Browsing by Subject "Autonomous vehicles"
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Item The autonomous guidance, navigation, and control laboratory at the University of Texas at Austin(2015-12) Lowery, Timothy Vernon; Açıkmeşe, Behçet; Akella, Maruthi RThis report details the design, construction, and contents of the Autonomous Guidance, Navigation, and Control Laboratory (AGNC Lab) for Dr. Behcet Acikmese at the University of Texas. It is intended as a resource for those who are new to the lab or to one of its systems. The lab was created to test --- on real-world platforms --- the control algorithms produced by Dr. Acikmese’s research group. To separate the control problems from other engineering challenges of autonomous vehicles, the lab uses an optical motion capture system which can relay vehicle's their position and orientation. To support hardware development, the lab houses a full compliment of hand tools, electronics equipment, and a 3D extrusion printer. The primary research vehicle is the quadrotor, selected for its mechanical simplicity, aerial agility, and recent ubiquity. Through the testing of several quadrotors, my group found existing platforms did not fulfill our need for small size and weight, outdoor flight, payload capacity, and computational power. In response, we designed a custom quadrotor and autopilot. The vehicle flies safely indoors, confidently outdoors, and with a payload of up to half its own mass. The autopilot is based on an ARM microprocessor, leaving ample overhead for our group's algorithms, and can easily add new functionality with breakout boards.Item Autonomous intersection management(2009-12) Dresner, Kurt Mauro; Stone, Peter, 1971-; Porter, Bruce W.; Waller, S T.; Kuipers, Benjamin J.; Veloso, Manuela M.Artificial intelligence research is ushering in an era of sophisticated, mass-market transportation technology. While computers can fly a passenger jet better than a human pilot, people still face the dangerous yet tedious task of driving. Intelligent Transportation Systems (ITS) is the field focused on integrating information technology with vehicles and transportation infrastructure. Recent advances in ITS point to a future in which vehicles handle the vast majority of the driving task. Once autonomous vehicles become popular, interactions amongst multiple vehicles will be possible. Current methods of vehicle coordination will be outdated. The bottleneck for efficiency will no longer be drivers, but the mechanism by which those drivers' actions are coordinated. Current methods for controlling traffic cannot exploit the superior capabilities of autonomous vehicles. This thesis describes a novel approach to managing autonomous vehicles at intersections that decreases the amount of time vehicles spend waiting. Drivers and intersections in this mechanism are treated as autonomous agents in a multiagent system. In this system, agents use a new approach built around a detailed communication protocol, which is also a contribution of the thesis. In simulation, I demonstrate that this mechanism can significantly outperform current intersection control technology-traffic signals and stop signs. This thesis makes several contributions beyond the mechanism and protocol. First, it contains a distributed, peer-to-peer version of the protocol for low-traffic intersections. Without any requirement of specialized infrastructure at the intersection, such a system would be inexpensive and easy to deploy at intersections which do not currently require a traffic signal. Second, it presents an analysis of the mechanism's safety, including ways to mitigate some failure modes. Third, it describes a custom simulator, written for this work, which will be made publicly available following the publication of the thesis. Fourth, it explains how the mechanism is "backward-compatible" so that human drivers can use it alongside autonomous vehicles. Fifth, it explores the implications of using the mechanism at multiple proximal intersections. The mechanism, along with all available modes of operation, is implemented and tested in simulation, and I present experimental results that strongly attest to the efficacy of this approach.Item Autonomous vehicles : land use implications for Austin, Texas(2015-08) Palmer, Rebekah Mae; Wegmann, Jake; Jiao, JunfengAutonomous vehicles are said to be a disruptive technology that will transform the way we live in coming decades. Drawing from the historical context of conventional vehicles and their subsequent transformation of land use development patterns, this paper seeks to understand the ancillary implications of such advances in transport. I assert the argument that Austin will be amongst the first cities to experience these shifts due to its history of economic development strategy, large populous of technology 'first-adopters,' the city's struggle to accommodate rapid growth, and Austin's context within Texas' business-friendly regulatory environment. The literature review aims to cover a broad, high-level view of the current status of autonomous vehicle development and provide context for how the academy is researching the possibilities for autonomous vehicle commercialization. A second portion of this report summarizes the views of Austin-based traffic engineers, transit researchers, attorneys, and other experts serving on various policy advisory councils in Austin, Travis County, and Central Texas.Item