Current trends in miniaturization and the requirements of commercialization are resulting in an increased focus on small Unmanned Aerial Systems (sUAS). However, conceptual design and mission performance of these sUAS remains a costly endeavor requiring multiple iterations. In this paper, a virtual reality framework is used to assess the mission performance of a representative quadrotor. The mission profile considers waypoint navigation through a cluttered forest environment simulated using the MORSE-Blender platform. The performance assessment is carried out for three different range and three different stereo camera sensors with varying specifications. The flight time, power usage, and rate of virtual collisions are recorded during a Monte Carlo simulation to evaluate the mission performance. Hypotheses pertaining to the trade-offs between sensor specifications, mission flight time, distance traveled, collision likelihood, and mission completion are also examined. Results indicate that range sensors are more capable of avoiding obstacles and that two of the three range sensor configurations in the simulations traveled significantly farther than the remaining sensors.
In prior works, the tasks of noise modeling and sensor characterization have typically been studied independently of each other. In spite of extensive research on noise modeling and sensor characterization, there still exists a need to bridge the gap between parameters used in sensor noise models, and quantities used to characterize commercially available sensors. The included work addresses this need by presenting tutorial-style exemplary analyses that relate noise model parameters to sensor characteristics for some common noise types found in sensors. Specifically, this work seeks to demonstrate that sensor noise characterization techniques can be applied to simulated sensor noise data to recover the original noise model parameters. The presented relationships between noise models and sensor characterization tools can help engineers and scientists numerically verify the expected performance bounds of their systems using simulated signals from commercially available sensors. Moreover, these numerical tools can also guide design engineers towards developing or selecting sensors with specifications especially suited to the design constraints of a given application.
This thesis addresses the impact of Cooperative Adaptive Cruise Control (CACC) on the formation of self-organized traffic jams. Self-organized traffic jams are also known as phantom jams and appear on a road when the vehicular density increases and exceeds a certain threshold (i.e., critical density). The master equation is used to model a self-organized traffic jam at mesoscopic scale. Furthermore, to examine the vehicular behavior under the traffic condition, the master equation utilizes the General Motor(GM)-fourth car-following model to obtain transition probability rates. Since CACC-enabled vehicle can obtain additional traffic states (including congestion information) via Dedicated Short Range Communication (DSRC), the existing car-following model is to be modified for deriving the behavior of CACC-enabled vehicles dependent on both traffic environment and vehicle states. By using the new CACC algorithm, this paper examines the role of CACC-enabled vehicles in mixed environment on traffic flow. As the proportion of CACC-enabled vehicles increases, it is shown that these changes lead to an increase in traffic mobility while maintaining low susceptibility and mitigating the rate of forming a cluster or jam.
Prior work has shown that while vehicles equipped with Adaptive Cruise Control (ACC) algorithms have the ability to improve traffic flows by increasing the critical density at which traffic jams begin to occur, they remain highly susceptible to the presence of even a few human-driven vehicles. This necessitates a trade-off assessment between improving traffic flow and reducing its susceptibility to presence of human-driven vehicles. In this paper, we address this issue via Cooperative Adaptive Cruise Control (CACC) algorithms that use information communicated via other vehicles or infrastructure. Specifically, the CACC algorithms use a modified form of the General Motors' car-following model, where the driver sensitivity is a function of the size of the existing traffic jam. Analysis using the master equation approach shows that, by altering its response based on the existing traffic state, the following connected vehicle is able to mitigate jam formation for a wide range of vehicular densities. Moreover, since the following connected vehicle can leverage information across larger distances, the `effective' critical density is higher than previous results developed with ACC-enabled vehicles only. The included results indicate that traffic state-dependent CACC algorithms can improve traffic flow, and also hold significant potential to reduce susceptibility to the presence of human-driven vehicles.
Sensor systems typically have not played a significant role in the aircraft conceptual design process. However, with the proliferation of unmanned aerial systems (UAS), the role of sensor systems on vehicle conceptual design can no longer be ignored. This issue is accentuated at smaller scales, where the sensing equipment may place significant constraints on the size, weight and power requirements of the aircraft. In this paper, we present a novel method that incorporates sensor systems into vehicle conceptual design and mission capability analysis. The presented method relies on a simulation framework in which the Blender Game Engine is used to generate complex cluttered flight environments. The MORSE simulator is used to fly vehicles with varying sensor configurations in these cluttered environments in order to assess mission capabilities. Results show the applicability of the presented approach for assessing waypoint navigation mission capabilities as a function of sensor configurations and specifications.
