In comparison to conventional cruise control, adaptive cruise control (ACC) vehicles are capable of sensing forward traffic and slowing to accommodate as necessary. When no forward vehicles are present, ACC function is the same as conventional cruise control. However, with ACC, when a slower vehicle is detected, the ACC system will decelerate and follow at a selected time-based distance. While slowing to follow, the driver will experience a system-controlled deceleration of the ACC vehicle. An experiment was conducted to evaluate driver preferences for the distance at which the primary deceleration occurs and the level of deceleration that is obtained. Driver intervention was required in one trial and driver response behavior was measured. Ten men and ten women in two age groups evaluated the decelerations from a cruise speed of 70mph to a following speed of 55mph behind a confederate lead vehicle on the highway. Evaluations can be made using four scales: Good vs. Bad, Comfortable vs. Uncomfortable, Jerky vs. Smooth, and Early vs. Late. Decelerations of approximately 0.06g which occur approximately 200ft to 250ft behind the lead vehicle were most preferred. Prior to intervention, foot position ranged from a point directly below the brake pedal to 16.4in from the brake pedal. Foot motion began between 21.12s time-to-collision (TTC) and 3.97s TTC. Eighty percent of the participants paused to "cover" the brake before final motion to activate the brake. The older age group intervened (braked) later than the younger age group. Driver braking after intervention ranged from 0.16g to 0.32g. / Master of Science

In this thesis, we present research on synthesizing autonomous driving with shared autonomy using Unity Engine. Adaptive Cruise Control (ACC) is considered as level 1 autonomous vehicle, which has been studied by academia and commercialized by industry. Cooperative Adaptive Cruise Control (CACC) system is an expansion of ACC, in which communication is set up between members to share driving information. Shared autonomy is a subject about human-computer interactivities. In our research, we developed a highly customizable 3D environment. We can simulate various driving scenarios and analyze the performance of different driving methods from human driving to CACC. The result of simulation proves the safety and efficiency of CACC, and the project also provides a potential of assisting the improvement of autonomous vehicles.

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In this master thesis the possibilities of combining an adaptive cruise control with information about the road ahead has been studied. The focus has been to investigate the possibility to save fuel by using information about road topology, Look-Ahead. An adaptive cruise control, AiCC, is used when there are preceding vehicles and when the driver in addition to choosing a desired travel speed for the vehicle also chooses a desired time gap that is to be kept to preceding vehicles travelling slower than the own vehicle. Using information about the road ahead and information of preceding vehicles a controller with the function to adapt the speed to the preceding vehicle, target, and at the same time reduce the fuel usage has been constructed. The controller considers the topography on the road and the distance to the target to be able to reduce the utilization of the brakes in steep downhills and to reduce fuel by slowing down before the downhill and then gain speed due to the gravitational force. The controller uses the assumption that the target travels with constant velocity at all time. The work has included simulations with two different test roads, one in Sweden with shorter and not so steep downhills. The other road is placed in Germany and has long and steep downhills. In the simulations three various time gaps, 1, 2 and 3 seconds, has been used and three different weights of the preceding vehicle, 20, 40 and 50 tonnes. The vehicle with the controller using adaptive cruise control and Look-Ahead has a weight of 40 tonnes. The results shows that fuel can be saved, using information about the road ahead in combination with an adaptive cruise control. The best result is obtained when the road contains steep and long downills, where the vehicle will gain speed due to the gravitational force. For the long and steep downhills the result is best when the target weight is 40 and 50 tonnes. When the downhills are smaller and not so steep the best result is obtained when the target weight is 20 tonnes. For these downhills the assumption that the target travels at constant speed makes the vehicle reduce the speed too much before the downhill, not considering that a heavier vehicle will accelerate in the downhill as well. The time gaps that gives the best result is time gap 1 second. This is due to that the aerodynamic force acting upon the vehicle is reduced when there is a preceding vehicle at a not too far distance. The smaller the distance to the preceding vehicle the more the aerodynamic force is reduced.

