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This thesis describes the initial development of the Texas A&M Autonomous Ground Vehicle test platform and waypoint following software, including the associated controller design. The original goal of the team responsible for the development of the vehicle was to enter the DARPA Grand Challenge in October 2005. A 2004 Ford F150 4x4 pickup was chosen as the vehicle platform and was modified with a 6” suspension lift and 35” tires, as well as a commercial drive-by-wire system. The waypoint following software, the design of which is described in this thesis, is written in C and successfully drives the vehicle on a course defined by GPS waypoints at speeds up to 50 mph. It uses various heuristics to determine desired speeds and headings and uses control feedback to guide the vehicle towards these desired states. A vehicle dynamics simulator was also developed for software testing. Ultimately, this software will accept commands from advanced obstacle avoidance software so that the vehicle can navigate in true off-road terrain.

With the automotive industry ever striving to push the limits of fuel efficiency, the Shell EcoMarathon offers a glimpse into this energy conserving mindset by challenging engineering students around the world to design and build ultra-efficient vehicles to compete regionally. This requires synchronization of engineering fields to ensure that the vehicle and powertrain system work in parallel to achieve similar goals. The goal for Cal Poly – San Luis Obispo’s EcoMarathon vehicle for the 2015 competition is to analyze the unique operating mode that the powertrain undergoes during competition and improve their current package to increase fuel efficiency. In this study, fuel delivery, ignition timing and engine temperature are experimentally varied to observe trends in steady state fuel consumption. A developmental simulation is then implemented with these trends to analyze potential differences in transient and steady state tuning targets. The engine is then tuned to finalized tuning targets and performance compared with benchmark values.

The passenger transportation sector is notoriously difficult to decarbonize. In this thesis, two distinct and novel methodologies to estimate the environmental impact of alternative and conventional transportation technologies are developed. In Chapter 2, a provincial fleet policy-driven linear programming model is developed to minimize the cost of three passenger vehicle electrification policies in Ontario under a 30% GHG reduction target by 2030. Provincial life-cycle emissions and total-cost-of-ownership associated with policy allocation is estimated. The results highlight that electrification of on-road passenger transportation will not be sufficient to meet the 30% reduction target despite Ontario's low-carbon electricity grid. Instead, reductions of between 24% to 26% are forecasted at an annual cost (for ten years) of between CAD 0.29 to 0.3 billion annually indicating that additional policies are necessary to realize a 30% reduction target. In Chapter 3, a trip-level vehicle framework is developed to determine under what operating conditions transit buses and passenger cars will be environmentally beneficial across the dimensions of technology, service mode, and power source pathway. The well-to-wheel energy consumption and GHG emissions are simulated for over 450 operating scenarios. Emissions are then normalized through passenger-trip emission thresholds to facilitate equivalent comparison across all dimensions. The results indicate that the most beneficial solution are fuel-cell electric car-share, battery electric car-share, and battery electric bus all powered by low-carbon intensity power sources at average occupancy (7.9-19.7 gCO2e passenger-service-mode-trip-km-travelled-1). Furthermore, transit bus technologies have the potential to reduce up to 2.3 times more GHG per passenger-trip than comparable ride-share passenger cars at average occupancies. The results of Chapter 2 and 3 highlight that technology alone may not be sufficient to achieve significant GHG reductions; policy which leverage local operating data and target GHG reduction associated with passenger-trips are critical to informing under what conditions a mobility solution is environmentally beneficial. / Thesis / Master of Civil Engineering (MCE) / There is a dire need to evaluate the effectiveness of transportation GHG mitigation policies as alternative mobility solutions are being adopted and the pressure to respond to climate change intensifies. This work evaluates the effectiveness of policy optimization and vehicle-level simulation techniques to inform GHG mitigation decision-making. A two-step approach is adopted herein. At the strategic level, a cost optimization model for passenger vehicle electrification policies in Ontario is calibrated to identify the optimal allocation of provincial policy to achieve a 30% GHG reduction by 2030. Next, a micro level focuses on the energy consumption of eight vehicle technologies over 450 operational scenarios is simulated and trip-level passenger emissions are estimated to reveal the environmentally beneficial mobility option, corresponding passenger thresholds, and extent of variability associated with local operating conditions. Overall, optimization and trip-level vehicle simulation can be used to demystify optimal decision-making related to mobility solutions.

