlsanity.com/paper/2111.03688/) (2021)

## Abstract (arXiv)

Scalability is one of the major issues for real-world Vehicle-to-Vehicle network realization. To tackle this challenge, a stochastic hybrid modeling framework based on a non-parametric Bayesian inference method, i.e., hierarchical Dirichlet process (HDP), is investigated in this paper. This framework is able to jointly model driver/vehicle behavior through forecasting the vehicle dynamical time-series. This modeling framework could be merged with the notion of model-based information networking, which is recently proposed in the vehicular literature, to overcome the scalability challenges in dense vehicular networks via broadcasting the behavioral models instead of raw information dissemination. This modeling approach has been applied on several scenarios from the realistic Safety Pilot Model Deployment (SPMD) driving data set and the results show a higher performance of this model in comparison with the zero-hold method as the baseline.