Autonomous driving technology is continuously evolving at an accelerated pace. The road environment is always uncertain, which requires an evasive manoeuvre that an autonomous vehicle can take. This evasive behaviour to avoid accidents in a critical situation is analogous to autonomous racing that operates at the limits of stable vehicle handling. In autonomous racing, the vehicle must operate in highly nonlinear operating conditions such as high-speed manoeuvre on sharp turns, avoiding obstacles and slippery road conditions. These dynamically changing racing situations require advanced path planning systems with obstacle avoidance executed in real-time. Therefore, the motion planning problem for autonomous racing is analogous to safe and reliable autonomous vehicle operation in critical situations. This thesis project evaluates the application of traction adaptive motion planning to autonomous racing on different road surfaces for a small-scale test vehicle in real-time. The evaluation is based on a state-of-the-art algorithm that uses a combination of optimization, trajectory rollout, and constraint adaption framework called "Sampling Augmented Real-Time Iteration (SAARTI)". SAARTI allows motion planning and control with respect to time-varying vehicle actuation capabilities while taking locally adaptive traction into account for different parts of the track as a constraint. Initially, the SAARTI framework is adapted to work with the SmallVehicles-for-Autonomy (SVEA) system; then, the whole system is simulated in a ROS (Robot Operating System) based SVEA simulator with a Hardware-in-the-loop setup. Later, the same setup is used for the real time experiments that are carried out using the SVEA vehicles, and the different critical scenarios are tested on the SVEA vehicle. The emphasis was given to the experimental results; therefore, the results also consider computationally intensive localization inputs while the motion planner was implemented in real-time instead of a simulation setup. The experimental results showed the impact of planning motions according to an approximately correct friction estimate when the friction parameter was close to the actual value. The results indicated that the traction variation had indeed affected the lap time and trajectory taken by the test vehicle. The lap time is affected significantly when the coefficient of friction value is far away from the real friction coefficient. It is observed that the lap time increased significantly at higher values of friction coefficient, when involving more excessive over-estimation of the traction, leading to the oscillatory motion and lane exits. Furthermore, the non-adaptive case scenario result shows that the test vehicle performed better when given friction parameter inputs to the algorithm approximately equal to the real friction value. / Teknik för autonom körning har utvecklats i snabb takt de senaste åren. Trafikmiljön innehåller många källor till osäkerhet, vilket ibland kräver undanmanövrar av det autonoma fordonet. Undanmanövrar i kritiska situationer är analoga med autonom racing i det avseendet att fordonet opererar nära gränsen av dess fysiska förmåga. I autonom racing måste fordonet fungera i hög grad olinjära driftsförhållanden som höghastighetsmanöver i skarpa svängar, undvika hinder och halt väglag. Dessa dynamiska föränderliga racingsituationer kräver avancerad vägplaneringssystem med undvikande av hinder exekveras i realtid. Därför är rörelseplaneringsproblemet för autonom racing är analogt med det för säkra undanmanövrer i kritiska situationer. Detta examensarbete utvärderar tillämpningen av dragkraft adaptiv till autonom racing på olika väglag för ett småskaligt testfordon i realtid. Utvärderingen baseras på en algoritm som kallas "Sampling Augmented Real Time Iteration (SAARTI)" som tillåter rörelse planering och kontroll med avseende på tidsvarierande fordonsdynamik, på så vis tar algoritmen hänsyn till lokalt varierande väglag. Arbetet började med att integrera SAARTI-ramverket med testplattformen Small-Vehicles-for-Autonomy (SVEA). Därefter utfördes hardware-in-the-loop simuleringar i ROS (Robot Operating System), och därefter utfördes fysiska tester med SVEA plattformen. Under experimenten kördes SAARTI-algoritmen parallellt med en beräkningsintensiv SLAM-algoritm för lokalisering. De experimentella resultaten visade att adaptiv rörelseplanering kan avhjälpa problemet med lokalt varierande väglag, givet att den uppskattade friktionsparametern är approximativt korrekt. Varvtiden påverkas negativt när friktionsskattningen avviker från den verkliga friktionskoefficienten. Vidare observerades att varvtiden ökade vid höga värden på den skattade friktionsparametern, vilket gav upphov till mer aggressiva manövrer, vilket i sin tur gav upphov till oscillerande rörelser och avåkningar.

