5 research outputs found
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
High-Definition Map Representation Techniques for Automated Vehicles
Many studies in the field of robot navigation have focused on environment representation and localization. The goal of map representation is to summarize spatial information in topological and geometrical abstracts. By providing strong priors, maps improve the performance and reliability of automated robots. Due to the transition to fully automated driving in recent years, there has been a constant effort to design methods and technologies to improve the precision of road participants and the environment’s information. Among these efforts is the high-definition (HD) map concept. Making HD maps requires accuracy, completeness, verifiability, and extensibility. Because of the complexity of HD mapping, it is currently expensive and difficult to implement, particularly in an urban environment. In an urban traffic system, the road model is at least a map with sets of roads, lanes, and lane markers. While more research is being dedicated to mapping and localization, a comprehensive review of the various types of map representation is still required. This paper presents a brief overview of map representation, followed by a detailed literature review of HD maps for automated vehicles. The current state of autonomous vehicle (AV) mapping is encouraging, the field has matured to a point where detailed maps of complex environments are built in real time and have been proved useful. Many existing techniques are robust to noise and can cope with a large range of environments. Nevertheless, there are still open problems for future research. AV mapping will continue to be a highly active research area essential to the goal of achieving full autonomy