The research objectives were (1) to perform a detailed review of the literature on speech recognition technology and the attentional demands of driving; (2) to develop decision tools that assist designers of in-vehicle systems; (3) to experimentally examine automatic speech recognition (ASR) design parameters, input modalities, and driver ages; and (4) to provide human factors recommendations for the use of speech recognition technology in automotive applications. Two experiments were conducted to determine the effects of ASR design parameters, input modality, and age on driving performance, system usability, and driver preference/acceptance. Eye movement behavior, steering input behavior, speed maintenance behavior, reaction time to forward scene event, task completion time, and task completion errors when driving and performing in-vehicle tasks were measured. Driver preference/acceptance subjective data were also recorded. The results showed that ASR design parameters significantly affected measures of driving performance, system usability, and driver preference/acceptance. However, from a practical viewpoint, ASR design parameters had a nominal effect on driving performance. Differences measured in driving performance brought on by changes in ASR system design parameters were small enough that alternative ASR system designs could be considered without impacting driving performance. No benefits could be claimed for ASR systems improving driving safety/performance compared to current manual-control systems. Speech recognition system design demonstrated a moderate influence on the usability of in-vehicle tasks. Criteria such as task completion times and task completion errors were shown to be different between speech-input and manual-input control methods, and under different ASR design configurations. Therefore, trade-offs between ASR system designs, and between speech-input and manual-input systems, could be evaluated in terms of usability. Finally, ASR system design had a nominal effect on driver preference/acceptance. Further research is warranted to determine if long-term use of ASR systems with less than optimal design parameters would result in significantly lower values for driver preference/acceptance compared to data collected in this research effort. Human factors recommendations for the use of ASR technology in automotive applications are included. The recommendations are based on the empirical research and the literature review on speech recognition technology and the attentional demands of driving. / Ph. D.

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 dissertation, we study macroscopic traffic flow modeling for intelligent transportation systems. Based on the characteristics of traffic flow evolution, and the requirement to realistically predict and ameliorate traffic flow in high traffic regions, we consider traffic flow modeling for intelligent transportation systems. Four major traffic flow modeling issues, that is, accurately predicting the spatial adjustment of traffic density, the traffic behavior on a long infinite road and on a road having egress and ingress to the flow, affect of driver behavior on traffic flow, and the route merit are investigated. The spatial adjustment of traffic density is investigated from a velocity adjustment perspective. Then the traffic behavior based on the safe distance and safe time is studied on a long infinite road for a transition and uniform flow. The traffic flow transition behavior is also investigated for egress and ingress to the flow having a regulation value which characterizes the driver response. The variation of regulation value refines the traffic velocity and density distributions according to a slow or aggressive driver response. Further, the influence of driver behavior on traffic flow is studied. The driver behavior includes the physiological and psychological response. In this dissertation, route merits are also developed to reduce the trip time, pollution and fuel consumption. Performance results of the proposed models are presented. / Graduate / 0543, 0544, 0548 / khanz@uvic,ca

<p>Many traffic accidents are caused by driver unawareness. This includes fatigue, drowsiness and distraction. In this thesis two systems are described that could be used to decrease the number of accidents. In the first part of this thesis a system using long-term information to warn drivers suffering from fatigue is developed. Three different versions with different criteria are evaluated. The systems are shown to handle more then 60% of the cases correctly.</p><p>The second part of this thesis examines the possibilities of developing a warning system based on the predicted time-to-lane crossing, TLC. A basic TLC model is implemented and evaluated. For short time periods before lane crossing this may offer adequate accuracy. However the accuracy is not good enough for the model to be used in a TLC based warning system to warn the driver of imminent lane departure.</p>

This thesis will present the design, implementation, and testing of a high voltage Charge-Coupled Device (CCD) controller ASIC for the Large Synoptic Survey Telescope (LSST), which will be used to study dark energy and dark matter. The LSST observatory, which includes a 3.2-gigapixel camera, will cover the entire sky every three nights by taking continuous 15-second exposures. The CCD controller ASIC, or Sensor Control Chip (SCC), will provide five CCD driver channels that are capable of generating serial or parallel clock signals for the LSST’s imaging sensors during readout mode. The SCC will also provide three programmable bias voltages for the CCDs along with eight supplementary programmable voltages and currents for the CCD’s output drain terminals. Additionally, the controller ASIC includes eight control signals for a separate Analog Signal Processing Integrated Circuit (ASPIC) that is designed as the readout chip for LSST. The SCC is designed to operate down to 153 K. Fabricated in a commercially available 0.8-micron Bipolar-CMOS-DMOS Silicon-On-Insulator (BCD-SOI) process, the SCC has been verified to meet all design requirements.

