Implicit Cooperative Positioning in Vehicular Networks

Absolute positioning of vehicles is based on Global Navigation Satellite Systems (GNSS) combined with on-board sensors and high-resolution maps. In Cooperative Intelligent Transportation Systems (C-ITS), the positioning performance can be augmented by means of vehicular networks that enable vehicles to share location-related information. This paper presents an Implicit Cooperative Positioning (ICP) algorithm that exploits the Vehicle-to-Vehicle (V2V) connectivity in an innovative manner, avoiding the use of explicit V2V measurements such as ranging. In the ICP approach, vehicles jointly localize non-cooperative physical features (such as people, traffic lights or inactive cars) in the surrounding areas, and use them as common noisy reference points to refine their location estimates. Information on sensed features are fused through V2V links by a consensus procedure, nested within a message passing algorithm, to enhance the vehicle localization accuracy. As positioning does not rely on explicit ranging information between vehicles, the proposed ICP method is amenable to implementation with off-the-shelf vehicular communication hardware. The localization algorithm is validated in different traffic scenarios, including a crossroad area with heterogeneous conditions in terms of feature density and V2V connectivity, as well as a real urban area by using Simulation of Urban MObility (SUMO) for traffic data generation. Performance results show that the proposed ICP method can significantly improve the vehicle location accuracy compared to the stand-alone GNSS, especially in harsh environments, such as in urban canyons, where the GNSS signal is highly degraded or denied.Comment: 15 pages, 10 figures, in review, 201

Infrastructure Wi-Fi for connected autonomous vehicle positioning : a review of the state-of-the-art

In order to realize intelligent vehicular transport networks and self driving cars, connected autonomous vehicles (CAVs) are required to be able to estimate their position to the nearest centimeter. Traditional positioning in CAVs is realized by using a global navigation satellite system (GNSS) such as global positioning system (GPS) or by fusing weighted location parameters from a GNSS with an inertial navigation systems (INSs). In urban environments where Wi-Fi coverage is ubiquitous and GNSS signals experience signal blockage, multipath or non line-of-sight (NLOS) propagation, enterprise or carrier-grade Wi-Fi networks can be opportunistically used for localization or “fused” with GNSS to improve the localization accuracy and precision. While GNSS-free localization systems are in the literature, a survey of vehicle localization from the perspective of a Wi-Fi anchor/infrastructure is limited. Consequently, this review seeks to investigate recent technological advances relating to positioning techniques between an ego vehicle and a vehicular network infrastructure. Also discussed in this paper is an analysis of the location accuracy, complexity and applicability of surveyed literature with respect to intelligent transportation system requirements for CAVs. It is envisaged that hybrid vehicular localization systems will enable pervasive localization services for CAVs as they travel through urban canyons, dense foliage or multi-story car parks

Cooperative Perception for Social Driving in Connected Vehicle Traffic

The development of autonomous vehicle technology has moved to the center of automotive research in recent decades. In the foreseeable future, road vehicles at all levels of automation and connectivity will be required to operate safely in a hybrid traffic where human operated vehicles (HOVs) and fully and semi-autonomous vehicles (AVs) coexist. Having an accurate and reliable perception of the road is an important requirement for achieving this objective. This dissertation addresses some of the associated challenges via developing a human-like social driver model and devising a decentralized cooperative perception framework. A human-like driver model can aid the development of AVs by building an understanding of interactions among human drivers and AVs in a hybrid traffic, therefore facilitating an efficient and safe integration. The presented social driver model categorizes and defines the driver\u27s psychological decision factors in mathematical representations (target force, object force, and lane force). A model predictive control (MPC) is then employed for the motion planning by evaluating the prevailing social forces and considering the kinematics of the controlled vehicle as well as other operating constraints to ensure a safe maneuver in a way that mimics the predictive nature of the human driver\u27s decision making process. A hierarchical model predictive control structure is also proposed, where an additional upper level controller aggregates the social forces over a longer prediction horizon upon the availability of an extended perception of the upcoming traffic via vehicular networking. Based on the prediction of the upper level controller, a sequence of reference lanes is passed to a lower level controller to track while avoiding local obstacles. This hierarchical scheme helps reduce unnecessary lane changes resulting in smoother maneuvers. The dynamic vehicular communication environment requires a robust framework that must consistently evaluate and exploit the set of communicated information for the purpose of improving the perception of a participating vehicle beyond the limitations. This dissertation presents a decentralized cooperative perception framework that considers uncertainties in traffic measurements and allows scalability (for various settings of traffic density, participation rate, etc.). The framework utilizes a Bhattacharyya distance filter (BDF) for data association and a fast covariance intersection fusion scheme (FCI) for the data fusion processes. The conservatism of the covariance intersection fusion scheme is investigated in comparison to the traditional Kalman filter (KF), and two different fusion architectures: sensor-to-sensor and sensor-to-system track fusion are evaluated. The performance of the overall proposed framework is demonstrated via Monte Carlo simulations with a set of empirical communications models and traffic microsimulations where each connected vehicle asynchronously broadcasts its local perception consisting of estimates of the motion states of self and neighboring vehicles along with the corresponding uncertainty measures of the estimates. The evaluated framework includes a vehicle-to-vehicle (V2V) communication model that considers intermittent communications as well as a model that takes into account dynamic changes in an individual vehicle’s sensors’ FoV in accordance with the prevailing traffic conditions. The results show the presence of optimality in participation rate, where increasing participation rate beyond a certain level adversely affects the delay in packet delivery and the computational complexity in data association and fusion processes increase without a significant improvement in the achieved accuracy via the cooperative perception. In a highly dense traffic environment, the vehicular network can often be congested leading to limited bandwidth availability at high participation rates of the connected vehicles in the cooperative perception scheme. To alleviate the bandwidth utilization issues, an information-value discriminating networking scheme is proposed, where each sender broadcasts selectively chosen perception data based on the novelty-value of information. The potential benefits of these approaches include, but are not limited to, the reduction of bandwidth bottle-necking and the minimization of the computational cost of data association and fusion post processing of the shared perception data at receiving nodes. It is argued that the proposed information-value discriminating communication scheme can alleviate these adverse effects without sacrificing the fidelity of the perception

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