Necessary conditions for stability of coupled autonomous vehicles in R are established in this thesis. The focus is on linear arrays with decentralized vehicles, where each vehicle interacts with only a few of its neighbors. Decentralized means that there is no central authority governing the motion. Instead, each vehicle registers only velocity and position relative to itself and bases its acceleration only on those data. Explicit expressions are obtained for necessary conditions for asymptotic stability in the cases that a system consists of a periodic arrangement of two or three different types of vehicles, i.e. configurations as follows: ...2-1-2-1 or ...3-2-1-3-2-1. Previous literature indicated that the (necessary) condition for stability in the case of a single vehicle type (...1-1-1) held that the first moment of certain coefficients of the interactions between vehicles has to be zero. Here, we show that that does not generalize. Instead, the (necessary) condition in the cases considered is that the first moment plus a nonlinear correction term must be zero.

The first generation of autonomous vehicles are equipped with Adaptive Cruise Control (ACC), which automatically adjusts the vehicle speed to maintain a safe following distance and gap selected by the driver. Today’s ACC can also operate at low speeds and signalized intersections on arterial streets. However, the latency of the on-board sensors can significantly increase the start-up lost time and reduce capacity and increase delay on arterials with signalized intersections. This study investigates the fundamental characteristics of traffic flow under ACC vehicles and mixed driving scenarios. Field tests demonstrated that the design of ACC vehicles can lead to delayed response and gradual acceleration when operating on arterials with speed fluctuations due to disturbances. This study also examines the effect of increasing adoption of ACC vehicles at signalized intersections. Field validated simulations suggest that 100% market penetration of ACC vehicles could decrease the capacity by up to 10%. Furthermore, fuel consumption and emissions (CO2, NOx, CO, HC) can increase by up to 33%. / Includes bibliography. / Thesis (MS)--Florida Atlantic University, 2021. / FAU Electronic Theses and Dissertations Collection

Automated driving can help solve the current and future problems of individualtransportation. Automated valet parking is a possible approach to help with overcrowded parking areas in cities and make electric vehicles more appealing. In an automated valet system, drivers are able to drop off their vehicle close to a parking area. The vehicle drives to a free parking spot on its own, while the driver is free to perform other tasks — such as switching the mode of transportation. Such a system requires the automated car to navigate unstructured, possibly three dimensional areas. This goes beyond the scope ofthe tasks performed in the state of the art for automated driving. This thesis describes a visual localization system that provides accuratemetric pose estimates. As sensors, the described system uses multiple monocular cameras and wheel-tick odometry. This is a sensor set-up that is close to what can be found in current production cars. Metric pose estimates with errors in the order of tens of centimeters enable maneuvers such as parking into tight parking spots. This system forms the basis for automated navigationin the EU-funded V-Charge project. Furthermore, we present an approach to the challenging problem of life-long mapping and localization. Over long time spans, the visual appearance ofthe world is subject to change due to natural and man-made phenomena. The effective long-term usage of visual maps requires the ability to adapt to these changes. We describe a multi-session mapping system, that fuses datasets intoiiia single, unambiguous, metric representation. This enables automated navigation in the presence of environmental change. To handle the growing complexityof such a system we propose the concept of Summary Maps, which contain a reduced set of landmarks that has been selected through a combination of scoring and sampling criteria. We show that a Summary Map with bounded complexity can achieve accurate localization under a wide variety of conditions. Finally, as a foundation for lifelong mapping, we propose a relational database system. This system is based on use-cases that are not only concerned with solving the basic mapping problem, but also with providing users with a better understanding of the long-term processes that comprise a map. We demonstrate that we can pose interesting queries to the database, that help us gain a better intuition about the correctness and robustness of the created maps. This is accomplished by answering questions about the appearance and distribution of visual landmarks that were used during mapping. This thesis takes on one of the major unsolved challenges in vision-based localization and mapping: long-term operation in a changing environment. We approach this problem through extensive real world experimentation, as well as in-depth evaluation and analysis of recorded data. We demonstrate that accurate metric localization is feasible both during short term changes, as exemplified by the transition between day and night, as well as longer term changes, such as due to seasonal variation.

No description available.