To monitor and control maritime autonomous surface ships in a land­based remote operations centre leads to several advantages compared to today when a whole crew is needed on board. For example, less humans would be required to control the same amount of ships, less personnel would be required to live or stay on the ship for a longtime and risk being injured or die in accidents or storms. To facilitate and ease a remote operators work, a risk contour could be a valuable tool and utility. A risk contour is a visual tool on the user interface used by the remote operator and illustrates an area around the ship. When a risk contour collides with another objector another risk contour, a warning notifies the operator of a dangerous situation. In case the operator has multiple monitors, the warning informs the operator to focus on the concerned monitor and Maritime Autonomous Surface Ship (MASS). In this project, risk contours have been researched through a background study, literature study and a case study. In the case study the risk contours were tested in a simulation environment by experienced test persons in the field. The test data was analysed and the opinion of the testers were gathered through simulations and reviewed with questions. The data from the testing showed that the risk contour affected the tester by increasing their risk awareness by increasing distances to other objects or ships and made them act earlier in riskful situations. The feedback from the simulation and the interviews regarding the proposed risk contour was overwhelmingly positive. Understanding the risk contour and the changed risk contour in the different scenarios were highly rated on scales from the participants. The conclusions from the thesis were that wind and currents are the two most relevant factors when studying environmental factors affecting the position of a MASS. The conclusion about which mechanical system was the most relevant, and will affect the risk contour the most when the system has reduced function, was concluded to be the rudder. The study shows that the risk contour for the MASS should be directed towards the relative course over ground when affected by wind and currents. The study also shows that the risk contour should remain the same but be extended when the vesselis affected by reduced manoeuvring ability. Lastly, conclusions about the results from the case study, were that the risk contour both is useful and helpful for the participants. / Att övervaka marina autonoma ytfartyg i en landbaserad fjärroperationcentral och kontrollera dem medför många fördelar i stället för att som idag behöva en hel besättning ombord. Att ha en operatör som övervakar flera fartyg medför många fördelar. Till exempel skulle, färre antal personer krävas för att styra samma antal fartyg samt färre personal vara tvungna att bo på fartyget i långa perioder och riskera att skadas eller avlida i olyckor eller oväder. För att underlätta och stödja övervakning av autonoma ytfartyg kan en risk­-kontur vara ett verktyg och hjälpmedel. En risk-­kontur är ett visuellt verktyg för fjärroperatören och illustrerar ett område runt omkring skeppet. När en risk­kontur kolliderar med ett annat föremål eller en annan risk­kontur, varnas operatören. Om operatören har flera monitorer, ger varningen en indikation om vilken monitor som operatören bör fokusera på. I detta projekt har risk­-konturer undersökts genom en bakgrunds-­, en litteratur-och en fallstudie. Fallstudien bestod av intervjuer med ett antal experter som därutöver fick agera testpersoner i en simuleringsmiljö med risk-konturer. Datan ifråntestningen visade att risk­konturen påverkade testoperatören genom att öka deras riskmedvetenhet och öka avstånd till andra skepp samt agera tidigare i riskfyllda situationer. Feedbacken från simuleringen och den föreslagna formen på risk-­konturen var överväldigande positiv. Risk­-konturen var lätt att förstå och den förändrade risk-konturen i olika scenarier fick högt betyg från deltagarna. Slutsatserna blev att vind och strömmar är de två mest relevanta faktorer när man studerar miljöfaktorer som påverkar positionen för ett marint autonomt ytfartyg direkt. Slutsatsen om vilket mekaniskt system som är det mest relevanta och därför kommer att påverka risk­konturen mest när systemet har reducerad förmåga visade sig vara roder­systemet. Studien visar att risk­-konturen för fartyget bör vara i riktning med den relativa kursen över grund när fartyget påverkas av vind och strömmar. Dessutom visar studien att risk­-konturen ska förbli samma form men förlängas när fartyget påverkas av reducerad manövreringsförmåga. Slutligen var slutsatserna om resultaten från fallstudien att risk­konturen både är användbar och till hjälp för deltagarna.

