01403nas a2200145 4500008004100000245006100041210006100102260000800163520095900171100001901130700001901149700002001168700002301188856004601211 2021 eng d00aSafer Adaptive Cruise Control for Traffic Wave Dampening0 aSafer Adaptive Cruise Control for Traffic Wave Dampening bACM3 a

This project aims to develop an adaptive cruise controller for vehicles at low speeds in stop-and-go traffic. Current adaptive cruise controllers can use RADAR sensors to follow a vehicle at high speeds (greater than 18 mph), but reach their limits if the lead vehicle’s velocity dips below threshold, requiring the driver of the host vehicle to resume control over the car’s speed. Some cruise controllers adapt to stop-and-go traffic, but these are mostly experimental and have yet to see widespread commercial implementation. These experimental models often have issues because of their limited data; consequently, the acceleration and deceleration can be jarring and uncomfortable to passengers. In contrast, because of our reliable sensor data, and the sensor configuration unique to the CAT Vehicle, our cruise controller will be capable of following cars at low speeds and functioning continuously, even when the car is stopped. 

1 aBaschab, Emily1 aBall, Savannah1 aVazzana, Audrey1 aSprinkle, Jonathan uhttp://dx.doi.org/10.1145/3450267.345200302204nas a2200169 4500008004100000245006100041210006100102520163400163653002801797653001601825653002101841100001901862700001901881700002001900700002301920856009101943 2020 eng d00aSafer Adaptive Cruise Control for Traffic Wave Dampening0 aSafer Adaptive Cruise Control for Traffic Wave Dampening3 a

Our goal is to develop an adaptive cruise controller for vehicles at low speeds in stop-and-go traffic. Current adaptive cruise controllers can use radar sensors to follow a vehicle at high speeds (greater than 18 mph), but reach their limits if the lead vehicle’s velocity dips below threshold, requiring the driver of the host vehicle to resume control over the car’s speed. Some cruise controllers adapt to stop-and-go traffic, but these are mostly experimental and have yet to see widespread commercial implementation. These experimental models often have issues because of their limited data; consequently, the acceleration and deceleration can be jarring and uncomfortable to passengers. In contrast, because of our reliable sensor data, and the sensor configuration unique to the CAT Vehicle, our cruise controller will be capable of following cars at low speeds and functioning continuously, even when the car is stopped.

This project has the potential to interest automobile companies who could implement this technology in future automobiles. If our technology were to be implemented in future automobiles, it would make driving considerably more convenient for drivers. This technology could also potentially reduce the number of traffic accidents, as well as making drivers feel safer when navigating traffic. However, if errors were to occur, they could potentially put the car’s passengers at risk, as well as the passengers in nearby vehicles.

Our project had a time frame of ten weeks during which we were able to model an adaptive cruise controller and test it in a simulation.

10aAdaptive Cruise Control10astop-and-go10aVehicle Autonomy1 aBaschab, Emily1 aBall, Savannah1 aVazzana, Audrey1 aSprinkle, Jonathan uhttps://csl.arizona.edu/content/safer-adaptive-cruise-control-traffic-wave-dampening-000446nas a2200133 4500008004100000245006700041210006600108260001200174100002600186700001700212700001800229700001300247856005200260 2019 eng d00aAutonomous vehicles: From vehicular control to traffic control0 aAutonomous vehicles From vehicular control to traffic control c12/20191 aMonache, M., L. Delle1 aSprinkle, J.1 aVasudevan, R.1 aWork, D. uhttp://dx.doi.org/10.1109/CDC40024.2019.902953501841nas a2200169 4500008004100000245005800041210005800099260004700157520127100204100002201475700001901497700001901516700001501535700002001550700001601570856008501586 2018 eng d00aCognitive Engine Testbed for Vehicular Communications0 aCognitive Engine Testbed for Vehicular Communications aTucson, ArizonabThe University of Arizona3 a

Ad-hoc networks have the potential to increase the safety and reliability of autonomous vehicles. The amount of radio spectrum available for such networks is limited, however. The use of cognitive radio, especially when integrated with reinforcement learning algorithms, may help to ease the issue of limited spectrum by finding optimal transmission policies and detecting the presence of other users, especially in a scenario where a Primary User and Secondary User are contesting for spectrum. This paper presents a testbed for simulating cognitive engines in these networks using a variety of reinforcement learning algorithms, including -greedy, Softmax Strategy, and Q-Learning. The goal of these cognitive engines is to learn to choose the best modulation and coding rates given various channel conditions and a user-defined optimization goal (i.e. maximize throughput, minimize bit error rate). The cognitive engine then learns the optimal coding rates and modulation schemes for the given environment, and the testbed displays a visual of the performance of the cognitive engine at each time step. In the case of Q-learning in a Primary User vs. Secondary User scenario, the cognitive engine also learns to choose the best channel for transmission.

