Charles Birdsong

A compensated acoustic actuator for systems with strong dynamic pressure coupling

This study improves the performance of a previously developed acoustic actuator in the presence of an acoustic duct system with strong pressure coupling. The speaker dynamics and the acoustic duct dynamics are first modeled separately. The two sysrems are then coupled, and the resulting system is modeled. A velocity sensor is developed and used in feedback compensation. The resulting speaker system has minimal magnitude and phase variation over a 20-200 Hz bandwidth. These conclusions are verified through experimental results.

Test Methods and Results for Sensors in a Pre-Crash Detection System

Automobile safety can be improved by anticipating a crash before it occurs and thereby providing additional time to deploy safety technologies. This requires an accurate, fast and robust pre-crash sensor that measures telemetry, discriminates between classes of objects over a range of conditions, and has sufficient range and area of coverage surrounding the vehicle. The sensor must be combined with an algorithm that integrates data to identify threat levels. No one sensor provides adequate information to meet these diverse and demanding requirements. However the requirements can be met with an optimal combination of multiple types of sensors. Previous work considered criteria for evaluating various sensors to find an optimal combination. This work presents test methods and results for selected sensors proposed for use in a precrash detection system. The test methods include static and dynamic telemetry testing to identify the range, accuracy, reliability and operating conditions for each sensor. Each sensor is evaluated for its ability to discriminate between classes of objects. The tests are applied to ultrasonic, laser range finder and radar sensors. These sensors were selected because they provide the maximum information, cover a broad range and region and are commercially viable in passenger vehicles.

Evaluation of Cost Effective Sensor Combinations for a Vehicle Precrash Detection System

The future of vehicle safety will benefit greatly from precrash detection – the ability of a motor vehicle to predict the occurrence of an accident before it occurs. There are many different sensor technologies currently available for pre-crash detection. However no single sensor technology has demonstrated enough information gathering capability within the cost constraints of vehicle manufacturers to be used as a stand alone device. A proposed solution consists of combining information from multiple sensors in an intelligent computer algorithm to determine accurate precrash information. In this paper, a list of sensors currently available on motor vehicles and those that show promise for future development is presented. These sensors are then evaluated based on cost, information gathering capability and other factors. Cost sensitivity is lower in large commercial vehicles than in personal vehicles due to their higher initial cost and longer life span making them a good candidate for early adoption of such a system. This work forms the basis for ongoing research in developing an integrated object detection and avoidance precrash sensing system.

ENHANCED VEHICLE IDENTIFICATION UTILIZING SENSOR FUSION AND STATISTICAL ALGORITHMS

Several studies in the area of vehicle detection and identification involve the use of probabilistic analysis and sensor fusion. While several sensors utilized for identifying vehicle presence and proximity have been researched, their effectiveness in identifying vehicle types has remained inadequate. This study presents the utilization of an ultrasonic sensor coupled with a magnetic sensor and the development of statistical algorithms to overcome this limitation. Mathematical models of both the ultrasonic and magnetic sensors were constructed to first understand the intrinsic characteristics of the individual sensors and also to provide a means of simulating the performance of the combined sensor system and to facilitate algorithm development. Preliminary algorithms that utilized this sensor fusion were developed to make inferences relating to vehicle proximity as well as type. It was noticed that while it helped alleviate the limitations of the individual sensors, the algorithm was affected by high occurrences of false positives. Also, since sensors carry only partial information about the surrounding environment and their measured quantities are partially corrupted with noise, probabilistic techniques were employed to extend the preliminary algorithms to include these sensor characteristics. These statistical techniques were utilized to reconstruct partial state information provided by the sensors and to also filter noisy measurement data. This probabilistic approach helped to effectively utilize the advantages of sensor fusion to further enhance the reliability of inferences made on vehicle identification. In summary, the study investigated the enhancement of vehicle identification through the use of sensor fusion and statistical techniques. The algorithms developed showed encouraging results in alleviating the occurrences of false positive inferences. One of the several applications of this study is in the use of ultrasonic-magnetic sensor combination for advanced traffic monitoring such as smart toll booths.Copyright