Autonomous navigation has the opportunity to make roads safer and help perform search and rescue missions by reducing human error. Odometry methods are essential to allow for autonomous navigation because they estimate how the robot will move based on the available sensors. This thesis aims to compare and evaluate the Cubature Kalman filter (CKF) based approach for visual-inertial odometry (VIO) to traditional Extended Kalman Filter (EKF) based methods on criteria such as the accuracy of the results. VIO methods use camera and IMU sensor for the predictions. The Multi-State-Constraint Kalman filter (MSCKF) was utilized as the foundation VIO approach to evaluate the underlying filter between EKF and CKF while maintaining the background conditions like visual tracking pipeline, IMU model, and measurement model constant. Evaluation metrics of absolute trajectory error (ATE) and relative error (RE) was used after tuning the filters on EuRoC and KAIST datasets. It is shown that, based on the existing implementation, the filters have no statistically significant difference in performance when predicting motion estimates, despite the fact that the absolute trajectory error of position for EKF estimation is lower. It is further shown that as the length of the trajectory increases, the estimation error for both filters rises unboundedly. Under the visual inertial framework of MSCKF, the CKF filter, which does not linearize the system, works equally as well as the well-established EKF filter and has the potential to perform better with more accurate nonlinear system and measurement models. / Autonom navigering har möjlighet att göra vägar säkrare och hjälpa till att utföra räddningsuppdrag genom att minska mänskliga fel. Odometrimetoder är viktiga för att möjliggöra autonom navigering eftersom de skattar hur roboten rör sig baserat på tillgängliga sensorer. Detta examensarbete syftar till att utvärdera Cubature Kalman filter (CKF) för visuell tröghetsodometri (VIO) och jämföra med traditionella Extended Kalman Filter (EKF) gällande bland annat noggrannhet. VIO-metoder använder kamera och IMU-sensor för skattningarna. MultiState Constraint Kalmanfiltret (MSCKF) användes som grund VIO-metoden för att utvärdera filteralgoritmerna EKF och CKF, samtidigt som de VIO-specifika delarna så som IMU-modell och mätmodell kunde förbli desamma. Utvärderingen gjordes baserat på absolut banfel (ATE) och relativa fel (RE) på EuRoC- och KAIST-datauppsättningar. Det visas att, baserat på den befintliga implementeringen, har filtren ingen statistiskt signifikant skillnad i prestanda när de förutsäger rörelsen, trots att det absoluta banafelet för positionen för EKF-uppskattning är lägre. Det visas vidare att när längden på banan ökar, ökar uppskattningsfelet för båda filtren obegränsat. Under MSCKFs visuella tröghetsramverk fungerar CKF-filtret, som inte linjäriserar systemet, lika bra som det väletablerade EKF-filtret och har potential att prestera bättre med mer exakta olinjära system och mätmodeller.

Modern vehicles have electrical architectures whose complexity grows year after year due to feature growth corresponding to customer expectations. The latest of the expectations, automation of the dynamic driving task however, is poised to bring about some of the largest changes seen so far. In one fell swoop, not only does required functionality for automated driving drastically increase the system complexity, it also removes the fall-back of the human driver who is usually relied upon to handle unanticipated failures after the fact. The need to architect thus requires a greater rigour than ever before, to maintain the level of safety that has been associated with the automotive industry. The work that is part of this thesis has been conducted, in close collaboration with our industrial partner Scania CV AB, within the Vinnova FFI funded project ARCHER. This thesis aims to provide a methodology for architecting during the concept phase of development, using industrial practices and principles including those from safety standards such as ISO 26262. The main contributions of the thesis are in two areas. The first area i.e. Part A contributes, (i) an analysis of the challenges of architecting automated driving, and serves as a motivation for the approach taken in the rest of this thesis, i.e. Part B where the contributions include, (ii) a definition of a viewpoint for functional safety according to the definitions of ISO 42010, (iii) a method to systematically extract information from legacy components and (iv) a process to use legacy information and architect in the presence of uncertainty to provide a work product, the Preliminary Architectural Assumptions (PAA), as required by ISO 26262. The contributions of Part B together comprise a methodology to architect the PAA.   