1 aAnderson, William1 aAponik, Ashley1 aDaoud, Youssef1 aTeku, Noel1 aVanhoy, Garrett1 aBose, Tamal uhttp://csl.arizona.edu/content/cognitive-engine-testbed-vehicular-communications01844nas a2200145 4500008004100000245005400041210005400095260006600149520131100215100002101526700001701547700001701564700002001581856009701601 2017 eng d00aValidation of a CRV Model Using TVWS Measurements0 aValidation of a CRV Model Using TVWS Measurements aSan Diego, CaliforniabThe Wireless Innovation Forumc11/20173 a

As autonomous vehicles advance, their commercial popularity will rise. This growth will increase the need for wireless transmission of data to these cars not only to drive more efficiently, but to also entertain the driver. As people are freed from the task of driving, the demand for in-car internet applications, such as Netflix or Skype, will grow. Currently, autonomous vehicles are allowed to transmit using the band specified by the IEEE protocol 802.11p. While vehicles can transmit data using the 5.9 GHz band (5.850-5.925 GHz), the band may not support wireless transmission of media to vehicles’ infotainment systems. This requires an alternative. With the switch from analog to digital television, the government has vacated the analog TV bands. These bands provide a possible solution to the limitations of 802.11p transmissions. The vacated space is called TV white space. One proposed use of this white space is to provide Wi-Fi. This idea has been called White-Fi. According to our research, researchers have measured whether the specific frequencies are occupied but do not provide the unprocessed data. With this in mind, we measure the occupancy of the TV white space and we simulate how a network using this band would perform under the multiple scenarios of everyday driving.

1 aAnderson, Khalil1 aLusk, Lauren1 aHands, Marti1 aVanhoy, Garrett uhttps://www.wirelessinnovation.org/assets/Proceedings/2017/winncomm%202017%20proceedings.pdf02203nas a2200193 4500008004100000245004600041210004600087260002400133300001000157520163100167100002201798700002101820700001701841700001701858700001701875700002301892700001601915856007801931 2016 eng d00aPower Efficient Vehicular Ad Hoc Networks0 aPower Efficient Vehicular Ad Hoc Networks aReston, VAc03/2016 a26-313 aInter-vehicular communication is a growing plat- form for improving roadway safety. The highly mobile nature of Vehicle to Vehicle communications causes rapid changes in network topologies and propagation conditions. Since the advent of Vehicular Ad-Hoc Networks (VANETs), over fifty routing protocols with attendant topologies have been proposed. Despite these protocols’ merits, many of them are not optimized for power management and frequency reuse. Our approach utilizes the one dimensional dynamic of divided highways to simplify the routing problem and reduce energy consumption. Since each car is aware of only two types of connections, up-road and down-road, we can form low power, line of sight links between adjacent vehicles. We also utilize a fuzzy logic algorithm that predicts the location of up-road cars to reduce interference from request for link signals. Once these links have been established, up-road vehicles send data down-road for a length of time based on the relative speed of the two vehicles. After this time period has expired the down-road vehicle must request additional information, restarting the timer. Data sent through the network will include information on up-road vehicles, and when required, messages such as accident notifications, alerts, and traffic warnings. Through simulation, we show that our approach to VANETs maintains its update frequency despite bumper to bumper traffic and uses two to five orders of magnitude less power than an IEEE 802.11 network with clustering and 1 mW transmit power. Overall, the network performs well and is a viable improvement to the standard.1 aHolcomb, Sterling1 aKnowlton, Audrey1 aGuerra, Juan1 aAsadi, Hamed1 aVolos, Haris1 aSprinkle, Jonathan1 aBose, Tamal uhttps://csl.arizona.edu/content/power-efficient-vehicular-ad-hoc-networks04605nas a2200169 4500008004100000245006000041210006000101260002700161520403200188100001904220700001504239700003004254700002404284700001704308700002304325856008704348 2015 eng d00aAdaptive Multifactor Routing with Constrained Data Sets0 aAdaptive Multifactor Routing with Constrained Data Sets aSan Diego, CAc03/20153 a

