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 author's concept of the distance and tracking control problem for autonomous vehicles relates to the cooperative behaviour of two successive vehicles travelling in the same environment. This behaviour requires one vehicle, designated the leader to move autonomously around it's environment with other vehicles, designated followers maintaining a coincident travel path and desired longitudinal distance with respect to the leader. Distance and tracking control is beneficial in numerous applications including guiding autonomous vehicles in Intelligent Transport Systems (ITS) which increases traffic safety and the capacity of pre-existing road infrastructure. Service robotics may also benefit from the cost savings and flexibility offered by distance and tracking control which enables a number of robots to cooperate together in order to achieve a task beyond the capabilities ofjust one robot. Using a distance and tracking control scheme an intelligent leader robot may guide a number of less intelligent (and therefore less costly and less complex) followers to a work-site to perform a task. The author's approach to the distance and tracking control problem consisted of two separate solutions - an initial solution used as a starting point and learning experience and a second, more robust, fuzzy control-based solution. This thesis briefly describes the initial solution, but places a greater emphasis on the second solution. The reason for this is that the fuzzy control-based solution offers significant improvement on the initial solution and was developed based on conclusions drawn from the initial solution. Most implementations of distance and tracking control, sometimes referred to as Intelligent Cruise Control (ICC) or platooning, are limited to longitudinal distance control only. The leader tracking control is performed either implicitly by a separate lane-following control system or by human drivers. The fuzzy control-based solution offered in this thesis performs both distance and tracking control of an autonomous follower vehicle with respect to a leader vehicle in front of it. It represents a simple and cost effective solution to the requirements of autonomous vehicles operating in ITS schemes - particularly close formation platooning. The follower tracks a laser signal emitted by the leader and monitors the distance to the follower at the same time using ultrasonic ranging techniques. The follower uses the data obtained from these measuring techniques as inputs to a fuzzy controller algorithm to adjust its distance and alignment with respect to the leader. Other systems employed on road vehicles utilise video-based leader tracking, or a range of lane-following methods such as magnetometer or video-based methods. Typically these methods are disadvantaged by substantial unit and/or infrastructure costs associated with their deployment. The limitations associated with the solutions presented in this thesis arise in curved trajectories at larger longitudinal distance separations between vehicles. The effects of these limitations on road vehicles has yet to be fully quantified, however it is thought that these effects would not disadvantage its use in close formation platooning. The fuzzy control-based distance and tracking control solution features two inputs, which are the distance and alignment of the follower with respect to the leader. The fuzzy controller asserts two outputs, which are left and right wheel velocities to control the speed and trajectory of a differential drive vehicle. Each of the input and output fuzzy membership functions has seven terms based around lambda, Z-type and S-type functions. The fuzzy rule base consists of 49 rules and the fuzzy inference stage is based on the MAX/MIN method. A Centre of Maximum (CoM) def'uzzification method is used to provide the two crisp valued outputs to the vehicle motion control. The methods chosen for the fuzzy control of distance and tracking for autonomous vehicles were selected based on a compromise between their computational complexity and performance characteristics. This compromise was necessary in order to implement the chosen controller structure on pre-existing hardware test beds based on an 8-bit microcontrollers with limited memory and processing resources. Overall the fuzzy control-based solution presented in this thesis effectively solves the distance and tracking control problem. The solution was applied to differential drive hardware test-beds and was tested to verify performance. The solution was thoroughly tested in both the simulation environment and on hardware test-beds. Several issues are identified in this thesis regarding the application of the solution to other platforms and road vehicle use. The solution will be shown to be directly portable to service robotics applications and, with minor modifications, applicable to road vehicle close-formation platooning.