Many traffic accidents are caused by driver unawareness. This includes fatigue, drowsiness and distraction. In this thesis two systems are described that could be used to decrease the number of accidents. In the first part of this thesis a system using long-term information to warn drivers suffering from fatigue is developed. Three different versions with different criteria are evaluated. The systems are shown to handle more then 60% of the cases correctly. The second part of this thesis examines the possibilities of developing a warning system based on the predicted time-to-lane crossing, TLC. A basic TLC model is implemented and evaluated. For short time periods before lane crossing this may offer adequate accuracy. However the accuracy is not good enough for the model to be used in a TLC based warning system to warn the driver of imminent lane departure.

Abstract Four high-performance circuits design techniques for embedded DRAM are proposed. First, a negative voltage generator having high efficiency is proposed to provide the negative voltage for the modified word line driver. The negative voltage generator circuits could be manufactured in n-Well CMOS process, and its operation achieve optimal output voltage. When 2.0-V supplied voltage is applied, the output voltage of -1.6-V is obtained. Even though, the supplied voltage is scaled down to 1.5-V, the output voltage can still achieve -1.05-V. In contrast, the output voltage of traditional one under 2.0-V supplied voltage is only -0.67-V. Second, a fast wordline driver suitable for PMOS pass transistor is proposed. The wordline driver improves the turned-on time by 26.8ns compared with the traditional one and raises the operating speed by 79%. Third, a new reduced clock-swing driver is proposed. Under 2.0-V supplied voltage and 100MHz operating frequency, the total power consumption of the new driver working with RCSFF is reduced by 10% than that of traditional one working with RCSFF. For the above advantage of low power, the new driver is thus more suitable for embedded DRAM applications. Fourth, a modified hierarchical read bus amplifier is proposed. The read bus amplifier is based on the new sense-amplifier. It could drive the output by full-swing voltage. It improves the sensing speed by 2.1ns. And it got the same advantage of no dc idling current as the traditional N&PMOS cross-coupled amplifier. In this thesis, finally, the performance of these circuits is also integrated and examined in an 1-Kbit embedded DRAM test circuit. The simulation RAS access time of 27.9ns is achieved under 2.0V supplied voltage and loading of 16-Mbit embedded DRAM. This indicated the above proposed circuits could be applied in the low voltage and high speed embedded DRAM.

This thesis will present the design, implementation, and testing of a high voltage Charge-Coupled Device (CCD) controller ASIC for the Large Synoptic Survey Telescope (LSST), which will be used to study dark energy and dark matter. The LSST observatory, which includes a 3.2-gigapixel camera, will cover the entire sky every three nights by taking continuous 15-second exposures. The CCD controller ASIC, or Sensor Control Chip (SCC), will provide five CCD driver channels that are capable of generating serial or parallel clock signals for the LSST’s imaging sensors during readout mode. The SCC will also provide three programmable bias voltages for the CCDs along with eight supplementary programmable voltages and currents for the CCD’s output drain terminals. Additionally, the controller ASIC includes eight control signals for a separate Analog Signal Processing Integrated Circuit (ASPIC) that is designed as the readout chip for LSST. The SCC is designed to operate down to 153 K. Fabricated in a commercially available 0.8-micron Bipolar-CMOS-DMOS Silicon-On-Insulator (BCD-SOI) process, the SCC has been verified to meet all design requirements.

No description available.

With the growing complexity of vehicle control systems it is becoming increasingly important to understand the interaction between drivers and vehicles. Existing driver models do not adequately characterise limitations resulting from drivers’ physical systems. In particular, sensory dynamics limit the ability of drivers to perceive the states of real or simulated vehicles. Therefore, the aim of this thesis is to understand the impact of sensory dynamics on the control performance of a human driver in real and virtual environments. A new model of driver steering control is developed based on optimal control and state estimation theory, incorporating models of sensory dynamics, delays and noise. Some results are taken from published literature, however recent studies have shown that sensory delays and noise amplitudes may increase during an active control task such as driving. Therefore, a parameter identification procedure is used to fit the model predictions to measured steering responses of real drivers in a simulator. The model is found to fit measured results well under a variety of conditions. An initial experiment is designed with the physical motion of the simulator matching the motion of the virtual vehicle at full scale. However, during more realistic manoeuvres the physical motion must be scaled or filtered, introducing conflicts between measurements from different sensory systems. Drivers are found to adapt to simple conflicts such as scaled motion, but they have difficulty adapting to more complicated motion filters. The driver model is initially derived for linear vehicles with stochastic target and disturbance signals. In later chapters it is extended to account for transient targets and disturbances and vehicles with nonlinear tyres, and validated once again with experimental results. A series of simulations is used to demonstrate novel insights into how drivers use sensory information, and the resulting impact on control performance. The new model is also shown to predict difficulties real drivers have controlling unstable vehicles more reliably than existing driver models.