Evaluation of estimation approaches on the quality and robustness of collision warning system

Vehicle safety is one of the most challenging aspect of future-generation autonomous and semi-autonomous vehicles. Collision warning systems (CCWs), as a proposed solution framework, can be relied as the main structure to address the issues in this area. In this framework, information plays a very important role. Each vehicle has access to its own information immediately. However, another vehicle information is available through a wireless communication. Data loss is very common issue for such communication approach. As a consequence, CCW would suffer from providing late or false detection awareness. Robust estimation of lost data is of this paper interest which its goal is to reconstruct or estimate lost network data from previous available or estimated data as close to actual values as possible under different rate of lost. In this paper, we will investigate and evaluate three different algorithms including constant velocity, constant acceleration and Kalman estimator for this purpose. We make a comparison between their performance which reveals the ability of them in term of accuracy and robustness for estimation and prediction based on previous samples which at the end affects the quality of CCW in awareness generation

Benets of tight coupled architectures for the integration of GNSS receiver and Vanet transceiver

Vehicular adhoc networks (VANETs) are one emerging type of networks that will enable a broad range of applications such as public safety, traffic management, traveler information support and entertain ment. Whether wireless access may be asynchronous or synchronous (respectively as in the upcoming IEEE 8021.11p standard or in some alternative emerging solutions), a synchronization among nodes is required. Moreover, the information on position is needed to let vehicular services work and to correctly forward the messages. As a result, timing and positioning are a strong prerequisite of VANETs. Also the diffusion of enhanced GNSS Navigators paves the way to the integration between GNSS receivers and VANET transceiv ers. This position paper presents an analysis on potential benefits coming from a tightcoupling between the two: the dissertation is meant to show to what extent Intelligent Transportation System (ITS) services could benefit from the proposed architectur

The Impact of Driver Reaction in Cooperative Vehicle Safety Systems

Cooperative Vehicular Safety (CVS) has recently been widely studied in the field of automated vehicular systems. CVS systems help decrease the rates of accidents. However, implementing and testing CVS applications in real world is very costly and risky. Hence, most of the related research studies on CVS applications have relied mainly on simulations. In simulated CVS systems, it is important to consider all critical aspects of used models, and how these models affect one another. The movement model is a key component in the simulation study of CVS systems, which controls the mobility of vehicles (nodes) and responses to the continually changing acquiredinformation. However, existing mobility models are not created to take action(s) in response to hazardous situations (identified by situational awareness component). Integrating the reaction(s) to a hazardous alert is a missing element in current CVS system simulations. Hence to rectify this deficiency, this work is to incorporate a Driver’s Reaction Model (DReaM) that react and respond to hazard alerts, and studies the effect of main components of CVS system including the added model. We examined a simulation modeling framework that describes cooperative vehicle safety system as one unified model. The studied framework is powered by cooperation and communication between vehicles. Investigated elements are communication model, movement model, warning generation, and driver response to warning indicating an emergency of near to crash situation

VANET Applications: Hot Use Cases

Current challenges of car manufacturers are to make roads safe, to achieve free flowing traffic with few congestions, and to reduce pollution by an effective fuel use. To reach these goals, many improvements are performed in-car, but more and more approaches rely on connected cars with communication capabilities between cars, with an infrastructure, or with IoT devices. Monitoring and coordinating vehicles allow then to compute intelligent ways of transportation. Connected cars have introduced a new way of thinking cars - not only as a mean for a driver to go from A to B, but as smart cars - a user extension like the smartphone today. In this report, we introduce concepts and specific vocabulary in order to classify current innovations or ideas on the emerging topic of smart car. We present a graphical categorization showing this evolution in function of the societal evolution. Different perspectives are adopted: a vehicle-centric view, a vehicle-network view, and a user-centric view; described by simple and complex use-cases and illustrated by a list of emerging and current projects from the academic and industrial worlds. We identified an empty space in innovation between the user and his car: paradoxically even if they are both in interaction, they are separated through different application uses. Future challenge is to interlace social concerns of the user within an intelligent and efficient driving

