16 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 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
Learning-based social coordination to improve safety and robustness of cooperative autonomous vehicles in mixed traffic
It is expected that autonomous vehicles(AVs) and heterogeneous human-driven
vehicles(HVs) will coexist on the same road. The safety and reliability of AVs
will depend on their social awareness and their ability to engage in complex
social interactions in a socially accepted manner. However, AVs are still
inefficient in terms of cooperating with HVs and struggle to understand and
adapt to human behavior, which is particularly challenging in mixed autonomy.
In a road shared by AVs and HVs, the social preferences or individual traits of
HVs are unknown to the AVs and different from AVs, which are expected to follow
a policy, HVs are particularly difficult to forecast since they do not
necessarily follow a stationary policy. To address these challenges, we frame
the mixed-autonomy problem as a multi-agent reinforcement learning (MARL)
problem and propose an approach that allows AVs to learn the decision-making of
HVs implicitly from experience, account for all vehicles' interests, and safely
adapt to other traffic situations. In contrast with existing works, we quantify
AVs' social preferences and propose a distributed reward structure that
introduces altruism into their decision-making process, allowing the altruistic
AVs to learn to establish coalitions and influence the behavior of HVs.Comment: arXiv admin note: substantial text overlap with arXiv:2202.0088
Prediction-aware and Reinforcement Learning based Altruistic Cooperative Driving
Autonomous vehicle (AV) navigation in the presence of Human-driven vehicles
(HVs) is challenging, as HVs continuously update their policies in response to
AVs. In order to navigate safely in the presence of complex AV-HV social
interactions, the AVs must learn to predict these changes. Humans are capable
of navigating such challenging social interaction settings because of their
intrinsic knowledge about other agents behaviors and use that to forecast what
might happen in the future. Inspired by humans, we provide our AVs the
capability of anticipating future states and leveraging prediction in a
cooperative reinforcement learning (RL) decision-making framework, to improve
safety and robustness. In this paper, we propose an integration of two
essential and earlier-presented components of AVs: social navigation and
prediction. We formulate the AV decision-making process as a RL problem and
seek to obtain optimal policies that produce socially beneficial results
utilizing a prediction-aware planning and social-aware optimization RL
framework. We also propose a Hybrid Predictive Network (HPN) that anticipates
future observations. The HPN is used in a multi-step prediction chain to
compute a window of predicted future observations to be used by the value
function network (VFN). Finally, a safe VFN is trained to optimize a social
utility using a sequence of previous and predicted observations, and a safety
prioritizer is used to leverage the interpretable kinematic predictions to mask
the unsafe actions, constraining the RL policy. We compare our prediction-aware
AV to state-of-the-art solutions and demonstrate performance improvements in
terms of efficiency and safety in multiple simulated scenarios