22 research outputs found
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
A Learning-based Stochastic MPC Design for Cooperative Adaptive Cruise Control to Handle Interfering Vehicles
Vehicle to Vehicle (V2V) communication has a great potential to improve
reaction accuracy of different driver assistance systems in critical driving
situations. Cooperative Adaptive Cruise Control (CACC), which is an automated
application, provides drivers with extra benefits such as traffic throughput
maximization and collision avoidance. CACC systems must be designed in a way
that are sufficiently robust against all special maneuvers such as cutting-into
the CACC platoons by interfering vehicles or hard braking by leading cars. To
address this problem, a Neural- Network (NN)-based cut-in detection and
trajectory prediction scheme is proposed in the first part of this paper. Next,
a probabilistic framework is developed in which the cut-in probability is
calculated based on the output of the mentioned cut-in prediction block.
Finally, a specific Stochastic Model Predictive Controller (SMPC) is designed
which incorporates this cut-in probability to enhance its reaction against the
detected dangerous cut-in maneuver. The overall system is implemented and its
performance is evaluated using realistic driving scenarios from Safety Pilot
Model Deployment (SPMD).Comment: 10 pages, Submitted as a journal paper at T-I
A Learning-Based Framework for Two-Dimensional Vehicle Maneuver Prediction over V2V Networks
Situational awareness in vehicular networks could be substantially improved
utilizing reliable trajectory prediction methods. More precise situational
awareness, in turn, results in notably better performance of critical safety
applications, such as Forward Collision Warning (FCW), as well as comfort
applications like Cooperative Adaptive Cruise Control (CACC). Therefore,
vehicle trajectory prediction problem needs to be deeply investigated in order
to come up with an end to end framework with enough precision required by the
safety applications' controllers. This problem has been tackled in the
literature using different methods. However, machine learning, which is a
promising and emerging field with remarkable potential for time series
prediction, has not been explored enough for this purpose. In this paper, a
two-layer neural network-based system is developed which predicts the future
values of vehicle parameters, such as velocity, acceleration, and yaw rate, in
the first layer and then predicts the two-dimensional, i.e. longitudinal and
lateral, trajectory points based on the first layer's outputs. The performance
of the proposed framework has been evaluated in realistic cut-in scenarios from
Safety Pilot Model Deployment (SPMD) dataset and the results show a noticeable
improvement in the prediction accuracy in comparison with the kinematics model
which is the dominant employed model by the automotive industry. Both ideal and
nonideal communication circumstances have been investigated for our system
evaluation. For non-ideal case, an estimation step is included in the framework
before the parameter prediction block to handle the drawbacks of packet drops
or sensor failures and reconstruct the time series of vehicle parameters at a
desirable frequency
A Driver Behavior Modeling Structure Based on Non-parametric Bayesian Stochastic Hybrid Architecture
Heterogeneous nature of the vehicular networks, which results from the
co-existence of human-driven, semi-automated, and fully autonomous vehicles, is
a challenging phenomenon toward the realization of the intelligent
transportation systems with an acceptable level of safety, comfort, and
efficiency. Safety applications highly suffer from communication resource
limitations, specifically in dense and congested vehicular networks. The idea
of model-based communication (MBC) has been recently proposed to address this
issue. In this work, we propose Gaussian Process-based Stochastic Hybrid System
with Cumulative Relevant History (CRH-GP-SHS) framework, which is a
hierarchical stochastic hybrid modeling structure, built upon a non-parametric
Bayesian inference method, i.e. Gaussian processes. This framework is proposed
in order to be employed within the MBC context to jointly model driver/vehicle
behavior as a stochastic object. Non-parametric Bayesian methods relieve the
limitations imposed by non-evolutionary model structures and enable the
proposed framework to properly capture different stochastic behaviors. The
performance of the proposed CRH-GP-SHS framework at the inter-mode level has
been evaluated over a set of realistic lane change maneuvers from NGSIM-US101
dataset. The results show a noticeable performance improvement for GP in
comparison to the baseline constant speed model, specifically in critical
situations such as highly congested networks. Moreover, an augmented model has
also been proposed which is a composition of GP and constant speed models and
capable of capturing the driver behavior under various network reliability
conditions.Comment: This work has been accepted in 2018 IEEE Connected and Automated
Vehicles Symposium (CAVS 2018
Implementation and Evaluation of a Cooperative Vehicle-to-Pedestrian Safety Application
While the development of Vehicle-to-Vehicle (V2V) safety applications based
on Dedicated Short-Range Communications (DSRC) has been extensively undergoing
standardization for more than a decade, such applications are extremely missing
for Vulnerable Road Users (VRUs). Nonexistence of collaborative systems between
VRUs and vehicles was the main reason for this lack of attention. Recent
developments in Wi-Fi Direct and DSRC-enabled smartphones are changing this
perspective. Leveraging the existing V2V platforms, we propose a new framework
using a DSRC-enabled smartphone to extend safety benefits to VRUs. The
interoperability of applications between vehicles and portable DSRC enabled
devices is achieved through the SAE J2735 Personal Safety Message (PSM).
However, considering the fact that VRU movement dynamics, response times, and
crash scenarios are fundamentally different from vehicles, a specific framework
should be designed for VRU safety applications to study their performance. In
this article, we first propose an end-to-end Vehicle-to-Pedestrian (V2P)
framework to provide situational awareness and hazard detection based on the
most common and injury-prone crash scenarios. The details of our VRU safety
module, including target classification and collision detection algorithms, are
explained next. Furthermore, we propose and evaluate a mitigating solution for
congestion and power consumption issues in such systems. Finally, the whole
system is implemented and analyzed for realistic crash scenarios
Context-Aware Target Classification with Hybrid Gaussian Process prediction for Cooperative Vehicle Safety systems
Vehicle-to-Everything (V2X) communication has been proposed as a potential
solution to improve the robustness and safety of autonomous vehicles by
improving coordination and removing the barrier of non-line-of-sight sensing.
Cooperative Vehicle Safety (CVS) applications are tightly dependent on the
reliability of the underneath data system, which can suffer from loss of
information due to the inherent issues of their different components, such as
sensors failures or the poor performance of V2X technologies under dense
communication channel load. Particularly, information loss affects the target
classification module and, subsequently, the safety application performance. To
enable reliable and robust CVS systems that mitigate the effect of information
loss, we proposed a Context-Aware Target Classification (CA-TC) module coupled
with a hybrid learning-based predictive modeling technique for CVS systems. The
CA-TC consists of two modules: A Context-Aware Map (CAM), and a Hybrid Gaussian
Process (HGP) prediction system. Consequently, the vehicle safety applications
use the information from the CA-TC, making them more robust and reliable. The
CAM leverages vehicles path history, road geometry, tracking, and prediction;
and the HGP is utilized to provide accurate vehicles' trajectory predictions to
compensate for data loss (due to communication congestion) or sensor
measurements' inaccuracies. Based on offline real-world data, we learn a finite
bank of driver models that represent the joint dynamics of the vehicle and the
drivers' behavior. We combine offline training and online model updates with
on-the-fly forecasting to account for new possible driver behaviors. Finally,
our framework is validated using simulation and realistic driving scenarios to
confirm its potential in enhancing the robustness and reliability of CVS
systems