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

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.Comment: This is the accepted version of the paper in 2018 IEEE 88th Vehicular Technology Conference (VTC2018-Fall) (references added, title and abstract modified

Distributed vehicular communication protocols for autonomous intersection management

Intersections are considered to be a vital part of urban transportation and drivers are prone to make more mistakes, when driving through the intersections. A high percentage of the total fatal car accidents leading to injuries are reported within intersections annually. On the other side, there usually is traffic congestion at intersections during busy times of day. Stopping the vehicles in one direction to let the vehicle pass in the other directions leads to this phenomenon and it has a huge effect on traffic delay, which causes great squander in natural and human resources as well as leading to weather pollution in metropolises. The goal of this paper is to design and simulate different spatio-temporal-based algorithms for autonomous connected vehicles to be able to cross the intersection safely and efficiently. Vehicles employ vehicle-to-vehicle (V2V) communication via dedicated short range communications (DSRC) [4, 1] to exchange their kinematic information with each other. The proposed algorithms are compared to each other as well as with traditional methods like traffic lights in terms of various performance metrics such as traffic congestion, speed and especially delay to find the optimal control approach for autonomous intersection management