Although Autonomous Vehicles (AVs) are quickly becoming a reality, there is much that needs to be understood before mainstream commercialization can occur. One critical issue is the interplay between multiple fields of engineering. Whereas the first part of this work is a granular treatment of a specific issue, the second part simultaneously examines numerous fields within the transportation industry. In the surge to understand and develop AVs, researchers tend to study specific subdivisions within the "vehicle engineering umbrella". In particular, mechanical and civil engineers study vehicle dynamics in two different levels of specificity. Mechanical engineers typically investigate small-scale dynamic behavior which applies to a single vehicle, such as vehicle-terrain interactions or the behavior of mechanical components. On the other hand, civil engineers tend to study kinematic behavior: the behavior of platoons as it pertains to large-scale traffic flow. Regardless of the scale of study, each subdivision has a set of performance metrics. Due to the differences among subdivisions, some performance metrics may (unintentionally) compete. Compromises must be made in the design stage to produce a vehicle which caters to an appropriate audience. The first part of this work features two major contributions to bridge the gap between the dynamic and kinematic perspectives. One is the application of Design Envelopes that establishes a framework to balance constraints and assess design tradeoffs arising from each viewpoints. Three Design Envelopes are introduced to reach compromises on a vehicle's velocity, acceleration, and jerk. Another contribution is a methodology to tune the parameters of a car-following model analytically. Current tuning practices require empirically collected traffic count data, which is cumbersome to obtain. Analytically parameterizing car-following models facilitates more robust planning and encompasses both the dynamic and kinematic perspectives. The second contribution utilizes these Design Envelopes to improve a currently-existing speed profile generator. Integrating the Design Envelopes reformulates the existing algorithm as a constrained LQR problem, which enhances ride comfort and maintains dynamic stability for not just one vehicle, but a platoon. Simulations demonstrate that the refined algorithm can reduce the travel time on a specific route by 3-4.4%. More importantly, the simulations demonstrate it is possible to synthesize multiple engineering fields to enhance AV design. The second part of this work features two contributions aimed at revisions to modern-day highway design policies based on the concept of combining microscopic and macroscopic principles. One common belief is that AVs should drive better than the best human drivers, which suggests operating at or close to the vehicle's theoretical handling limits. Operating in this manner requires a thorough understanding of the associated risks, particularly the risks stemming from uncertainty. This is especially pertinent as there are many inherently probabilistic quantities that are conveniently treated as deterministic in vehicle performance simulations, such as the coefficient of friction. This is a questionable practice when operating on the precipice of compromised safety. Thus, the second part of this work probabilistically examines the chance of handling loss given the amount of tire-road friction and driver acceleration. The result is a mathematically rigorous quantification of a safety margin for various road conditions and driver ability levels. Changes to the official US highway design handbook are recommended based on the findings. / Doctor of Philosophy / Autonomous vehicles (AVs) are quickly becoming a reality. In the surge to understand and develop AVs, researchers tend to study specific subdivisions within the vehicle engineering umbrella. In particular, mechanical and civil engineers study vehicle dynamics in two different levels of specificity. Mechanical engineers typically investigate the dynamics of a single vehicle, such as vehicle-terrain interactions or how various mechanical components operate. On the other hand, civil engineers tend to study traffic flow, which involves platoons (large groups of vehicles). Regardless of the scale of study, each subdivision has a set of performance metrics. Due to the differences among subdivisions, some performance metrics may (unintentionally) compete. Compromises must be made in the design stage to produce a vehicle which caters to an appropriate audience. This work features four main contributions. The first contribution is the application of Design Envelopes that establishes a framework to balance constraints arising from the different ways of studying vehicle dynamics. Three Design Envelopes are introduced to reach compromises on various facets of a vehicle's behavior, such as the vehicle's speed. The second contribution utilizes these Design Envelopes to improve a currently-existing speed profile generator. The current speed profile generator determines how to smoothly transition between two speeds (such as needing to decelerate to remain under a speed limit), but the ride may be uncomfortable to passengers. Integrating the Design Envelopes into the algorithm enhances the ride comfort for not just one vehicle, but a platoon. Simulations demonstrate that the refined algorithm can reduce the travel time on a specific route by 3-4.4%. The third contribution examines how horizontal curves on highways are designed, and a revision based on an acceleration-based safety margin is proposed. Finally, the fourth contribution considers important design variables probabilistically to establish a link between a motorist's acceleration and the chance of a tire skidding failure, which can impact the way straightaway road segments are designed to accommodate sudden braking maneuvers. As a whole, this work demonstrates it is possible to synthesize multiple engineering fields to enhance both current and future (full-scale AV implementation) roadway design.