The advent of intelligent vehicle technologies holds significant potential to alter the dynamics of traffic flow. Prior work on the effects of such technologies on the formation of self-organized traffic jams has led to analytical solutions and numerical simulations at the mesoscopic scale, which may not yield significant information about the distribution of vehicle cluster size. Since the absence of large clusters could be offset by the presence of several smaller clusters, the distribution of cluster sizes can be as important as the presence or absence of clusters. To obtain a prediction of vehicle cluster distribution, the included work presents a statistical mechanics-inspired method of simulating traffic flow at a microscopic scale via the generalized Ising model. The results of the microscopic simulations indicate that traffic systems dominated by adaptive cruise control (acc)-enabled vehicles exhibit a higher probability of formation of moderately sized clusters, as compared with the traffic systems dominated by human-driven vehicles; however, the trend is reversed for the formation of large-sized clusters. These qualitative results hold significance for algorithm design and traffic control because it is easier to predict and take countermeasures for fewer large localized clusters as opposed to several smaller clusters spread across different locations on a highway.
Current research methods directed towards measuring the inﬂuence of speciﬁc agents on the dynamics of a large-scale multi-agent system (MAS) rely largely on the notion of controllability of the full-order system, or on the comparison of agent dynamics with a user-deﬁned macroscopic system property. However, it is known that several large-scale multiagent systems tend to self-organize, and their dynamics often reside on a low-dimensional manifold. The proposed framework uses this fact to measure an agent’s inﬂuence on the macroscopic dynamics. First, the minimum embedding dimension that can encapsulate the low-dimensional manifold associated with the self-organized dynamics is identiﬁed using a modiﬁcation of the method of false neighbors. Second, the minimum embedding dimension is used to guide the Krylov subspace-based model order reduction of the system dynamics. Finally, an existing controllability-based metric is applied to the local reducedorder representation to measure an agent’s inﬂuence on the self-organized dynamics. With this technique, one can identify regions of the state space where an agent has signiﬁcant local inﬂuence on the dynamics of the self-organizing MAS. The proposed technique is demonstrated by applying it to the problem of vehicle cluster formation in trafﬁc, a prototypical self-organizing system. As a result, it is now possible to identify regions of the roadway where an individual driver has the ability to inﬂuence the dynamics of a self-organized trafﬁc jam.
This dissertation addresses the issue of inﬂuence in self-organizing multi-agent systems by using trafﬁc jams as a prototypical example of self-organized behavior. Speciﬁcally, the problem of ascertaining the inﬂuence of a set of agents on the ensemble dynamics is addressed through two complementary approaches. In the ﬁrst approach, discussed in Part I of the dissertation, the ability to inﬂuence ensemble dynamics is studied as a function of changing agent population demographics. Statistical mechanics-inspired methodologies, such as the master equation and the generalized Ising model, are used to study the effect of introduction of vehicles equipped with adaptive cruise control (ACC) algorithms on the self-organized dynamics of trafﬁc jams. Results indicate mixed positive and negative effects of introduction of acc-equipped vehicles at various trafﬁc densities. While this approach can help guide long-term intelligent vehicle deployment strategies on the time scale of years or decades, population demographic control is not a feasible solution for inﬂuencing large-scale multi-agent systems on the time scale of minutes or hours. Thus, the second approach, discussed in Part II of this dissertation, addresses the problem by identifying appropriate regions of the state space within which the control efforts exerted by a small set of agents can inﬂuence the self-organized dynamics of the ensemble. The methodologies adopted in this approach make use of the kinematic wave theory of trafﬁc ﬂow and the notion of controllability to present the novel concept of inﬂuential subspaces. Results indicate that there exists a strong spatial dependence that governs an agent’s ability to inﬂuence the self-organized dynamics of large-scale multi-agent systems.
This work introduces the novel concept of an influential subspace, with focus on its application to highway traffic containing connected vehicles. In this context, an influential subspace of a connected vehicle is defined as the region of a highway where the connected vehicle has the ability to positively influence the macrostate, i.e. the traffic jam, so as to dissipate it within a specified time interval. Analytical expressions for the influential subspace are derived using the LighthillWhitham-Richards theory of traffic flow. Included results describe the span of the influential subspace for specific traffic flow conditions and pre-specified driving algorithms of the connected vehicles.
Intelligent vehicles equipped with adaptive cruise control (ACC) technology have the potential to significantly impact the traffic flow dynamics on highways. Prior work in this area has sought to understand the impact of intelligent vehicle technologies on traffic flow by making use of mesoscopic modeling that yields closed-form solutions. However, this approach does not take into account the self-organization of vehicles into clusters of different sizes. Consequently, the predicted absence of a large traffic jam might be inadvertently offset by the presence of many smaller clusters of jammed vehicles. This study - inspired by research in the domain of statistical mechanics - uses a modification of the Potts model to study cluster formation in mixed traffic flows that include both human-driven and ACC-enabled vehicles. Specifically, the evolution of self-organized traffic jams is modeled as a non-equilibrium process in the presence of an external field and with repulsive interactions between vehicles. Monte Carlo simulations of this model at high vehicle densities suggest that traffic streams with low ACC penetration rates are likely to result in larger clusters. Vehicles spend significantly more time inside each cluster for low ACC penetration rates, as compared to streams with high ACC penetration rates.