Autonomous Vehicles can benefit greatly from the use of cellular infrastructure. Consequently, it may be desirable at times to consider the availability of this infrastructure when planning autonomous vehicle routes. In order to make such decisions it is necessary to have up-to-date knowledge of signal strength in surrounding areas. We consider the quality of routing possible when using incomplete knowledge of signal strength along a route. As our motivation for how signal strength information would be constrained we consider Vehicle to Vehicle communications. Such communications offer great promise in creating real-time signal maps through a decentralized data collection and aggregation process. One might envision such a process involving the transfer of signal reception information between cars within an area. Such a process, while low-latency and low-cost, could suffer from limited data availability beyond relatively short ranges. In order to route based on signal strength we employ a weighting formula to combine distance and signal strength into a single cost quantity. Then, we apply this formula to a city grid map with signal strength information. We replace the distance values with the formula’s aggregate cost values. The cost values are then presented to a shortest path routing algorithm to determine the lowest cost path. Finally, we simulate a vehicle which regularly updates a signal map of its surroundings and continuously updates its route in response. The costs of its chosen path and the cost of the ideal path are then recorded. In order to rigorously test our routing formula’s performance with varying degrees of information we employ a Matlab program that randomly generates thousands of city signal maps to run the routing formula and algorithms on. The routing algorithms are run with route signal knowledge between 0% and 100%. We chart the average ratio between partial knowledge and full knowledge path costs. We consider the performance of a variety of algorithms and conclude that using Dijkstra we may produce routes that are 95% optimal using a signal knowledge window only 1/10 of our total route size. These results indicate the potential for excellent routing even when V2V communication can only offer highly constrained data sets. However, the use of random maps potentially weakens our results as real world signal maps tend to be patterned and non-random. Such patterns are extremely problematic as there is great potential for scenarios that do not occur frequently in a randomly generated map and that require extensive map knowledge. For example, the need to find an exit to a signal dead-zone One might view our randomized maps as presenting the signal knowledge limited algorithm a series of local minima optimization problems. In a random map with extremely high signal value variance there is likely to be a variety of immediately visible signal dead-zones and strong signal zones that all have small sizes and are consistently intermixed. As a result, a knowledge limited algorithm can easily avoid a high cost path by moving between small strong signal zones while avoiding the interspersed dead zones with little advanced knowledge. We expect that the most relevant metric for signal formula routing performance may be the ratio between the median size of extreme signal zones and the size of the signal knowledge window. This ratio directly determines the ability of the path cost minimization algorithm to solve path cost as a series of local minima. In order to test the potential value of this metric we tweak our random map generation algorithm to assign a random signal values to increasing numbers of intersections. For example, we might assign each random value to 4x4 intersection grids. Then, we would apply that single value to 8x8 grids. This modification allows us to directly alter the size of extreme signal zones and test the performance of the routing algorithm as extreme signal zones outsize the size of the signal knowledge window