Automated vehicles (AVs) are becoming more common each day as car manufacturers have started to include advanced driving assistant systems (ADAS) in trendline models. The most basic level of vehicle automation includes Adaptive Cruise Control (ACC) can disrupt and change traffic flow. The current study proposes the development of controlled experiments to obtain traffic flow properties for vehicles equipped with ACC in different scenarios. As part of this dissertation, the effects of ACC on capacity are quantified at steady state conditions, meaning cruising speeds or free flow, and at bottlenecks, where speed fluctuations occur. The effects of ACC on traffic flow properties are also assessed by the construction and study of the Fundamental Diagram. Lastly, the vehicles are submitted to less predictable deceleration scenarios that involve a leading vehicle driven in ACC mode and a leading vehicle driven manually. The reaction of ACC for these cases is documented. / Includes bibliography. / Thesis (M.S.)--Florida Atlantic University, 2021. / FAU Electronic Theses and Dissertations Collection

Adaptive Cruise Control (ACC) vehicles have a longer reaction time, and the on-board sensors have a limited detection range that adversely affects the freeway bottleneck capacity. These limitations can cause small speed fluctuations into larger stop-and-go waves at typical freeway bottlenecks. Microsimulation results revealed that flow instability increases with the increase in ACC market penetration for a single lane freeway. The ACC car following model was developed for higher speed ranges only; thus, it could not capture rapid deceleration to lower speeds, let alone complete stops. The algorithm applies collision avoidance and brake relatively late in those instances, which leads to vehicles clustered closer together when at complete stops (or lower speeds). Therefore, the jam density increases with ACC market penetration. Simulation results also represented that no change in capacity was observed with the introduction of ACC vehicles on a freeway without diverging off-ramp and merging on-ramp demand compared to manually driven vehicles. The result is owed to the fact that lane changes and disturbances are not prominent without merging and diverging sections. However, the situation aggravates more for ACC vehicles when there is diverging off-ramp demand and merging on-ramp demand. The effect becomes severe with the increase of ACC market penetration. The field experiments for the fundamental characteristics of traffic flow showed that maximum capacity can be achieved when all the vehicles are operating in ACC mode. However, that maximum flow is unstable, and a minor speed variation can cause severe capacity drop. The jam density is also more in all ACC scenario that might result in rapid queue propagation as the wave speed is larger compared to the mixed driving scenario. / Includes bibliography. / Thesis (MS)--Florida Atlantic University, 2021. / FAU Electronic Theses and Dissertations Collection