The use of global positioning systems, or GPS, as a means of logistical organization for fleet vehicles has become more widespread in recent years. The system has the ability to track vehicle location, report on diagnostic trouble codes, and keep tabs on maintenance schedules. This helps to improve the safety and productivity of the vehicles and their operators. Additionally, the increasing use of yaw and roll stability control in commercial trucks has contributed to an increased level of safety for truck drivers. However, these systems require the vehicle to begin a yaw or roll event before they assist in maintaining control. This thesis presents a new method for utilizing the GPS signal in conjunction with a new fuzzy logic-based stability index, the Total Safety Margin (TSM), to create a superior active safety system. This thesis consists of four main components: An overview of GPS technology is presented with coverage of several automotive-based applications. The proposed implementation of GPS in the new Hardware-in-the-Loop (HIL) driving simulator under development at the Virginia Tech Center for Vehicle Systems and Safety (CVeSS) is presented. The three degree-of-freedom (3DOF), linear, single track equation set used in the Matlab simulations is derived from first principles. Matlab and TruckSim 7® simulations are performed for five vehicle masses and three forward velocities in a ramp-steer maneuver. Using fuzzy logic to develop the control rules for the Total Safety Margin (TSM), TSM matrices are built for both the Matlab and TruckSim 7® results based on these testing conditions. By comparing these TSM matrices it is shown that the two simulation methods yield similar results. A discussion of the development and implementation of the aforementioned HIL driving simulator is presented, specifically the steering subsystem. Using Matlab/Simulink, dSPACE ControlDesk, and CarSim RT® software it is shown that the steering module is capable of steering the CarSim RT® simulation vehicle accurately within the physical range of the steering sensor used. / Master of Science

Dynamic Collaborative Driving is the concept of decentralized multi-vehicle automated driving where vehicles form dynamic local area networks within which information is shared to build a dynamic data representation of the environment to improve road usage and safety. The vision is to have networks of cars spanning multiple lanes forming these dynamic networks so as to optimize traffic flow while maintaining safety as each vehicle travels to its destinations. A basic requirement of any vehicle participating in dynamic collaborative driving is longitudinal and lateral control. Without this capability, higher-level coordination is not possible. This thesis investigates the issue of the control of an automobile in the context of a Dynamic Collaborative Driving system. Each vehicle involved is considered a complex composite nonlinear system. Therefore a complex nonlinear model of the vehicle dynamics is formulated and serves as the control system design platform. Due to the nonlinear nature of the vehicle dynamics, a nonlinear approach to control is used to achieve longitudinal and lateral control of the vehicle. This novel approach combines the use of reinforcement learning: a modern machine learning technique, with adaptive control and preview control techniques. This thesis presents the design of both the longitudinal and lateral control systems which serves as a basis for Dynamic Collaborative Driving. The results of the reinforcement learning phase and the performance of the adaptive control systems for single automobile performance as well as the performance in a multi-vehicle platoon is presented.

