Driving can provide independent and efficient mobility. However, according to the driving license directive (91/439/EEC) are persons with locomotor impairments are only allowed drive if their disabilities can be compensated. Compensation can be realised by vehicle adaptations. The directive provides meagre guidance on how vehicles should be adapted or how to verify that the compensatory requirements are fulfilled. This is a gap in the current process for licensing drivers with physical disabilities. Furthermore, the Swedish process from driver assessment to driver licensing and adaptation approval is complex, fragmented, and suffer from lack of communication between involved authorities. The objective of this thesis was to contribute to the development of a method to evaluate vehicle adaptations for driver with physical disabilities. The focus was on the evaluation of adaptations for steering, accelerating and braking. Three driving simulator experiments and one manoeuvre test with adapted vehicles were conducted. A group of drivers with tetraplegia driving with hand controls were compared to able-bodied drivers in the first experiment. Even if the drivers with tetraplegia had a longer brake reaction time they performed comparable to the able-bodied drivers. However, they spent more effort and were more tired in order to perform as well as the able-bodied drivers. It was concluded that the adaptation was not sufficient. An Adaptive Cruise Controller (ACC) was tested in the second experiment in order to find out if it could alleviate the load on drivers using hand controls. It was found that the ACC decreased the workload on the drivers. However, ACC systems need to be adjustable and better integrated. The results from the first two experiments were used to provide some guidelines for ACCsystems to be used by drivers with disabilities. The third experiment was preceded by a manoeuvre test with joystick controlled cars. The test revealed some problems, which were attributed to time lags, control interference, and lack of feedback. Four joystick designs were tested with a group of drivers with tetraplegia in the third experiment. It was concluded that time lags should be made similar to what is found in standard cars. Lateral and longitudinal control should be separated. Active feedback can improve vehicle control but should be individually adjusted. The experiments revealed that drivers with the same diagnose can be functionally very diverse. Thus, an adaptation evaluation should be made individually. Furthermore, the evaluation should include a manoeuvre test. Finally, it was concluded that the evaluation approach applied in the experiments was relevant but needs to be further developed.

In recent decades, open, online learning environments have become progressively more popular and well-funded. An integral aspect of this open learning movement is the transition of a substantial amount of control of the learning process from designers and instructors to the users engaging with the environment. With heavy investments coming from both the public and private sectors, and an ever-growing market of online learners, it is crucial that we better understand how the provision of user control over the learning process affects the quality of that learning process. The purpose of this study was to investigate the effects of one aspect of open learning environments that has yet to be fully understood: user choice of learning sequence, or non-sequential user choice. Building on previous research with open educational resources (OERs) designed to help drivers learn about adaptive cruise control (ACC), an advanced car safety system, this research compared the learning process of subjects with (N = 42) and without (N = 42) control of the learning sequence. Specifically, this study sought to investigate two core issues: 1) the effect(s), positive or negative, that non-sequential user choice has on the development of mental models of ACC, as measured by a post-test assessment; and 2) the relationship among post-test performance, chosen order of resources, and time spent engaging with individual learning resources. To examine these issues, two primary analyses were completed. To address the effect of non-sequential user choice, subjects’ performance on scenario problems and a declarative knowledge post-test was compared using independent sample t-tests (α = .05). A multiple regression analysis was conducted to investigate the relationship among post-test performance, chosen order of resources, and time spent on each of three learning resources (α = .05). Subjects in the experimental (choice) condition scored significantly worse on the post-test assessment than subjects in the control (non-choice) condition (t[82] = -2.116, p < .05, d = -0.462). The regression analysis found a significant regression equation (F(4,37) = 3.930, p < .05) with an R2 of 0.298 (Adjusted R2 = 0.222). Surprisingly, however, only one of the resource time predictor variables was an individually significant predictor of post-test performance. Possible explanations for these findings are explored based on the available research literature. These explanations include the possibility of choice overload, poor decision-making by subjects, confusion due to a lack of instructional guidance, and the development of choice apathy. However, further research is necessary to determine why non-sequential user choice had a negative effect, as well as to expand research on non-sequential user choice to other contexts and content areas.

This master thesis deals with implementation of the radar sensor for adaptive cruise control system. It discusses used technologies and processes and documents implementation of signal processing serving for the purpose of adaptive cruise control. It also describes the testing on the real data gathered in traffic. Texas Instrument's AWR1843 radar module was used as the sensor. This sensor represents currently very popular milimeter wave technology radars. Result of this master thesis are two implemented systems processing digital signal. One of them is a prototype application of the adaptive cruise control system, which also visualises the data. The other is implemented firmware of radar module doing real-time on-chip signal processing according to adaptive cruise control logic.

