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.

No description available.

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

Intelligent cruise control (ICC) has the potential to impact both roadway throughput and safety by assisting drivers in maintaining safe headways. This thesis explores this potential through comparisons of ICC to conventional cruise control (CCC) and manual driving. Accordingly, descriptions are given of both CCC and ICC systems. Furthermore, descriptions of ICC evaluation studies and car-following models are presented. The evaluation of ICC is conducted using data collected as part of the Field Operational Test (FOT) performed in Ann Arbor, Michigan. Two levels of analysis are presented in this thesis. The first level of analysis compares the usage of ICC to CCC from a macro level. This study demonstrated that ICC was used more along similar trips. In addition, it was shown that there was no difference in usage of the ON, SET, CANCEL and RESUME buttons. ICC resulted in a higher usage of the ACCEL button and a lower usage of the COAST button compared to CCC. Furthermore, the number of brake interventions while ICC was engaged was higher than CCC. Lastly, the macro-level analysis indicated that there was no difference in the number of near encounters for ICC and CCC. The second analysis makes comparisons at a micro level. The most probable speed, acceleration and headway for each driving mode as well as the probability of using cruise control (based on speed) were determined. The probability of ICC use exceeded CCC use for every freeway speed bin and all but two high-speed arterial speed bins. Finally, a car-following behavior comparison was performed. Manual driving resulted in larger headway values for speeds less than 80 km/h. The ICC speed-headway curve was similar to the CCC speed-headway curve created from high-speed arterial data. The mean headway-speed charts, however, indicated that ICC was more similar to manual driving. Exploration into the specific differences is needed in order to determine the impact of ICC on system safety. / Master of Science

The Hybrid Electric Vehicle Team (HEVT) at Virginia Tech participates in the 4-Year EcoCAR Mobility Challenge organized by Argonne National Laboratory. The objective of this competition is to modify a stock 2019 internal combustion engine Chevrolet Blazer and incorporate a hybrid powertrain and advanced driver assist systems. The Blazer has a P4 hybrid architecture which contains an electric traction motor on the rear axle and an internal combustion engine on the front axle. HEVT seeks to develop a vehicle with advanced driving capabilities to demonstrate energy savings by utilizing existing technologies. The hybrid market has generally been tailored to small compact vehicles however, a Chevrolet Blazer is a midsize utility vehicle that offers additional space with the benefit of increased fuel economy. The research discussed in this paper focuses on the design of a Signalized Intersection Control Strategy. First, research is performed on different methods of intersection control and implementation with an existing Model Predictive Adaptive Cruise Controller. Based on ease of integration into an existing tuned Eco Adaptive Cruise Control System (ACC), a control strategy operating in the background of the main vehicle controllers is chosen. The main topic of this research is the development and simulation of a Signalized Intersection Control Strategy that works through an Eco ACC system to achieve further energy savings during an approach to a connected intersection while ensuring rider safety. This paper expands on the current knowledge of vehicle utilization of Signal Phase and Timing (SPaT) signals through simulated test cases of a vehicle system model using MATLAB. In each case, the tractive energy consumption and travel times are analyzed for both the Eco ACC system with Signalized Intersection Control Strategy (informed) vehicle and an assumed uninformed driver for comparison. In the case of a vehicle approaching a green intersection which turns red several seconds after SPaT information is received, the informed system shows a 92% decrease or 75 Wh/mi reduction in propel energy consumption at when compared to an uninformed driver. However, in a similar case where the vehicle accelerates back to cruising speed after the light turns green, displays only an 11% decrease or 47 Wh/mi reduction in propel energy consumption at the wheel when compared to the uninformed driver. These simulations confirm that the Signalized Intersection Control Strategy reduces the propel energy consumption at the wheel during approaches to signalized intersections without extending the travel time greatly and in some cases at all. The results of this research show that the control strategy reduces tractive energy consumption while maintaining travel time. / Master of Science / The Hybrid Electric Vehicle Team (HEVT) at Virginia Tech participates in the 4-Year EcoCAR Mobility Challenge organized by Argonne National Laboratory. The objective of this competition is to change a stock 2019 internal combustion engine Chevrolet Blazer into a functioning hybrid. This conversion is accomplished with the addition of an electric motor to allow the vehicle to burn less gasoline and increase customer appeal. The hybrid market has generally been tailored to small compact vehicles however, a Chevrolet Blazer is a midsize utility vehicle that offers additional space with the benefit of increased fuel economy. The research discussed in this paper focuses on the design of a Signalized Intersection Control Strategy. First, research is performed on various methods of existing intersection speed control. Based on ease of integration, a background process is chosen to update the set speed of the vehicle. The main topic of this research is the development and simulation of a Signalized Intersection Control Strategy that achieves greater energy savings during approaches to intersections. This paper expands on the current knowledge of vehicle utilization of Signal Phase and Timing (SPaT) signals through simulated test cases of a vehicle system model using MATLAB. In the case of a vehicle approaching a green intersection which turns red several seconds later, the implemented strategy shows a 92% decrease in energy consumption when compared to an uninformed driver. However, a similar case where the vehicle accelerates back to cruising speed after the light turns green displays only an 11% decrease in energy consumption when compared to an uninformed driver. These simulations confirm that the Signalized Intersection Control Strategy successfully reduces energy consumption without significant travel time extensions. The results of this research show that the control strategy reduces tractive energy consumption while maintaining travel time.

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.

No description available.

<p>Vehicle simulators are becoming more common in vehicle industries. Company earns lot of money on simulations instead of real tests. Real tests are necessary but not made so extensively as before.</p><p>In this thesis the building of an vehice simulator will be described and a comparison between three different cruise controls. The three cruise controls are PI-regulator, a regulator who regulates after positions in the terrain and a MPC-regulator. The reason for choosing this three is to see the difference between simple regulation and more complex regulation with respect to fuel consumption, travel time and complexity.</p><p>The vehicle simulator is made in Matlab/Simulink, Visual Studio and Open Scene Graph. The facilities needed for runnning the simulator is a relative good computer with a grapics card on at least 128 MB RAM plus a steering wheel and pedals for brake and gas to achieve best feeling. A keyboard can be used but it reduces almost all feeling.</p><p>After several simulations a conclusion was made. The MPC-regulator was the regulator who consumed least fuel and travel time. The regulator who regulates after positions in the terrain was not too far away. It would be interesting to do more research about it. In fact it is only a PI-regulator who makes ``clever'' decisions when a hill with enough slope appears. With enough slope means a downhill where the vehicle can accelerate without the use of fuel and an uphill where the vehicle can not keep its speed with maximum use of fuel.</p><p>A conclusion was stated that the friction and height profile influenced on settings for the PI-regulator and with some adjustments on this settings, fuel could be saved.</p>

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.