Accessibility plays an important role in a number of scientific fields, and significant advances in measuring accessibility have been made over the past two decades. However, since the comprehensive review of accessibility measures conducted by Geurs and van Wee in 2004, no attempt has been made to update their study. In addition, the emergence of Automated Vehicles (AVs) is expected to dramatically impact accessibility. Therefore, based on the relevant assessment criteria proposed by Geurs and van Wee (2004) (i.e., theoretical basis, interpretability, operationalization, and usability), this research reviews: (1) progress made over the past two decades on measuring accessibility; and (2) how accessibility measures have incorporated the impacts of AVs. A total of 495 papers and books were identified through a search of Scopus, Web of Science, and EBSCOhost in May 2019. The results found that the existing accessibility measures have been further refined, and new measures have been created by leveraging more advanced behavior theories and/or models. In addition, the operationalization of almost all of the measures has become easier due to more readily available data and more advanced implementation tools. As a result of these changes, accessibility measures are becoming more usable and can more accurately assess social, economic, and environmental impacts. However, the interpretation of these measures is becoming more difficult due to the incorporation of more complicated theories and models. Interestingly, very few papers discussed AVs in the context of accessibility measures. Finally, as a result of this study, future research opportunities are identified. / Master of Urban and Regional Planning / The concept of accessibility plays an important role in a number of scientific fields (e.g., transportation planning, environmental conservation, and economic development, etc.), and a change in accessibility can have a direct impact on an individual's quality of life. Transportation accessibility is a function of the connectivity between origins (e.g., a home) and destinations (e.g., a place of employment). Significant advances in measuring accessibility have been made over the past two decades. However, since the comprehensive review of accessibility measures conducted by Geurs and van Wee in 2004, no attempt has been made to update their study. In addition, the emergence of Automated Vehicles (AVs) is expected to dramatically impact accessibility. Therefore, based on the relevant assessment criteria proposed by Geurs and van Wee (2004) (i.e., theoretical basis, interpretability, operationalization, and usability), this research reviews: (1) progress made over the past two decades on measuring accessibility; and (2) how accessibility measures have incorporated the impacts of AVs. The theoretical basis refers to whether an accessibility measure is developed based on solid theories or models, and whether the measure is sensitive to: (a) opportunity changes (e.g., changes in the location of jobs); (b) transport cost changes (e.g., travel time changes); (c) temporal changes (e.g., the change of travel options throughout different times-of-day); and (d) individual changes (e.g., how residents' travel behavior changes due to the emergence of a new subway line). Interpretability refers to how easy an accessibility measure can be explained and understood by planners, engineers, and decision makers. Operationalization refers to how easy it is to use a measure in practice. Finally, usability refers to whether the results of an accessibility measure can be used to assess social, economic, and environmental impacts. A total of 495 papers and books were identified through a search of Scopus, Web of Science, and EBSCOhost in May 2019. The results found that existing accessibility measures have been further refined, and new measures have been created by leveraging more advanced behavior theories and/or models. In addition, the operationalization of almost all of the measures has become easier due to more readily available data and more advanced implementation tools. As a result of these changes, accessibility measures are becoming more usable and can more accurately assess social, economic, and environmental impacts. However, the interpretation of these measures is becoming more difficult due to the incorporation of more complicated theories and models. Interestingly, very few papers discussed AVs in the context of accessibility measures. Finally, as a result of this study, future research opportunities are identified.

Highway systems world wide continue to see an ever increased number of vehicles and subsequently a rise in congested traffic. This results in longer commute times, wasted energy as vehicles idle in stop and go traffic, and increases the risk of accidents. In short, increased congestion costs time and money. These issues have prompted much research into Automated Highway Systems (AHS). In AHS vehicles using computer algorithms can safely travel at much smaller inter-vehicle distances than human drivers are capable of. This increases the capacity of existing highway systems. Sensors aboard each vehicle make this possible by monitoring their surroundings. Vehicles equipped with Adaptive Cruise Control (ACC) are capable of this type of close proximity travel. ACC packages are becoming common as a standard package on many mid-priced vehicles. Another form of automation, Cooperative Adaptive Cruise Control (CACC), which utilizes wireless communication between vehicles, has been proposed and will likely become available within the next couple decades. CACC allows each vehicle to communicate their intended speed or position changes to surrounding vehicles, further decreasing the possibility of collisions. These automation methods are proposed to reduce driver stress, increase highway throughput, and decrease accident rates. However, the fact that vehicles are being automated creates new opportunities for malicious individuals to wreak havoc on society. This research investigates the possibility that some vehicles on the highway might be under the control of malicious individuals who have modified their automated control systems to negatively affect vehicles around them. These malicious actors might also exploit the wireless communication of CACC vehicles and hack their control algorithms, causing them to become unstable. These hacked vehicles could become passive participants in the attack unbeknownst to the driver of the vehicle. The result of such attacks could be congested traffic, rapid changes in acceleration causing drivers discomfort, or multi-vehicle collisions. Such attacks could effectively negate the benefits of implementing AHS. The goal of this work is to bring to light possible weaknesses in the proposed systems so they can be rectified before becoming an issue to the public at large.

In intelligent transportation systems, most of the research work has focused on lane change assistant systems. No existing work considers minimizing the interruption of traffic flow by maximizing the number of lane changes while eliminating the collisions. In this thesis, we develop qualitative and quantitative approaches for minimizing the interruption of traffic flow for three lane scenarios and show that we can extend our approach to any random number of lanes. The algorithm we propose in this thesis is able to achieve the maximum number of lane changes provided that only one vehicle per group (novel concept which is described in this thesis) is allowed to change lanes at a time. Simulation results show that our approach provides much better performance when compared with different lane change algorithms without incurring large overhead, and is hence suitable for online use.