Analysis of Driver Behavior Modeling in Connected Vehicle Safety Systems Through High Fidelity Simulation

A critical aspect of connected vehicle safety analysis is understanding the impact of human behavior on the overall performance of the safety system. Given the variation in human driving behavior and the expectancy for high levels of performance, it is crucial for these systems to be flexible to various driving characteristics. However, design, testing, and evaluation of these active safety systems remain a challenging task, exacerbated by the lack of behavioral data and practical test platforms. Additionally, the need for the operation of these systems in critical and dangerous situations makes the burden of their evaluation very costly and time-consuming. As an alternative option, researchers attempt to use simulation platforms to study and evaluate their algorithms. In this work, we introduce a high fidelity simulation platform, designed for a hybrid transportation system involving both human-driven and automated vehicles. We decompose the human driving task and offer a modular approach in simulating a large-scale traffic scenario, making it feasible for extensive studying of automated and active safety systems. Furthermore, we propose a human-interpretable driver model represented as a closed-loop feedback controller. For this model, we analyze a large driving dataset to extract expressive parameters that would best describe different driving characteristics. Finally, we recreate a similarly dense traffic scenario within our simulator and conduct a thorough analysis of different human-specific and system-specific factors and study their effect on the performance and safety of the traffic network

Application and Control Aware Communication Strategies for Transportation and Energy Cyber-Physical Systems

Cyber--Physical Systems (CPSs) are a generation of engineered systems in which computing, communication, and control components are tightly integrated. Some important application domains of CPS are transportation, energy, and medical systems. The dynamics of CPSs are complex, involving the stochastic nature of communication systems, discrete dynamics of computing systems, and continuous dynamics of control systems. The existence of communication between and among controllers of physical processes is one of the basic characteristics of CPSs. Under this situation, some fundamental questions are: 1) How does the network behavior (communication delay, packet loss, etc.) affect the stability of the system? 2) Under what conditions is a complex system stabilizable?;In cases where communication is a component of a control system, scalability of the system becomes a concern. Therefore, one of the first issues to consider is how information about a physical process should be communicated. For example, the timing for sampling and communication is one issue. The traditional approach is to sample the physical process periodically or at predetermined times. An alternative is to sample it when specific events occur. Event-based sampling requires continuous monitoring of the system to decide a sample needs to be communicated. The main contributions of this dissertation in energy cyber-physical system domain are designing and modeling of event-based (on-demand) communication mechanisms. We show that in the problem of tracking a dynamical system over a network, if message generation and communication have correlation with estimation error, the same performance as the periodic sampling and communication method can be reached using a significantly lower rate of data.;For more complex CPSs such as vehicle safety systems, additional considerations for the communication component are needed. Communication strategies that enable robust situational awareness are critical for the design of CPSs, in particular for transportation systems. In this dissertation, we utilize the recently introduced concept of model-based communication and propose a new communication strategy to address this need. Our approach to model behavior of remote vehicles mathematically is to describe the small-scale structure of the remote vehicle movement (e.g. braking, accelerating) by a set of dynamic models and represent the large-scale structure (e.g. free following, turning) by coupling these dynamic models together into a Markov chain. Assuming model-based communication approach, a novel stochastic model predictive method is proposed to achieve cruise control goals and investigate the effect of new methodology.;To evaluate the accuracy and robustness of a situational awareness methodology, it is essential to study the mutual effect of the components of a situational awareness subsystem, and their impact on the accuracy of situational awareness. The main components are estimation and networking processes. One possible approach in this task is to produce models that provide a clear view into the dynamics of these two components. These models should integrate continuous physical dynamics, expressed with ordinary differential equations, with the discrete behaviors of communication, expressed with finite automata or Markov chain. In this dissertation, a hybrid automata model is proposed to combine and model both networking and estimation components in a single framework and investigate their interactions.;In summary, contributions of this dissertation lie in designing and evaluating methods that utilize knowledge of the physical element of CPSs to optimize the behavior of communication subsystems. Employment of such methods yields significant overall system performance improvement without incurring additional communication deployment costs

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