4,095 research outputs found
Two-Hop Connectivity to the Roadside in a VANET Under the Random Connection Model
We compute the expected number of cars that have at least one two-hop path to
a fixed roadside unit in a one-dimensional vehicular ad hoc network in which
other cars can be used as relays to reach a roadside unit when they do not have
a reliable direct link. The pairwise channels between cars experience Rayleigh
fading in the random connection model, and so exist, with probability function
of the mutual distance between the cars, or between the cars and the roadside
unit. We derive exact equivalents for this expected number of cars when the car
density tends to zero and to infinity, and determine its behaviour using
an infinite oscillating power series in , which is accurate for all
regimes. We also corroborate those findings to a realistic situation, using
snapshots of actual traffic data. Finally, a normal approximation is discussed
for the probability mass function of the number of cars with a two-hop
connection to the origin. The probability mass function appears to be well
fitted by a Gaussian approximation with mean equal to the expected number of
cars with two hops to the origin.Comment: 21 pages, 7 figure
Adoption of vehicular ad hoc networking protocols by networked robots
This paper focuses on the utilization of wireless networking in the robotics domain. Many researchers have already equipped their robots with wireless communication capabilities, stimulated by the observation that multi-robot systems tend to have several advantages over their single-robot counterparts. Typically, this integration of wireless communication is tackled in a quite pragmatic manner, only a few authors presented novel Robotic Ad Hoc Network (RANET) protocols that were designed specifically with robotic use cases in mind. This is in sharp contrast with the domain of vehicular ad hoc networks (VANET). This observation is the starting point of this paper. If the results of previous efforts focusing on VANET protocols could be reused in the RANET domain, this could lead to rapid progress in the field of networked robots. To investigate this possibility, this paper provides a thorough overview of the related work in the domain of robotic and vehicular ad hoc networks. Based on this information, an exhaustive list of requirements is defined for both types. It is concluded that the most significant difference lies in the fact that VANET protocols are oriented towards low throughput messaging, while RANET protocols have to support high throughput media streaming as well. Although not always with equal importance, all other defined requirements are valid for both protocols. This leads to the conclusion that cross-fertilization between them is an appealing approach for future RANET research. To support such developments, this paper concludes with the definition of an appropriate working plan
TruPercept: Trust Modelling for Autonomous Vehicle Cooperative Perception from Synthetic Data
Inter-vehicle communication for autonomous vehicles (AVs) stands to provide
significant benefits in terms of perception robustness. We propose a novel
approach for AVs to communicate perceptual observations, tempered by trust
modelling of peers providing reports. Based on the accuracy of reported object
detections as verified locally, communicated messages can be fused to augment
perception performance beyond line of sight and at great distance from the ego
vehicle. Also presented is a new synthetic dataset which can be used to test
cooperative perception. The TruPercept dataset includes unreliable and
malicious behaviour scenarios to experiment with some challenges cooperative
perception introduces. The TruPercept runtime and evaluation framework allows
modular component replacement to facilitate ablation studies as well as the
creation of new trust scenarios we are able to show
Heterogeneous V2V Communications in Multi-Link and Multi-RAT Vehicular Networks
Connected and automated vehicles will enable advanced traffic safety and
efficiency applications thanks to the dynamic exchange of information between
vehicles, and between vehicles and infrastructure nodes. Connected vehicles can
utilize IEEE 802.11p for vehicle-to-vehicle (V2V) and vehicle-to-infrastructure
(V2I) communications. However, a widespread deployment of connected vehicles
and the introduction of connected automated driving applications will notably
increase the bandwidth and scalability requirements of vehicular networks. This
paper proposes to address these challenges through the adoption of
heterogeneous V2V communications in multi-link and multi-RAT vehicular
networks. In particular, the paper proposes the first distributed (and
decentralized) context-aware heterogeneous V2V communications algorithm that is
technology and application agnostic, and that allows each vehicle to
autonomously and dynamically select its communications technology taking into
account its application requirements and the communication context conditions.
This study demonstrates the potential of heterogeneous V2V communications, and
the capability of the proposed algorithm to satisfy the vehicles' application
requirements while approaching the estimated upper bound network capacity
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
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