A significant challenge in working with the industry is finding the right fit between idealized principles and practical utility. The methodology in Part B has been judged fit for purpose by different parts of the organization at Scania and multiple case studies have been conducted to assess its usefulness in collaboration with senior architects. The methodology was found to be conducive in both, generating the PAA of a quality that was deemed suitable to the organization and, to find inadequacies in the architecture that had not been found earlier using the previous non-systematic methods. The benefits have led to a commissioning of a prototype tool to support the methodology that has begun to be used in projects related to automation at Scania. The methodology will be refined as the projects progress towards completion using the experiences gained. A further impact of the work is seen in two patent filings that have originated from work on the case studies in Part B. Emanating from needs discovered during the application of the methods, these filed patents (with no prior publications) outline the future directions of research into reference architectures augmented with safety policies, that are safe in the presence of detectable faults and failures. To aid verification of these ideas, work has begun on identifying critical scenarios and their elements in automated driving, and a flexible simulation platform is being designed and developed at KTH to test the chosen critical scenarios. / Efterfrågan på nya funktioner leder till en ständigt ökande komplexitet i moderna fordon, speciellt i de inbyggda datorsystemen. Införande av autonoma fordon utgör inte bara det mest aktuella exemplet på detta, utan medför också en av de största förändringar som fordonsbranschen sett. Föraren, som ”back-up” för att hantera oväntade situationer och fel, finns inte längre där vid höggradig automation, och motsvarande funktioner måste realiseras i de inbyggda system vilket ger en drastisk komplexitetsökning. Detta ställer systemarkitekter för stora utmaningar för att se till att nuvarande nivå av funktionssäkerhet bibehålls. Detta forskningsarbete har utförts i nära samarbete med Scania CV AB i det Vinnova (FFI)-finansierade projektet ARCHER. Denna licentiatavhandling har som mål att ta fram en metodik för konceptutveckling av arkitekturer, förankrat i industriell praxis och principer, omfattande bl.a. de som beskrivs i funktionssäkerhetsstandards som ISO 26262. Avhandlingen presenterar resultat inom två områden. Det första området, del A, redovisar, (i) en analys av utmaningar inom arkitekturutveckling för autonoma fordon, vilket också ger en motivering för resterande del av avhandlingen. Det andra området, del B, redovisar, (ii) en definition av en ”perspektivmodell” (en s.k. ”viewpoint” enligt ISO 42010) för funktionssäkerhet, (iii) en metod för att systematiskt utvinna information från existerande komponenter, och (iv) en process som tar fram en arbetsprodukt för ISO 26262 – Preliminära Arkitektur-Antaganden (PAA). Denna process använder sig av information från existerande komponenter – resultat (iii) och förenklar hantering av avsaknad/osäker information under arkitekturarbetet. Resultaten från del B utgör tillsammans en metodik för att ta fram en PAA. En utmaning i forskning är att finna en balans mellan idealisering och praktisk tillämpbarhet. Metodiken i del B har utvärderats i flertalet industriella fallstudier på Scania i samverkan med seniora arkitekter från industrin, och har av dessa bedömts som relevant och praktiskt tillämpningsbar. Erfarenheterna visar att metodiken stödjer framtagandet av PAA’s av   lämplig kvalitet och ger ett systematiskt sätt att hantera osäkerhet under arkitekturutvecklingen. Specifikt så gjorde metoden det möjligt att identifiera komponent-felmoder där arkitekturen inte var tillräcklig för åstadkomma önskad riskreducering, begränsningar som inte hade upptäckts med tidigare metoder. Ett prototypverktyg för att stödja metodiken har utvecklats och börjat användas på Scania i projekt relaterade till autonoma fordon. Metodiken kommer sannolikt att kunna förfinas ytterligare när dessa projekt går mot sitt slut och mer erfarenheter finns tillgängliga. Arbetet i del B har vidare lett till två patentansökningar avseende koncept som framkommit genom fallstudierna. Dessa koncept relaterar till referensarkitekturer som utökats med policies för personsäkerhet (Eng. ”safety”) för att hantera detekterbara felfall, och pekar ut en riktning för framtida forskning. För att stödja verifiering av dessa koncept har arbete inletts för att identifiera kritiska scenarios för autonom körning. En flexibel simuleringsplattform håller också på att designas för att kunna testa kritiska scenarios. / Vinnova-FFI funded Project ARCHER

Trust is important for the adoption of autonomous vehicles. Providing voiced explanations explaining a vehicle’s behavior has been found to improve trust, but it is unclear how explanations should be presented. In this experimental study, it was investigated whether manipulating the temporal sequence of “How” explanations, that describe what the vehicle does, and “Why” explanations, that describe why the vehicle does something, influence users’ trust, mental workload, situational awareness, and preferences. The research questions are: 1. What is the optimal sequence of presenting explanations, measured by the least amount of mental workload, increased trust, and increased situational awareness? 