Crash Scene Re-Generation Using Structural Hidden Markov Models

1. Crash Scene Re-Generation Using Structural Hidden Markov Models Professor Djamel Bouchaffra (Advisor)Raef Aidibi (Ph.D. Student)Computer Science & Engineering 131 Dodge Hall Phone: 248-370-2242, email: bouchaff@oakland.edu 4 – Our Approach: Implement a learning system possessing the capability to search for structural information of the sequence of events preceding the collision; i.e., search for the structural information representing a component behavior. The National Transportation Safety Board has recommended that Automobile Manufacturers and the National Highway Traffic Safety Administration work cooperatively to gather information on automotive crashes, using collision sensing and recording devices. [National Transportation Safety Board Symposium On Recorders] 1 - Problem Statement Current crash scene re-generating methods lack the capabilities of searching for component structural information of the sequence of events preceding the collision, where design problems are not revealed efficiently & early enough. • 5 – The Structured Hidden Markov Models Approach: • The sequence that describes the entire pattern (S) is explained by a single Hidden Markov Model (Oi) which represents the Component Status Sequence. • This hidden Markov model is extended to contain structural information that are embedded within the pattern. • The contribution of each component to the entire pattern is evaluated. These components are merged together to describe the structure of this pattern. e.g. dynamic stability, injectors status, yaw angle, tire torque… Component Structure Status Natural Sensor Network e.g. vehicle speed, engine rpm, brake pressure, injectors durations. status, torque, … Center of mass ends up outside of the vehicle area of contact Design Defects Scene (Roll Over) e.g. accelerating, braking, decelerating, turning … State of the Vehicle Sequence describing the entire pattern Component Observation Sequence Structured HMM containing structural Information State Sequence representing State of the Vehicle, Monitored by Sensor Network Usually occur at intersections Side Impact Crash Scene (Road Design Defect) 5 – Re-Generating the Crash Sequence using SHMM: Probability Evaluation: Predicting the scene represented by the classified sequence, captured by the sensor network: The evaluation problem in SHMM consists of evaluating the probability for the model λ = (π;A; B; S;D), to produce the sequence O. is expressed as: State Decoding: The state decoding problem consists of determining the optimal state sequence: Find the best structural sequence that led into this scene class, that best esplains the sequence: Current Methodologies in Generating/Analyzing crash scenes (Puzzle Construction) They think I’ m a learning Machine … Structure Decoding: The structural decoding problem consists of determining the optimal structure sequence: Parameter Re-estimation: In the structural HMM paradigm, a forward-backward maximization algorithm to re-estimate the parameters contained in the model is utilized. • Who Would benefit from the Learning Machine? • Design Engineers • Police Officers • City Planners • Insurance Companies • Public • 6 – References: • D. Bouchaffra and J. Tan, "Structural Hidden Markov Models using a Relation of Equivalence: Application to Automotive Designs",  in Data Mining and Knowledge Discovery Journal, Springer-V, 2005 • D. Bouchaffra and J. Tan, "Introduction to Structural Hidden Markov Models: Application to Handwritten Numeral Recognition",  Intelligent Data Analysis Journal, IDA, Editor-in-Chief: A. Famili, Vol., 10:1, IOS Press, 2006 • D. Bouchaffra and J. Tan, "Structural Hidden Markov Model and Its Application in Automotive Industry", Enterprise Information Systems V, Camp, O.; Filipe, J.B.; Hammoudi, S.; Piattini, M.G. (Eds.), XIV, 332 p., Hardcover, ISBN: 1-4020-1726-X, Published by Springer, 2004 • Djamel Bouchaffra and Jun Tan, "Introduction to Structural HMM and it's Application in Pattern Classification", ANNIE'2004, Smart Engineering System Design-Neural Network, Fuzzy Logic, Evolutionary Programming, Complex Systems and Artificial Life, Nov. 7-10, 2004, University of Missouri-Rolla. • Djamel Bouchaffra and Jun Tan, "Introduction to the Concept of Structural HMM: Application to Mining Customers' Preferences for Automotive Designs", The 17th International Conference on Pattern Recognition (ICPR) Cambridge, United Kingdom, 23-26 August, 2004 (Proceedings Published by IEEE Computer Society). • Raef Aidibi, "Introduction to Hidden Markov Models Decision Processes (HMMDP)", International Computer System and Information Technology, ICSIT'05 (IEEE/ CDTA) July 19-22, 2005 Algiers 2 - Drawbacks 1 – Heavily based on assumptions 2 – Inaccurate & time-consuming 3 – Might lead to wrong conclusions 4 – Does not reveal design defects 5 – Diverts focus from core problem