Automated driving systems (ADS) are projected to bring a plethora of benefits to society, such as enhanced road safety and heightened quality of life. However, placing one's trust in the hands of an automated system is still a large concern to society. To facilitate the large-scale adoption of ADSs, they must be stringently tested and evaluated prior to their deployment on public roadways due to their direct impact on the safety of other motorists and vulnerable road users. Currently, no standardized method of quantifying ADS performance exists, so this research project contributes to the evaluation of ADSs by developing and demonstrating a test method that solely characterizes the motion control subsystem of an ADS. The developed test method involved generating representative driving scenarios that exercised both the longitudinal and lateral control elements of an ADS. This method was then demonstrated using two test vehicles with different control system architectures by (1) defining and injecting a ground truth trajectory into the ADS, (2) characterizing the motion control subsystem by quantifying its ability to follow the ground truth path under both nominal conditions and conditions where disturbances were introduced, and (3) analyzing the response of each vehicle to characterize their respective control systems as well as identify differences between the two control architectures. First, a set of representative driving scenarios was created to test the longitudinal and lateral control elements both in isolation and in tandem. Multiple unique design variations of each scenario were created by implementing various target speeds, accelerations, and turning radii that map to both standard and emergency maneuvers. The parameters were set to match naturalistic driving or regulatory requirements identified as part of a literature review. Next, a reference trajectory—the ground truth set of waypoints that define the position and speed of the ADS—was generated for each driving scenario. This reference trajectory was implemented using three methods: recording the waypoint trail of a human driver and creating a synthetic waypoint list mathematically or with CarMaker, a simulation platform for automobile testing (IPG Automotive 2021). Once this step was completed, the reference trajectory was inserted into the ADS to isolate the motion control system and facilitate a repeatable test input. When the test vehicle was under ADS control, the experimenter served as the designated fallback user so they could take control of the vehicle if necessary. Finally, a set of test metrics related to the operation of the ADS (lateral offset, heading error, speed error, longitudinal stop position error, and test completion percentage) were calculated using kinematic data to characterize each motion control system architecture. The analysis of the kinematic metrics for each test scenario demonstrated that the method could effectively evaluate the performance of ADS in various scenarios and highlight the strengths and weaknesses of each system. The control system of Vehicle A consistently lagged in throttle and brake actuation and rounded corners by turning early and with a larger cornering radius. This control system also could not exceed a lateral acceleration of 3.5 m/s2 when under ADS control and limited its yaw rate to keep the lateral acceleration below this level. Consequently, this limitation caused the vehicle to turn wide for radius and speed combinations with a lateral acceleration greater than 3.5 m/s2. On the other hand, the control system of Vehicle B consistently exhibited a small delay before turning and tended to overshoot lane changes at higher lateral accelerations. Regarding disturbances, only the road grade significantly affected the response of both vehicles. / Master of Science / Automated driving systems (ADS) are projected to improve road safety and quality of life, but they must be comprehensively tested and evaluated before their deployment on public roadways. Currently, no standardized method of quantifying ADS performance exists, so this research project contributes to the evaluation of ADSs by developing and demonstrating a test method that identifies limitations of two dissimilar ADS motion controllers. First, real-world driving scenarios were used to test the longitudinal (throttle/brake) and lateral (steering) control elements both in isolation and in tandem. Multiple levels of maneuver harshness were tested to simulate both standard and emergency maneuvers, and these levels were determined by conducting a literature review of human driving behavior. Roadway and vehicle disturbances were also implemented to investigate if the control systems could adjust for external factors. Next, a reference path was generated for each driving scenario using three separate methods, and once this was done, the vehicle attempted to follow the reference path. Finally, a set of test metrics related to the path-following ability of the ADS were calculated, and this data was used to characterize each motion control system architecture. The analysis of metrics demonstrated that the method could effectively evaluate the performance of each control system by highlighting their weaknesses. The control system of Vehicle A consistently accelerated and braked late as well as rounded corners by turning early and with a larger radius. This control system also could not complete harsh cornering maneuvers. On the other hand, the control system of Vehicle B consistently turned late and tended to overshoot lane changes, especially those with a higher speed or harsher steering maneuver. Regarding disturbances, only the road grade significantly affected the response of both vehicles.