2. Which explanation type do people prefer the most? 3. Does explanation type affect attentional task performance? These questions were assessed by using questionnaires and qualitative data in a within group design. The sample consisted of 42 participants between the ages of 21 and 37. The data was analyzed with a repeated measures analysis of variance (ANOVA) and a binomial test. The study found no effect of different explanation types on trust and mental workload. However, the scenarios had a significant effect on the results. Presenting the “How + Why” explanation before an event caused the best situational awareness. Although no significant difference in preferences was found, the “Why” explanation had the highest mean preference. This study shows that when evaluating different explanation types, it is important to consider situational factors that can affect measured variables such as vehicle’s behavior and surrounding environment. Future studies should focus on exploring situational factors further. / Förtroende är viktigt för införandet av autonoma fordon. Att ge auditiva förklaringar om bilens handlingar har setts förbättra förtroendet, men det är oklart hur förklaringar bör presenteras. I denna experimentella studie undersöktes det om manipulering av den tidsmässiga sekvensen av "Hur"-förklaringar, som beskriver vad fordonet gör, och "Varför"-förklaringar, som beskriver varför fordonet gör något, påverkade användarens förtroende, mentala arbetsbelastning, situationsmedvetenhet, och preferenser. Forskningsfrågorna var: 1. Vilken är den optimala sekvensen för att presentera förklaringar, mätt med minsta möjliga mentala arbetsbelastning, ökat förtroende och ökad situationsmedvetenhet? 2. Vilken typ av förklaring föredrar folk mest? 3. Påverkar typ av förklaring uppmärksamhetsuppgift? Dessa frågeställningar undersöktes med hjälp av frågeformulär och kvalitativa data i en inomgruppsdesign. Urvalet bestod av 42 deltagare mellan 21–37 år. Data analyserades med en variansanalys med upprepade mått (ANOVA) och ett binomialtest. Studien fann ingen effekt av olika förklaringstyper på förtroende och mental arbetsbelastning. Scenarierna hade dock signifikant effekt på resultaten. Att presentera "Hur + Varför"-förklaringen innan en händelse gav den bästa situationsmedvetenheten. Även om ingen signifikant skillnad i preferens hittades, hade "Varför"-förklaringen det högsta medelvärdet. Denna studie visade att när man utvärderar olika förklaringstyper är det viktigt att ta hänsyn till situationsfaktorer som kan påverka uppmätta variabler som fordonets beteende och omgivande miljö. Framtida studier bör fokusera på att utforska situationsfaktorer ytterligare.

<p dir="ltr">Approaches to system testing and evaluation (T&E) are becoming increasingly relevant as artificial intelligence (AI)/machine learning (ML) technology expands across the industry’s current landscape. As the AI/ML landscape continues to develop, greater amounts of data are required to build the next generation of technology. Multiple communities have worked to create frameworks to interact with such scales of data, yet a gap persists in the ability to utilize data generated throughout the development process to support the for use in a T&E program. The objective of this thesis is to address this gap through a multi-fidelity approach to the test and evaluation of AI-enabled systems. This approach is constructed using a space of models to visualize similarities and differences between each individual model. Once requirements and potential tests that models can be employed to fulfill are organized, a method to sequentially select models for testing is utilized. Models are selected to maximize utility, dependent on model performance and cost to the T&E team. Experimentation was conducted through the case of an autonomous vehicle (AV) perception system, where models were constructed using a simulation of the Purdue University campus for AVs to drive around. Results show that the proposed approach, when paired with Bayesian Optimization for sequential test selection through an expected improvement acquisition function, can effectively select models in a manner that works to minimize uncertainty and cost for the test team. Through computational experiments, the proposed approach can be used to develop test combinations that minimize costs and maximize utility while maximizing the information a T&E team has on how well a system can meet a set of testing requirements in operational conditions.</p>

In today’s world, the applications of Unmanned Aerial Vehicle (UAV) systems are leaping by extending their scope from military applications on to commercial and medical sectors as well. Owing to this commercialization, the need to append external hardware with UAV systems becomes inevitable. This external hardware could aid in enabling wireless data transfer between the UAV system and remote Wireless Sensor Networks (WSN) using low powered architecture like Thread, BLE (Bluetooth Low Energy). The data is being transmitted from the flight controller to the ground control station using a MAVlink (Micro Air Vehicle Link) protocol. But this radio transmission method is not secure, which may lead to data leakage problems. The ideal aim of this research is to address the issues of integrating different hardware with the flight controller of the UAV system using a light-weight protocol called UAVCAN (Unmanned Aerial Vehicle Controller Area Network). This would result in reduced wiring and would harness the problem of integrating multiple systems to UAV. At the same time, data security is addressed by deploying an encryption chip into the UAV system to encrypt the data transfer using ECC (Elliptic curve cryptography) and transmitting it to cloud platforms instead of radio transmission.

