Engineering Design Synthesis of Sensor and Control Systems for Intelligent Vehicles

Yizhen Zhang

Ph.D. Thesis, California Institute of Technology
(May 2006).


This thesis investigates the application of formal engineering design synthesis methodologies to the development of sensor and control systems for intelligent vehicles with a series of meaningful case studies.

A formal engineering design synthesis methodology based on evolutionary computat ion is presented, with special emphasis on dealing with modern engineering design challenges, such as high or variable complexity of design solutions, multiple conflicting design objectives, and noisy evaluation results, etc., which are common when design and optimization of distributed control systems such as intelligent vehicles are considered. The efficacy of the evolutionary design synthesis method is validated through multiple different case studies, where a variety of novel design solutions are generated to represent different engineering design trade-offs, and they have achieved performances comparable to, if not better than, that of hand-coded solutions in the same simplified environment. More importantly, this automatic design synthesis method shows great potential to handle more complex design problems with a large number of design variables and multi-modal noise involved, where a good hand-coded solution may be very difficult or even impossible to obtain. In summary, the evolutionary design synthesis methodology appears promising to

In addition, multiple levels of vehicle simulation models with different computational cost and fidelity as well as necessary driver behaviors are implemented for different types of simulation experiments conducted for different research purposes. Efforts are made to try to get as much as possible out of limited computational resources, such that good candidate solutions can be generated efficiently with less computational time and human engineering effort.

Furthermore, different threat assessment measures and collision avoidance algorithms are reviewed and discussed. A new threat assessment measure, time-to-last-second-braking (Tlsb), is proposed, which directly characterizes human natural judgment of the urgency and severity of threats in terms of time. Based on driver reaction time experimental results, new warning and overriding criteria are proposed in terms of the new Tlsb measure, and the performance is analyzed statistically in terms of two typical sample pre-crash traffic scenarios. Less affected by driver behavior variability, the new criteria characterize the current dynamic situations better than the previous ones, providing more appropriate warning and more effective overriding at the last moment. Finally, the possibility of frontal collision avoidance through steering (lane-changing) is discussed, and similarly the time-to-last-second-steering (Tlss) measure is proposed and compared with Tlsb.