Self-organizing traffic jams are known to occur in medium-to-high density traffic flows, and it is suspected that adaptive cruise control (ACC) may affect their onset in mixed human-ACC traffic. Unfortunately, closed-form solutions that predict the occurrence of these jams in mixed human-ACC traffic do not exist. In this paper, both human and ACC driving behaviors are modeled using the General Motors fourth car-following model and are distinguished by using different model parameter values. A closed-form solution that explains the impact of ACC on congestion due to the formation of self-organized traffic jams (or “phantom” jams) is presented. The solution approach utilizes the master equation for modeling the self-organizing behavior of traffic flow at a mesoscopic scale and the General Motors fourth car-following model for describing the driver behavior at the microscopic scale. It is found that, although the introduction of ACC-enabled vehicles into the traffic stream may produce higher traffic flows, it also results in disproportionately higher susceptibility of the traffic flow to congestion.
Prior experiments have confirmed that specific GPS-free terrain-based localization algorithms can perform vehicle tracking in real-time on a single road segment at a time. However, the ability of these algorithms to perform vehicle tracking on large road networks, i.e. across intersections and multiple road segments, has not been verified. In this study, it is shown that it is possible to build upon the existing terrain-based localization algorithms to maintain vehicle tracking in large road networks. A set of estimators based on the Unscented Kalman Filter framework is used to track the vehicle in a section of a road network, i.e. across a few road segments and an intersection. A multiple model estimation scheme, based on comparing incoming attitude measurements with a terrain map, is used to identify the road segment that the vehicle is currently traveling over. Experiments indicate that it is possible to maintain vehicle tracking as a vehicle travels across an intersection in a road network.
Prior experiments have confirmed that specific terrain-based localization algorithms, designed to work in GPS-free or degraded-GPS environments, achieve vehicle tracking with tactical-grade inertial sensors. However, the vehicle tracking performance of these algorithms using low-cost inertial sensors with inferior specifications has not been verified. The included work identifies, through simulations, the effect of inertial sensor characteristics on vehicle tracking accuracy when using a specific terrain-based tracking algorithm based on Unscented Kalman Filters. Results indicate that vehicle tracking is achievable even when low-cost inertial sensors with inferior specifications are used. However, the precision of vehicle tracking decreases approximately linearly as bias instability and angle random walk coefficients increase. The results also indicate that as sensor cost increases, the variance in vehicle tracking error asymptotically tends to zero. Put simply, as desired precision increases, increasingly larger and quantifiable investment is required to attain an improvement in vehicle tracking precision.
When engineered items fail, there are often indicators of decay long before the system collapses. This research explores this concept applied to complex vehicles operated in public transportation, and the results can be extrapolated to any vehicle system. Transit bus reliability data gathered from eight transit agencies distributed across the United States are analyzed at a vehicle and subsystem level to identify system failures. An analysis of vehicle subsystem component failures is conducted, where the theory of reliability of repairable systems is applied to the in-transit data to determine if major component failures can be detected by increases in cumulative and subsystem failure rates. Thus, the impact of the research illustrates that major repairs might be detected far in advance of when they are needed.
Self-organizing traffic jams are known to occur in medium-to-high density traffic flows and it is suspected that ACC algorithms may affect their onset in mixed human-ACC traffic flows. Unfortunately, closed-form solutions that predict the statistical occurrence of these jams in mixed traffic do not exist. In this paper, a closed form solution that explains the impact of adaptive cruise control (ACC) on congestion due to the formation of self-organizing traffic jams (or “phantom” jams) is obtained. The master equation approach is selected for developing a model that describes the self-organizing behavior of traffic flow at a mesoscopic scale. The master equation approach is further developed to incorporate driver (or agent) behavior using ACC or car-following algorithms. The behavior for both human-driven and ACC vehicles is modeled using the General Motors‟ fourth model. It is found that while introduction of ACC vehicles into traffic may enable higher traffic flows, it also results in disproportionately higher susceptibility of the traffic flow to congestion.
This thesis describes the analysis of a potential control mechanism for self-organizing systems by studying the specific problem of self-organizing traffic jams. Self-organizing traffic jams are known to occur in medium-to-high density traffic flows. Various techniques for modeling traffic flow are discussed and their advantages and limitations are considered. The master equation approach is selected for developing a model that describes the self-organizing behavior of traffic flow at a mesoscopic scale. The master equation approach is further developed to incorporate driver (or agent) behavior. Control of the self-organizing system is presented via introduction of similar agents with slightly varying interaction properties. The introduction of such agents into a self-organizing system is considered to be analogous to the introduction of vehicles with adaptive cruise control (ACC) into traffic flow. The behavior for both human-driven and ACC vehicles is modeled using the same driver model but with slightly different model parameters. It is found that introduction of a small percentage of agents with slightly different interaction behavior has the potential to affect the dynamics of the self-organizing system. Specifically, it is found that while introduction of ACC vehicles into traffic may enable higher traffic flows, it also results in disproportionately higher susceptibility of the traffic flow to congestion.