Advanced Driving Assistance Systems (ADAS) has the potential to decrease the number of fatal accidents in traffic. However, in some cases, drivers with the systems in their car are resistant against using them. Exploring the underlying reasons and factors of the low-usage of ADAS was the purpose of this thesis. The thesis consists of Study I, an exploratory interview study with ten drivers who had cars with ADAS. The goal of Study I was to highlight the possible reasons behind the low usage of ADAS. The results of Study I were used to design Study II, which consisted of a survey targeted to drivers who had access to the ADAS adaptive cruise control and lane keep assist (N = 49). The results indicate that the factors or circumstances that affect usage depend on the ADAS and the user groups. Some identified underlying factors for low usage behavior of ADAS are the need to monitor the vehicle more when ADAS is activated and lack of trust in own ability when using ADAS compared to the high usage group. / Advanced Driving Assistance Systems (ADAS) har potential att förhindra antalet dödsfall i trafiken. Det förekommer att förare som har systemen i sin bil, väljer bort att använda dem. Syftet med den här uppsatsen var att undersöka underliggande orsaker och faktorer till låg användningsgrad av ADAS. Uppsatsen består av två studier. Studie I är en explorativ intervjustudie med tio förare som hade bilar med ADAS. Målet med Studie I var att ringa in de möjliga bakomliggande faktorerna för låg användningsgrad av ADAS. Resultaten från Studie I användes för att utforma en enkätstudie till Studie II som var riktad till förare som hade bilar med förarstödsystemen adaptiv farthållare och körfältsassistans (N = 49). Resultaten pekar på att de underliggande orsakerna och faktorerna beror på vilken ADAS som avses samt vilket användargrupp föraren tillhör. Några underliggande faktorer för låg användingsgruppen tycks vara känsla av att behöva övervaka fordonet samt lägre grad av tilltro till den egna förmågan än vad höganvändingsgrupper rapporterade.

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The autonomous driving (AD) industry is advancing at a rapid pace. New sensing technology for tracking vehicles, controlling vehicle behavior, and communicating with infrastructure are being added to commercial vehicles. These new automotive technologies reduce on road fatalities, improve ride quality, and improve vehicle fuel economy. This research explores two types of automotive sensor fusion systems: a novel radar/camera sensor fusion system using a long shortterm memory (LSTM) neural network (NN) to perform data fusion improving tracking capabilities in a simulated environment and a traditional radar/camera sensor fusion system that is deployed in Mississippi State’s entry in the EcoCAR Mobility Challenge (2019 Chevrolet Blazer) for an adaptive cruise control system (ACC) which functions in on-road applications. Along with vehicles, pedestrians, and cyclists, the sensor fusion system deployed in the 2019 Chevrolet Blazer uses vehicle-to-everything (V2X) communication to communicate with infrastructure such as traffic lights to optimize and autonomously control vehicle acceleration through a connected corridor

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<p>Driver Assistance Systems like Adaptive Cruise Control (ACC) can help prevent accidents by reducing the workload on the driver. However, this can only be accomplished if the driver can rely on the system to perform safely even in the presence of faults.</p> <p>In this thesis we develop an Adaptive Cruise Control model that will be used to investigate Hybrid Systems Fault Diagnosis techniques. System Identification is performed upon an electric motor to obtain its transfer function. This electric motor belongs to a 1/10th scale RC car that is being used as part of a test bench for the Adaptive Cruise Control system. The identified model is then used to design a hybrid controller which will switch between a set of LQR controllers to create an example Adaptive Cruise Controller. The model of the controller is then used to generate fixed point code for implementation on the testbed and validation against the model controller. Finally a detailed hazard analysis of the resulting system is performed using Leveson's STPA.</p> / Master of Applied Science (MASc)

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.

Traffic signals typically produce vehicle stops and thus increase vehicle fuel consumption levels. Vehicle stops produced by traffic signals, decrease vehicle fuel economy on arterial roads making it significantly lower than that on freeways. Eco-Cooperative Adaptive Cruise Control (Eco-CACC) systems can improve vehicle fuel efficiency by receiving Signal Phasing and Timing (SPaT) data form downstream signalized intersections via vehicle-to-infrastructure communication. The algorithm that was developed in an earlier study provides advisory speed recommendations to drivers to reduce vehicle fuel consumption levels in the vicinity of traffic signalized intersections. The research presented in this thesis enhances the algorithm by adding a queue length estimation component and incorporates the algorithm in the INTEGRATION microscopic traffic simulation software to test the system under varying conditions. The enhanced Eco-CACC algorithm is then tested in a simulation environment considering different levels of connected vehicle (CV) market penetration levels. The simulation analysis demonstrates that the algorithm is able to reduce the vehicle fuel consumption level by as high as 40%. Moreover, the overall benefits of the proposed algorithm is evaluated for different intersection configurations and CV market penetration rates (MPRs). The results demonstrate that for single lane approaches, the algorithm can reduce the overall fuel consumption levels and that higher MPRs result in larger savings. While for multilane approaches, lower MPRs produce negative impacts on fuel efficiency; only when MPRs are greater than 30%, can the algorithm work effectively in reducing fuel consumption levels. Subsequently a sensitivity analysis is conducted. The sensitivity analysis demonstrates that higher market penetration rates of Eco-CACC enabled vehicles can improve the environmental benefits of the algorithm, and the overall savings in fuel consumption are as high as 19% when all vehicles are equipped with the system. While, on multi-lane approaches, the algorithm has negative impacts on fuel consumption levels when the market penetration rate is lower than 30 percent. The analysis also indicates that the length of control segments, the SPaT plan, and the traffic demand levels affect the algorithm performance significantly. The study further demonstrates that the algorithm has negative impacts on fuel consumption levels when the network is over-saturated. / Master of Science