A delayed response policy for autonomous intersection management(2010-08) Shahidi, Neda; Stone, Peter, 1971-; Julien, ChristineThe DARPA Urban Challenge in 2007 showed that fully autonomous vehicles, driven by computers without human intervention on public roads, are technologically feasible with current intelligent vehicle technology [6]. Some researchers predict that within 5-20 years there will be autonomous vehicles for sale on the automobile market. Therefore, the time is right to rethink our current transportation infrastructure, which is primarily designed for human drivers, not autonomous vehicles. The Autonomous Intersection Management (AIM) project at UT Austin aims to propose a large-scale, real-time framework to be a substitute for current traffic light and stop signs. Automobiles in modern urban settings spend a lot of time idling at intersections. In 2007, US drivers wasted 4.16 billion hours of their time and 2.81 billion gallons of gas in congestion, costing a total of 87.2 billion dollars nationwide [18]. A big portion of this waste takes place at intersections. The AIM project is able to utilize the capacity of intersections to minimize time waste and fuel consumption. The fundamental idea of Autonomous Intersection management (AIM) [13] is a reservation system in which cells in space-time will be reserved by the au- tonomous vehicles based on their trajectories. An intersection manager takes care of the reservation as well as communication with the vehicles. This mechanism tries to maximize the usage of the intersection area. It ensures a collision free intersection as well. The main question of this project is what intersection control mechanism is appropriate for reducing an autonomous vehicle's waiting time and improving the throughput of the intersection. Previous work proposed the first-come-first-served (FCFS) policy in which the reservation requests are served as soon as they are received. The results of simulation show that FCFS outperforms the current traffic systems, traffic light and stop sign, by orders of magnitude. We, however, observe that FCFS performs suboptimal in certain traffic patterns that are pretty common in urban settings. In this project, first we study the limitations of FCFS, then develop a more efficient policy to alleviate these limitations. The idea that we examined is a systematic policy of granting reservations that have the objective of minimizing the cost of delaying vehicles. In an attempt to build the system in reality, we used miniature robots called Eco-be. Due to their cost and size, Eco-bes are good candidates for testing a multi-agent system with a large number of agents. In spite of the fact that the physical challenges of Eco-bes do not perfectly match those of full size autonomous vehicles, they are still useful for demonstration and education purposes as well as for the study of collisions for which experiments with full size vehicles are costly and dangerous.Item The future of fully automated vehicles : opportunities for vehicle- and ride-sharing, with cost and emissions savings(2014-08) Fagnant, Daniel James; Kockelman, KaraFully automated or autonomous vehicles (AVs) hold great promise for the future of transportation, with Google and other auto manufacturers intending on introducing self-driving cars to the public by 2020. New automation functionalities will produce dramatic transportation system changes, across safety, mobility, travel behavior, and the built environment. This work’s results indicate that AVs may save the U.S. economy up to $37.7 billion from safety, mobility and parking improvements at the 10% market penetration level (in terms of system-wide vehicle-miles traveled [VMT]), and up to $447.1 billion with 90% market penetration. With only 10% market share, over 1,000 lives could be saved annually. However, realizing these potential benefits while avoiding pitfalls requires overcoming significant barriers including AV costs, liability, security, privacy, and missing research. Additionally, once fully self-driving vehicles can safely and legally drive unoccupied, a new personal travel transportation mode looks set to arrive. This new mode is the shared automated vehicle (SAV), combining on-demand service features with self-driving capabilities. This work simulates a fleet of SAVs operating within Austin, Texas, first using an idealized grid-based representation, and next using Austin’s actual transportation network and travel demand flows. This second model incorporates dynamic ride-sharing (DRS), allowing two or more travelers with similar origins, destinations and departure times to share a ride. Model results indicate that each SAV could replace around 10 conventionally-owned household vehicles while serving over 56,000 person-trips. SAVs’ ability to relocate unoccupied between serving one traveler and the next may cause an increase of 7-10% more travel; however, DRS can result in reduced overall VMT, given enough SAV-using travelers willing to ride-share. Furthermore, using DRS results in overall lower wait and service times for travelers, particularly from pooling rides during peak demand. SAVs should produce favorable emissions outcomes, with an estimated 16% less energy use and 48% lower volatile organic compound (VOC) emissions, per person-trip compared to conventional vehicles. Finally, assuming SAVs cost $70,000 each, an SAV fleet in Austin could provide a 19% return on