1 aMiller, Torger1 aRoss, Cody1 aBarbosa, Matheus, Marques1 aHirzallah, Mohammed1 aVolos, Haris1 aSprinkle, Jonathan uhttps://csl.arizona.edu/content/adaptive-multifactor-routing-constrained-data-sets03427nas a2200157 4500008004100000245008400041210006900125520286200194100001903056700001903075700001703094700001703111700002303128700001603151856010203167 2015 eng d00aIntersection Management via the Opportunistic Organization of Platoons by Route0 aIntersection Management via the Opportunistic Organization of Pl3 aIntersections behave as a pathway for traffic redirection and play an important role in the general movement of traffic, because of this, traffic congestion is not desirable and methods to remove congestion or its effects have been implemented. Generally, congestion occurs when a group of vehicles that is large relative to available road space attempts to move through an area that cannot redirect the group effectively. The are of this paper’s focus is the intersection. Some methods currently implemented to control congestion are roadway expansion, the use of adaptive traffic lights, and the use of public transportation. Good short term solutions, they only postpone the occurrence of congestion and are also costly. One way to deal with congestion is have vehicles cooperate with each other and their intersections through means of wireless communication. The vehicles can then be organized by a management system in such a way that vehicle throughput at an intersection can be maintained even at high levels of traffic congestion. A requirement of the system proposed in this paper is that all vehicles behave autonomously in order to obtain easily predictable behavior. The following describes the results of such a simulation of such a system. The data gathered over four simulations show that a higher platoon depth causes a general decrease in delay. The results between signalized and un-signalized simulations were vastly different with the un-signalized having significantly better results. The delay at a platoon depth of 9, for the unsignalized intersection, was -1.8, this brings the ideal delay to 98.2 seconds down from 100 seconds. This was due to the way intersection merging and gap threading is performed and is valid for several reasons. Because this method of intersection management desires to decrease the delay in traffic as much as possible, it would not make sense to slow down a platoon in order to perform a maneuver. The signalized method performed significantly worse than the un-signalized due to how the signalized method was implemented. Only one traffic light was green at a time, hence, whenever a platoon was stopped at intersection, that platoon would be guaranteed a delay of at most 56 seconds, which is how long a red light lasts for one signal. Though inefficient signal timing was used, we hypothesize that the behavior of a right of way system, such as a traffic light, is fundamentally flawed. By delegating the right of way to only a few lanes at a time, it is guaranteed that some vehicles will be stopped and will therefore experience delay. In order to show that the capacity of signalized intersection management through the use of traffic lights falls short when compared to un-signalized autonomous intersection management, better traffic light control methods will need to be tested in the future.1 aHeras, Alberto1 aClaytor, Lykes1 aVolos, Haris1 aAsadi, Hamed1 aSprinkle, Jonathan1 aBose, Tamal uhttps://csl.arizona.edu/content/intersection-management-opportunistic-organization-platoons-route00416nas a2200157 4500008004100000245001800041210001800059260001300077300001200090490000900102100002300111700002000134700002100154700001900175856006400194 2010 eng d00aMetamodelling0 aMetamodelling bSpringer a59–780 v61001 aSprinkle, Jonathan1 aRumpe, Bernhard1 aVangheluwe, Hans1 aKarsai, Gábor uhttp://www.springer.com/computer/swe/book/978-3-642-16276-300494nas a2200169 4500008004100000245003500041210003500076260001300111300001400124490000900138100002700147700002000174700002200194700002300216700002100239856006400260 2010 eng d00aModel Evolution and Management0 aModel Evolution and Management bSpringer a243–2720 v61001 aLevendovszky, Tíhamer1 aRumpe, Bernhard1 aSchätz, Bernhard1 aSprinkle, Jonathan1 aVangheluwe, Hans uhttp://www.springer.com/computer/swe/book/978-3-642-16276-300607nas a2200169 4500008004100000245005100041210005000092260005600142100003300198700001900231700001900250700002300269700001900292700002600311700001700337856008300354 2002 eng d00aComputer-aided aircraft maintenance scheduling0 aComputeraided aircraft maintenance scheduling a2015 Terrace PlacebVanderbilt UniversitycNovember1 avan Buskirk, Christopher, P.1 aDawant, Benoit1 aKarsai, Gábor1 aSprinkle, Jonathan1 aSzokoli, Gabor1 aSuwanmongkol, Karlkim1 aCurrer, Russ uhttps://csl.arizona.edu/content/computer-aided-aircraft-maintenance-scheduling00545nas a2200193 4500008004100000245005000041210004900091260001300140300001200153490000700165100002000172700001800192700001900210700002100229700002000250700002300270700001900293856003900312 2001 eng d00aComposing Domain-Specific Design Environments0 aComposing DomainSpecific Design Environments cNovember a44–510 v341 aLédeczi, Ákos1 aBakay, Árpad1 aMaroti, Miklos1 aVolgyesi, Péter1 aNordstrom, Greg1 aSprinkle, Jonathan1 aKarsai, Gábor uhttp://dx.doi.org/10.1109/2.96344300554nas a2200181 4500008004100000245003300041210003000074260004200104100002000146700001900166700001800185700002000203700002300223700002000246700002300266700002100289856006200310 2001 eng d00aGME 2000 Users Manual (v2.0)0 aGME 2000 Users Manual v20 bVanderbilt University, ISIScDecember1 aLédeczi, Ákos1 aMaroti, Miklos1 aBakay, Árpad1 aNordstrom, Greg1 aGarrett, Jason, T.1 aThomason, Chuck1 aSprinkle, Jonathan1 aVolgyesi, Péter uhttps://csl.arizona.edu/content/gme-2000-users-manual-v2000404nas a2200145 4500008004100000245003100041210003100072260001200103300001400115490000600129100002300135700003300158700001900191856004800210 2000 eng d00aModeling Agent Negotiation0 aModeling Agent Negotiation cOctober a454–4590 v11 aSprinkle, Jonathan1 avan Buskirk, Christopher, P.1 aKarsai, Gábor uhttp://dx.doi.org/10.1109/ICSMC.2000.885034