A PRE-CRASH SIMULATOR TO EVALUATE VEHICLE COLLISION PREDICTION ALGORITHMS

Abstract This paper describes a software simulator for pre-crash collision predictions. The simulator is a surrogate test bed for evaluating the performance of proposed pre-crash algorithms. It reads data from a file, transfers distance and angular position of a target to a test algorithm, and then records the algorithms predictions. To illustrate the simulator functionality, a simplified test algorithm is also described. This algorithm predicts collision risks based on assumptions about the size and acceleration of a target object, and the turning and braking limits of the host vehicle. The test algorithm is shown to be effective for cases where both the vehicle and the target move along straight lines but less effective for curved paths. This result is typical of the difficulty in predicting the future position of another vehicle when its motion may change suddenly in the short time before a crash event.

Modeling of Vehicle Magnetic Footprint in 3-D Space for Type Detection

This paper presents a modified multiple 3-D dipole model to capture the complex magnetic footprints created by different vehicles on the road. In this study, laboratory bench tests were carried out to record the magnetic behavior of single dipole magnets and road tests were then conducted to record the complex magnetic behavior of vehicles. A preliminary 2-D modified dipole model similar to literature was developed and then expanded to a high fidelity 3-D multiple dipole model. An exhaustive parametric study was conducted to identify relevant design parameters for model matching. The 2-D model helped corroborate the results of laboratory bench tests using magnets and showed that the magnetic sensor was capable of identifying different sized magnets based on their magnetic footprints. Similar conclusions were made when applying the 3-D multiple dipole model to the experimental road tests. Different analytical functions were developed to help distinguish vehicle types based on their magnetic footprint. The analytical and experimental study conducted showed that vehicle magnetic footprint could be captured by mathematical models and that the magnetic sensor could be used to identify vehicle types.Copyright

Undergraduate Research: Experiences from a Three-Year Project

Undergraduates receive many benefits from participation in research activities, including exposure to advanced topics, introduction to research methods, and direct interaction with faculty and other students. Faculty and institutions benefit as well — fresh eyes in research projects, more energized research groups, and more engaged alumni. However, there are some challenges in designing a research program to work primarily with undergraduates. These include the students’ lack of exposure to advanced topics, short tenure on the project, and potentially lower commitment to the results. There are a number of ways to address these concerns, however. Short student tenure and limited student experience may be offset by breaking up a long-term project into manageable short-term chunks, identifying specific deliverables for each student, and implementing a rigorous data reporting and storage system. Student motivation may be enhanced by linking performance to grades or to an external competition. This paper presents results of using these and other techniques in a multi-disciplinary vehicle sensing research project involving sixteen undergraduates over a three-year period. Although individual student time on the project ranged from only three to twelve months, all students were able to contribute to the project. Student activities were grouped into individual and small group tasks, each with specific goals and timetables. Rigorous electronic documentation and data storage techniques were employed to enable new students to come up-to-speed quickly. A mix of course credits, supplemental pay, and an intercollegiate competition were used to maintain student motivation. Project successes include high student satisfaction, conference papers, a demonstration pre-crash sensing system, and participation in an international student competition.Copyright

DEVELOPMENT OF A COMPARISON INDEX AND A DATABASE FOR SEA MODEL RESULTS

This study analyzes methods of comparing SEA model results with experimental results for key traits. These qualitative traits provide the basis for correlation of model results with experimental results through the development of a comparison index. This paper formulates a comparison index and illustrates the application to SEA models. A customized data structure was designed around the comparison index to store all necessary aspects of the modeling, experiment and comparison results. This data structure was then implemented using relational database software. These new tools; the comparison index and the SEA database, will create a common language and a forum for SEA model results that will aid and stimulate dialog in the SEA modeling community and in tern, advance the science of SEA modeling.