With the arrival of automated vehicles (AVs) on our streets virtually around the corner, this thesis explores advances in automated driving technology with a focus on ethical decision making in dilemmatic traf- fic situations. In a total of five publications, we take a multi-facetted approach to analyse and address the core challenges related to auto- mated ethical decision making in AVs. In publications one through three, we conduct a series of immersive virtual reality studies to analyze human behavior in traffic dilemmas, explore mathematical approaches to model the decision making process, investigate how the assessment methodology can affect moral judgment, and discuss the implications of these studies for algorithmic decision making in the real-world. In publication number four, we provide a comprehensive summary of the status quo of AV technology and legislation with regard to automated ethical decision making. Here, we discuss when and why ethical deci- sion making systems become necessary in AVs, review existing guide- lines for the behavior of AVs in dilemma situations, and compile a set of 10 demands and open questions that need to be addressed in the pursuit of a framework for ethical decision making in AVs. Finally, the basis for automated ethical decision making in AVs will be provided by accurate assessments of the immediate environment of the car. The pri- mary technology used to provide the required information processing of camera and LiDAR images in AVs is machine learning, and in particular deep learning. In publication five, we propose a form of adaptive acti- vation functions, addressing a central element of deep neural networks, which could, for instance, lead to increased detection rates of relevant objects, and thus help to provide a more accurate assessment of the AVs environment. Overall, this thesis provides a structured and compre- hensive overview of the state of the art in ethical decision making for AVs. It includes important implications for the design of decision mak- ing algorithms in practice, and concisely outlines the central remaining challenges on the road to a safe, fair and successful introduction of fully automated vehicles into the market.

This dissertation investigates the value of deep reinforcement learning (DRL) within an agent-based model (ABM) of a large open-air venue. The intent is to reduce civilian casualties in an active shooting incident (ASI). There has been a steady increase of ASIs in the United States of America for over 20 years, and some of the most casualty-producing events have been in open spaces and open-air venues. More research should be conducted within the field to help discover policies that can mitigate the threat of a shooter in extremis. This study uses the concept of dynamic signage, controlled by a DRL policy, to guide civilians away from the threat and toward a safe exit in the modeled environment. It was found that a well-trained DRL policy can significantly reduce civilian casualties as compared to baseline scenarios. Further, the DRL policy can assist decision makers in determining how many signs to use in an environment and where to place them. Finally, research using DRL in the ASI space can yield systems and policies that will help reduce the impact of active shooters during an incident.

<div> <div> <div> <p>Commercial vehicles in the United States account for a significant fraction of greenhouse gas emissions and NOx emissions. The objectives of this work are reduction in commercial vehicle NOx emissions through enhanced aftertreatment thermal management via diesel engine variable valve actuation and the reduction of commercial vehicle fuel consumption/GHG emissions by enabling more effective class 8 truck platooning. </p> <p><br></p><p>First, a novel diesel engine aftertreatment thermal management strategy is proposed which utilizes a 2-stroke breathing variable value actuation strategy to increase the mass flow rate of exhaust gas. Experiments showed that when allowed to operate with modestly higher engine-out emissions, temperatures comparable to baseline could be achieved with a 1.75x exhaust mass flow rate, which could be beneficial for heating the SCR catalyst in a cold-start scenario. </p> <p><br></p><p>Second, a methodology is presented for characterizing aerodynamic drag coefficients of platooning trucks using experimental track-test data, which allowed for the development of high-fidelity platoon simulations and thereby enabled rapid development of advanced platoon controllers. Single truck and platoon drag coefficients were calculated for late model year Peterbilt 579’s based on experimental data collected during J1321 fuel economy tests for a two-truck platoon at 65 mph with a 55’ truck gap. Results show drag coefficients of 0.53, 0.50, and 0.45 for a single truck, a platoon front truck, and a platoon rear truck, respectively. </p> <p><br></p><p>Finally, a PID-based platoon controller is presented for maximizing fuel savings and gap control on hilly terrain using a dynamically-variable platoon gap. The controller was vetted in simulation and demonstrated on a vehicle in closed-course functionality testing. Simulations show that the controller is capable of 6-9% rear truck fuel savings on a heavily-graded route compared to a production-intent platoon controller, while increasing control over the truck gap to discourage other vehicles from cutting in. </p></div></div></div>