Dynamic Collaborative Driving is the concept of decentralized multi-vehicle automated driving where vehicles form dynamic local area networks within which information is shared to build a dynamic data representation of the environment to improve road usage and safety. The vision is to have networks of cars spanning multiple lanes forming these dynamic networks so as to optimize traffic flow while maintaining safety as each vehicle travels to its destinations. A basic requirement of any vehicle participating in dynamic collaborative driving is longitudinal and lateral control. Without this capability, higher-level coordination is not possible. This thesis investigates the issue of the control of an automobile in the context of a Dynamic Collaborative Driving system. Each vehicle involved is considered a complex composite nonlinear system. Therefore a complex nonlinear model of the vehicle dynamics is formulated and serves as the control system design platform. Due to the nonlinear nature of the vehicle dynamics, a nonlinear approach to control is used to achieve longitudinal and lateral control of the vehicle. This novel approach combines the use of reinforcement learning: a modern machine learning technique, with adaptive control and preview control techniques. This thesis presents the design of both the longitudinal and lateral control systems which serves as a basis for Dynamic Collaborative Driving. The results of the reinforcement learning phase and the performance of the adaptive control systems for single automobile performance as well as the performance in a multi-vehicle platoon is presented.

This thesis presents a new nonlinear automobile dynamical model and investigates the possibility of automobile dynamic control with wheel torque utilizing this model. The model has been developed from first principles by applying classical mechanics. Inputs to the model are the four independent wheel torques, while the steer angles at each wheel are specified as independent time-varying signals. In this way, consideration of a variety of steering system architectures, including rear-wheel steer, is possible, and steering introduces time-varying structure into the vehicle model. The frictional contact at the wheel-road interface is modeled by use of the LuGre dynamic friction model. Extensions to the existing two-dimensional LuGre friction model are derived and the steady-state of the friction model is compared to existing static friction models. Simulation results are presented to validate the model mathematics and to explore automobile behavior in a variety of scenarios. Vehicle control with wheel torque is explored using the theory of input-output linearization for multi-input multi-output systems. System relative degree is analyzed and use of steady-state LuGre friction in a control design model is shown to give rise to relative degree singularities when no wheel slip occurs. Dynamic LuGre friction does not cause such singularities, but instead has an ill-defined nature under the same no-slip condition. A method for treating this ill-defined condition is developed, leading to the potential for the system to have relative degree. Longitudinal velocity control and combined longitudinal and angular vehicle velocity control are demonstrated in simulation using input-output linearization, and are shown to produce improved vehicle response as compared to the open-loop behavior of the automobile. Robustness of the longitudinal velocity control to friction model parameter variation is explored and little impact to the controller's ability to track the desired trajectory is observed.

In this thesis, a simulation package for the Six Degrees of Freedom (6DOF) motion of an underwater vehicle is developed. Mathematical modeling of an underwater vehicle is done and the parameters needed to write such a simulation package are obtained from an existing underwater vehicle available in the literature. Basic equations of motion are developed to simulate the motion of the underwater vehicle and the parameters needed for the hydrodynamic modeling of the vehicle is obtained from the available literature. 6DOF simulation package prepared for the underwater vehicle was developed using the MATLAB environment. S-function hierarchy is developed using the same platform with C++ programming language. With the usage of S-functions the problems related to the speed of the platform have been eliminated. The use of Sfunction hierarchy brought out the opportunity of running the simulation package on other independent platforms and get results for the simulation.

In 2004, students at the University of Guelph designed and constructed an all-wheel-drive Formula SAE vehicle for competition. It utilized an electronically-controlled, hydraulic-actuated limited slip center coupling from Haldex Traction Ltd, to transfer torque to the front wheels. The initial control system design was not comprehensively conceived, so there was a need for a thoroughly developed control system for the all-wheel-drive actuator augmented with commonly available sensors and a low cost controller. This thesis presents a novel all-wheel-drive active torque transfer controller using a neural network estimated slip angle velocity. This controller specifically targets a racing vehicle by allowing rapid direction changes for maneuverability but damping slip angle changes for increased controllability. The slip angle velocity estimate was able to track the actual simulated value it was trained against with excellent phase matching but with some offsets and phantom spikes. Using the estimated slip angle velocity for control realized smooth control output, excellent stability, and a fast turn-in yaw response on par with rear-wheel-drive configurations. A full vehicle simulation with software-in-the-loop testing for control software was also developed to aid the system design process and avoid vehicle run time for tuning. This design flow should significantly decrease development time for controls algorithm work and help increase innovation within the team.