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