Safety concerns have been keeping autonomous vehicles off the roads for decades, although the main drivers for introducing some autonomy are to increase safety, reduce congestion, and greenhouse gas emissions. Safety is a vast topic that includes the safety of the system alone, known as string stability, and the safety of the system on public roads. This thesis provides experimental validation of the string stability of the Assume-Guarantee approach. This approach suggests that each agent models the interactions with neighbors as bounded disturbances while simultaneously self-imposing symmetric magnitude bounds. Two main controllers were tested in an indoor lab set-up: decentralized platooning and decentralized cooperative adaptive cruise controllers. First, we tested three versions of the platooning controller whose objective is to maintain a constant spacing. They differ in the assumptions and guarantees. We observed a robust performance with relaxed bounds and some violations as the bounds become tighter and tighter. Second, we modified and transformed the platoon model into cooperative adaptive cruise control (CACC). Unlike the platoon controller, the cooperative adaptive cruise controller keeps the time gap constant. Two experiments were conducted at different velocities to evaluate the limitation of the controller. The results show a stable and smooth performance.

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The Hybrid Electric Vehicle Team (HEVT) of Virginia Tech is partaking in the 4-Year EcoCar Mobility Challenge organized by Argonne National Labs. The objective of this competition is to modify a stock 2019 traditional internal combustion engine Chevrolet Blazer and to transform the vehicle into a P4 hybrid. Due to the P4 Hybrid architecture, the HEVT vehicle has an internal combustion engine on the front axle and an electric motor on the rear axle. The goal of this competition is to create a vehicle that achieves better fuel economy and increases customer appeal. The general target market of hybrids is smaller vehicles. As a midsize sport utility vehicle (SUV), the Blazer offers a larger vehicle with the perk of better fuel economy. In the competition, the vehicle is assessed on the ability to integrate advanced vehicle technology, improve consumer appeal, and provide comfort for the passenger. The research of this paper is centered around the design of a full range longitudinal Adaptive Cruise Control (ACC) algorithm. Initially, research is conducted on various linear and nonlinear control strategies that provide the necessary functionality. Based on the ability to predict future time instances in an optimal method, the Model Predictive Control (MPC) algorithm is chosen and combined with other standard control strategies to create an ACC system. The main objective of this research is the implementation of Adaptive Cruise Control features that provide comfort and energy savings to the rider while maintaining safety as the priority. Rider comfort is achieved by placing constraints on acceleration and jerk. Lastly, a proper energy analysis is conducted to showcase the potential energy savings with the implementation of the Adaptive Cruise Control system. This implementation includes tuning the algorithm so that the best energy consumption at the wheel is achieved without compromising vehicle safety. The scope of this paper expands on current knowledge of Adaptive Cruise Control by using a simplified nonlinear vehicle system model in MATLAB to simulate different conditions. For each condition, comfort and energy consumption are analyzed. The city 505 simulation of a traditional ACC system show a 14% or 42 Wh/mi reduction in energy at the wheel. The city 505 simulation of the environmentally friendly ACC system show a 29% or 88 Wh/mi reduction in energy at the wheel. Furthermore, these simulations confirm that maximum acceleration and jerk are bounded. Specifically, peak jerk is reduced by 90% or 8 m/s3 during a jerky US06 drive cycle. The main objective of this analysis is to demonstrate that with proper implementation, this ACC system effectively reduces tractive energy consumption while improving rider comfort for any vehicle. / Master of Science / The Hybrid Electric Vehicle Team (HEVT) of Virginia Tech is partaking in the 4-Year EcoCar Mobility Challenge organized by Argonne National Labs. The objective of this competition is to modify a stock 2019 Chevrolet Blazer into a hybrid. This modification is accomplished by creating a vehicle that burns less gasoline and increases customer appeal. The general target market of hybrids is smaller vehicles. As a midsize sport utility vehicle (SUV), the Blazer offers a larger vehicle with the perk of better fuel economy. In the competition, the vehicle is assessed on the ability to integrate advanced technology, improve consumer appeal, and provide comfort for the passenger. The research of this paper is centered around the design of Adaptive Cruise Control (ACC). Initially, research is conducted on various control strategies that provide the necessary functionality. A controller that predicts future events is selected for the Adaptive Cruise Control. The main objective of this research is the implementation of Adaptive Cruise Control features that provide comfort and energy consumption savings to the rider while maintaining safety as the priority. Rider comfort is achieved by creating a smoother ride. Lastly, a proper energy analysis showcases the potential energy savings with the implementation of the Adaptive Cruise Control system. The scope of this paper expands on current knowledge of Adaptive Cruise Control by using a simplified vehicle model to simulate different conditions. The city simulations of a traditional ACC system show a 14% reduction in energy at the wheel. City simulations of the environmentally friendly Adaptive Cruise Controller show a 29% reduction in energy. Both of these simulations allow for comfortable ride. Specifically, maximum car jerk is reduced by 90%. The main objective of this analysis is to demonstrate that with proper implementation, this ACC system effectively reduces energy consumption at the wheel while improving rider comfort.