investment, when charging $1 per trip-mile served. In summary, this new paradigm holds much promise that technological advances may soon realized.Item Integrating autonomous vehicle behavior into planning models(2015-05) Levin, Michael William; Boyles, Stephen David, 1982-; Kockelman, Kara MAutonomous vehicles (AVs) may soon be publicly available and are expected to increase both network capacity and travel demand. Reduced safety margins from computer precision may increase network capacity and allow for more efficient intersection controls. AVs also offer the option of repositioning trips to avoid parking fees or share the vehicle between household members, which may increase the total number of vehicle trips and decrease the relative utility of transit. Since AVs may be available within one or two decades, which is within the span of long-term planning models, practitioners may soon wish to predict the effects of AVs on traffic networks. This thesis modifies the four-step planning model commonly used by practitioners to include AV behaviors and capacity improvements. Because dynamic traffic assignment (DTA) offers more realistic flow propagation and intersection control options, the four-step model is modified to incorporate DTA with endogenous departure time choices. To facilitate modeling of AV intersections, the tile-based reservation (TBR) control policy is simplified into a conflict region (CR) model compatible with general simulation-based DTA and with greatly improved computational tractability. Results suggest that although the total number of personal-vehicle trips may almost double (due to repositioning trips to the origin to avoid parking costs), increases in network and intersection capacity can mostly offset or even improve network conditions. Use of dynamic flow propagation instead of static travel time functions in the four-step model results in predictions of increased average travel speed although both static and dynamic planning models predict a high reliance on repositioning trips (i.e., empty-vehicle travel). To study AV behaviors in DTA, this thesis first integrates DTA into the four-step model with the addition of departure time choice. This model alone may be useful for practitioners as departure time modeling is a major concern with DTA planning models. Also, the TBR intersection policy has only been studied in micro-simulation with heuristic routing strategies. The CR model opens this new technique to study under UE behavior, which is the first step for the bridge between technology demonstration simulations to models practitioners can use to evaluate implementation. . Therefore, the models developed here for the purposes of predicting AV trip and mode choices may themselves become useful tools for other applications.Item Management of a shared, autonomous, electric vehicle fleet : vehicle choice, charging infrastructure & pricing strategies(2015-08) Chen, Tong Donna; Kockelman, Kara; Machemehl, Randy; Boyles, Stephen; Stone, Peter; Baldick, RossThere are natural synergies between shared autonomous vehicle (AV) fleets and electric vehicle (EV) technology, since fleets of AVs resolve the practical limitations of today's non-autonomous EVs, including traveler range anxiety, access to charging infrastructure, and charging time management. Fleet-managed AVs relieve such concerns, managing range and charging activities based on real-time trip demand and established charging-station locations, as demonstrated in this paper. This work explores the management of a fleet of shared autonomous (battery-only) electric vehicles (SAEVs) in a regional (100-mile by 100-mile) discrete-time, agent-based model. The dissertation examines the operation of SAEVs under various vehicle range and charging infrastructure scenarios in a gridded city modeled roughly after the densities of Austin, Texas. Results indicate that fleet size is sensitive to battery recharge time and vehicle range, with each 80-mile range SAEV replacing 3.7 privately owned vehicles and each 200-mile range SAEV replacing 5.5 privately owned vehicles, under Level II (240-volt AC) charging. With Level III 480-volt DC fast-charging infrastructure in place, these ratios rise to 5.4 vehicles for the 80-mile range SAEV and 6.8 vehicles for the 200-mile range SAEV. However, due to the need to travel while "empty" for charging and passenger pickup, SAEV fleets are predicted to generate an additional 7.1 to 14.0% of travel miles. Financial analysis suggests that the combined cost of charging infrastructure, vehicle capital and maintenance, electricity, insurance, and registration for a fleet of SAEVs ranges from $0.42 to $0.49 per occupied mile traveled, which implies SAEV service can be offered at the equivalent per-mile cost of private vehicle ownership for low-mileage households, and thus be competitive with current manually-driven carsharing services and significantly less expensive than on-demand driver-operated transportation services. The mode share of SAEVs in the simulated mid-sized city is predicted to be between 14 and 39%, when competing against privately-owned, manually-driven vehicles and city bus service. This assumes SAEVs are priced between $0.75 and $1.00 per mile, which delivers significant net revenues to the fleet owner-operator, under all modeled scenarios, assuming 80-mile-range EVs and remote/cordless Level II charging infrastructure and $10,000-per-vehicle automation costs.