In the field of modern automotive engineering, many researchers are focusing on the development of advanced vehicle control systems such as autonomous vehicle systems and Advanced Driver Assistance Systems (ADAS). Furthermore, Driver Assistance Systems (DAS) such as cruise control, Anti-Lock Braking Systems (ABS), and Electronic Stability Control (ESC) have become widely popular in the automotive industry. Therefore, vehicle control research attracts attention from both academia and industry, and has been an active area of vehicle research for over 30 years, resulting in impressive DAS contributions. Although current vehicle control systems have improved vehicle safety and performance, there is room for improvement for dealing with various situations. The objective of the research is to develop a predictive vehicle control system for improving vehicle safety and performance for autonomous vehicles and ADAS. In order to improve the vehicle control system, the proposed system utilizes information about the upcoming local driving environment such as terrain roughness, elevation grade, bank angle, curvature, and friction. The local driving environment is measured in advance with a terrain measurement system to provide terrain data. Furthermore, in order to obtain the information about road conditions that cannot be measured in advance, this work begins by analyzing the response measurements of a preceding vehicle. The response measurements of a preceding vehicle are acquired through Vehicle-to-Vehicle (V2V) or Vehicle-to-Infrastructure (V2I) communication. The identification method analyzes the response measurements of a preceding vehicle to estimate road data. The estimated road data or the pre-measured road data is used as the upcoming driving environment information for the developed vehicle control system. The metric that objectively quantifies vehicle performance, the Performance Margin, is developed to accomplish the control objectives in an efficient manner. The metric is used as a control reference input and continuously estimated to predict current and future vehicle performance. Next, the predictive control algorithm is developed based on the upcoming driving environment and the performance metric. The developed system predicts future vehicle dynamics states using the upcoming driving environment and the Performance Margin. If the algorithm detects the risks of future vehicle dynamics, the control system intervenes between the driver's input commands based on estimated future vehicle states. The developed control system maintains vehicle handling capabilities based on the results of the prediction by regulating the metric into an acceptable range. By these processes, the developed control system ensures that the vehicle maintains stability consistently, and improves vehicle performance for the near future even if there are undesirable and unexpected driving circumstances. To implement and evaluate the integrated systems of this work, the real-time driving simulator, which uses precise real-world driving environment data, has been developed for advanced high computational vehicle control systems. The developed vehicle control system is implemented in the driving simulator, and the results show that the proposed system is a clear improvement on autonomous vehicle systems and ADAS. / Ph. D. / In the field of modern automotive engineering, many researchers are focusing on the development of advanced vehicle control systems such as autonomous vehicle systems and Advanced Driver Assistance Systems (ADAS). Furthermore, cruise control, Anti-Lock Braking Systems, and Electronic Stability Controls have become widely popular in the automotive industry. Although vehicle control systems have improved vehicle safety and performance, there is still room for improvement for dealing with various situations. The objective of the research is to develop a predictive vehicle control system for improving vehicle safety and performance for autonomous vehicles and ADAS. In order to improve the vehicle control system, the proposed system utilizes information about the upcoming driving conditions such as road roughness, elevation grade, bank angle, and curvature. The driving environment is measured in advance with a terrain measurement system. Furthermore, in order to obtain the information about road conditions that cannot be measured in advance, this work begins by analyzing a preceding vehicle’s response to the road. The combined road data is used as the upcoming driving environment information. The measurement that indicates vehicle performance, the Performance Margin, is developed to accomplish the research objectives. It is used in the developed control system, which predicts future vehicle performance. If the system detects future risks, the control system will intervene to correct the driver’s input commands. By these processes, the developed system ensures that the vehicle maintains stability, and improves vehicle performance regardless of the upcoming and unexpected driving conditions. To implement and evaluate the proposed systems, a driving simulator has been developed. The results show that the proposed system is a clear improvement on autonomous vehicle systems and ADAS.