Fling Tseng

From vehicle stability control to intelligent personal minder: Real-time vehicle handling limit warning and driver style characterization

This paper presents a novel approach to developing a driver advisory system that can warn the drivers of driving conditions close to the limit of vehicle handling. This advisory system utilizes intelligence inferred from vehicle states, measured signals, and the other computed variables used for active safety and vehicle control purposes. The onboard computing resources, algorithms, and sensors used to deduce such intelligence exist in todays electronic stability control systems.

Real-time driving behavior identification based on driver-in-the-loop vehicle dynamics and control

This paper studies to characterize driver driving behavior or driver control structure in real time. The three proposed methods use some of the signals such as the driver actuation measurements, the relative ranges between a leading and a following vehicle during a car-following maneuver, and the vehicle dynamic responses such as the vehicles longitudinal acceleration and deceleration. All the used signals exist in various electronic control systems. Vehicle tests were conducted on a test vehicle to illustrate the effectiveness of the proposed methods in identifying aggressive and cautious driving behaviors.

Adaptive real-time advisory system for fuel economy improvement in a hybrid electric vehicle

In this paper, we present a fuzzy logic based adaptive algorithm with a learning mechanism that estimates drivers long term and short term preferences. The algorithm represents a significant advancement to the capability of our previous non-adaptive real-time fuel economy advisory system that was implemented in a Ford Escape Hybrid [8][9]. This real-time advisory system proposed in [8][9]achieved improved fuel economy by providing visual and haptic feedbacks to the driver to change his or her driving style or behavior for a given vehicle condition. It was tuned to maximize fuel economy without significantly impacting the performance of the vehicle. Some drivers may perceive its feedback to be intrusive on one extreme while some other drivers may feel it ineffective on another extreme, depending on the drivers driving styles. The new adaptive algorithm learns drivers intentions by monitoring their driving styles and behaviors, and addresses the issues of intrusiveness of the advisory feedback. This proposed adaptive algorithm balances the competing requirements for improved fuel economy and drivability by maintaining vehicle performance that is acceptable to the current drivers driving style and behavior while providing mechanism to improve fuel economy. This system was developed and validated on the Ford Escape Hybrid vehicle. Experimental results show that the proposed adaptive algorithm is capable of improving drivers behavior and style without being perceived as ineffective or intrusive and achieves fuel economy improvements.

A mutual information based online evolving clustering approach and its applications

In this article, a new recursive evolving clustering method is proposed based on the well-known Gustafson–Kessel algorithm. The novelty of the proposed method involves the adaptation and integration of the mutual information based formulation to accommodate the Mahalanobis distance, which functions as the similarity measure and the unification of the clustering generation and pruning mechanisms. Example applications of the method are also discussed in the areas of data compression and knowledge extraction.