Trust in automatic controllers may have an impact on awareness of system limitations and ultimately use of these systems. The purpose of this research is to determine the effects and likelihood of too much trust (overtrust) in drivers that use adaptive cruise control (ACC), a type of automatic controller that maintains vehicle speed and headway time. To add to the existing literature, this study aimed to define a significant relationship among trust, use, and awareness of ACC limitations. A post mailed or electronic-based survey was distributed to potential ACC users with 118 responses used for the main analysis. The survey responses provided demographic information, illustrated levels of trust, awareness of ACC limitations, and system use. A hierarchical cluster analysis of the data related to trust in ACC produced four clusters: overtrust, cautious, neutral, and distrust. Binary and multinomial logistic regression models then predicted the likelihood for overtrust (cluster membership). Participants in the overtrust cluster displayed the lowest level of awareness regarding ACC limitations and the highest levels of misuse. Users were more likely to overtrust ACC if they were male, misused the system, lacked limitation awareness, and indicated a willingness to opt for ACC in their next vehicle. Overtrust in ACC was shown to lead to misuse and cloud awareness of the systems' limitations. Trust seemed to be formed in the initial interactions with ACC therefore a priori trust levels of potential users should be sought before appropriate guidance is given or demonstrated through test use.

In driving, multiple variables in automated systems such as adaptive cruise control (ACC) and active steering, and in the environment dynamically change and interact. This complexity makes it difficult for operators to track the activities and responses of automation. The inability of operators to monitor and understand automation's behavior contributes to inappropriate reliance, i.e. when an operator uses automation that performs poorly or fails to use automation that is superior to manual control. The decision to use or not use automation is one of the most important an operator can make, particularly in time-critical or emergency situations, therefore it is essential that an operator is calibrated in their automation use. An operator's decision to rely on automation depends on trust. System feedback provided to the operator is one means to calibrate trust in automation in that the type of feedback may differentially affect trust. The goal of this research is to help operators manage imperfect automation in real-time and to promote calibrated trust and reliance. A continuous information display that provides information on system behavior relative to its operating context is one means to promote such calibration. Three specific aims are pursued to test the central hypothesis of this dissertation that continuous feedback on the state and behavior of the automation informs operators of the evolving relationship between system performance and operating limits, therefore promoting accurate mental models and calibrated trust. The first aim applies a quantitative model to define the effect of understanding on driver-ACC interaction failures and to predict driver response to feedback. The second aim presents a systematic approach to define the feedback needed to support appropriate reliance in a demanding multi-task domain such as driving. The third aim assesses the costs and benefits of presenting drivers with continuous visual and auditory feedback. Together these aims indicate that continuous feedback on automation's behavior is a viable means to promote calibrated trust and reliance. The contribution of this dissertation is in providing purpose, process, and performance information to operators through a continuous, concurrent information display that indicates how the given situation interacts with the characteristics of the automation to affect its capability.

In this research, an autonomous vehicle function management methodology is studied. In accordance with the traffic situation, the decision making level chooses the optimal function that guarantees safety and minimizes fuel consumption while the control level is implemented via neuromorphic strategy based on the brain limbic system. To realize the decision making strategy, the Analytic Hierarchy Process (AHP) is used by considering driving safety, driving speed, and fuel efficiency as the objectives. According to the traffic situation and predefined driving mode, Lane Change Maneuver (LCM) and Adaptive Cruise Control (ACC) are chosen as the alternative functions in the AHP framework. The adaptive AHP is utilized to cope with dynamically changing traffic environment. The proposed adaptive AHP algorithm provides an optimal relative importance matrix that is essential to make decisions under a varying traffic situation and driving modes. The simulation results show that proposed autonomous vehicle function management structure produces optimal decisions that satisfy the driving preference. The stability of BLS based control is also investigated via Cell-to-Cell Mapping. In this research, autonomous vehicle functions such as Lane change maneuver and Adaptive cruise control are developed by means of BLS based control. The simulation results considered various traffic situations that an autonomous vehicle can encounter. To demonstrate the suggested control method Cell-to-Cell Mapping is utilized. Subsequently, the autonomous vehicle function management strategy is developed by Applying AHP and an adaptive AHP strategy is developed to cope with various traffic situations and driving modes. The suggested method is verified numerical simulations.