Kwaku O. Prakah-Asante

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.

Real-time driver characterization during car following using stochastic evolving models

This paper studies characterizing the driving behavior during steady-state and transient car-following. An approach utilizing the online learning of an evolving Takagi-Sugeno fuzzy model that is combined with a probabilistic model is applied to capture the multi-model and evolving nature of the driving behavior. The approach is validated by testing on a vehicle during different driving conditions.

A two-stage-training support vector machine approach to predicting unintentional vehicle lane departure

ABSTRACT Advanced driver assistance systems, such as unintentional lane departure warning systems, have recently drawn much attention and efforts. In this study, we explored utilizing the nonlinear binary support vector machine (SVM) technique to predict unintentional lane departure, which is innovative, as the SVM methodology has not previously been attempted for this purpose in the literature. Furthermore, we developed a two-stage training scheme to improve SVMs prediction performance in terms of minimization of the number of false positive prediction errors. Experiment data generated by VIRTTEX, a hydraulically powered, 6-degrees-of-freedom moving base driving simulator at Ford Motor Company, were used. All the vehicle variables were sampled at 50 Hz and there were 16 drowsy drivers (about 3 hours of driving per subject) and six control drivers (approximately 20 minutes f driving each). In total, 3,508 unintentional lane departures occurred for the drowsy drivers and 23 for the control drivers. Our study involving these 22 drivers with a total of more than 7.5 million prediction decisions demonstrates that (a) excellent SVM prediction performance, measured by numbers of false positives (i.e., falsely predicted lane departures) and false negatives (i.e., lane departures failed to be predicted), was achieved when the prediction horizon was 0.6 seconds or less, (b) lateral position and lateral velocity worked the best as SVM input variables among the nine variable sets that we explored, and (c) the radial basis function performed the best as the SVM kernel function.

A support vector machine approach to unintentional vehicle lane departure prediction

Advanced driver assistance systems, such as unintentional lane departure warning systems, have recently drawn much attention and R & D efforts. Such a system may assist the driver by monitoring the driver or vehicle behaviors to predict/detect driving situations (e.g, lane departure) and alert the driver to take corrective action. In this paper, we show how the support vector machine (SVM) methodology can potentially provide enhanced unintentional lane departure prediction, which is a new method relative to literature. Our binary SVM employed the Radial Basis Function kernel to classify time series of select vehicle variables. The SVM was trained and tested using the driver experiment data generated by VIRTTEX, a hydraulically powered 6-degrees-of-freedom moving base driving simulator at Ford Motor Company. The data that we used represented 16 drowsy subjects (three-hour driving time per subject) and six control subjects (20 minutes driving per subject), all of which drove a simulated 2000 Volvo S80. The vehicle variables were all sampled at 50 Hz. There were a total of 3,508 unintentional lane departure occurrences for the drowsy drivers and only 23 for four of the six control drivers (two had none). The SVM was trained by over 60,000 time series examples (the actual number depended on the prediction horizon) created from 50% of the lane departures. The training data were removed from the testing data. During the testing, the SVM made a lane departure prediction at every sampling time for every one of the 22 drivers (over 6.8 million predictions in total). The overall sensitivity and specificity of the SVM with a 0.2-second prediction horizon for the 22 drivers were 99.77465% and 99.99997%, respectively. The SVM predicted, on average 0.200181 seconds in advance, lane departure correctly for all the control drivers, but missed 4 of the 1,758 and gave false positives for another 2 for the drowsy drivers. For the prediction horizon of 0.4s, there was 1 false positive case for the control subjects, and the false negative and false positive cases rose substantially to 10 and 137 for the drowsy drivers, respectively.

Hybrid Intelligent System for Driver Workload Estimation for tailored vehicle-driver communication and interaction

Advanced vehicle cabin technologies provide drivers infotainment, navigation, and enhanced convenient driving experiences. As interaction between the driver and in cabin technologies increases it is beneficial to provide tailored driver communication for an improved cabin experience. Assessment of the driving demand is of particular value to assist in modulating communication and vehicle system interactions with the driver. The complex vehicle, driver, and environment driving contexts require innovative prognostic approaches to driver workload inference. This paper presents a Hybrid Intelligent System for Driver Workload Estimation (HWLE). The real-time HWLE soft computing modules incorporate expert models, model-driven reasoning, and specialized computational intelligence techniques to compute an aggregated WLE-Index. The context depended WLE-Index facilitates tailoring vehicle-driver communication and interaction based on the driving demand. Results from application of the HWLE system under real-time conditions are presented.

Obstacle State Estimation For Imminent Crash Prediction & Countermeasure Deployment Decision-Making

This paper describes how predictive crash sensing and deployment control of safety systems require reliable and accurate kinematic information about potential obstacles in the host vehicle environment. The projected trajectories of obstacles in the path of the vehicle assist in activation of safety systems either before, or just after collision, for improved occupant protection. An analysis of filtering and estimation techniques applied to imminent crash conditions are presented in this paper. Optimization of design criteria used to achieve required response performance, and noise minimization, are evaluated based on the safety system to be activated. The predicted target information is applied in the coordinated deployment of injury mitigation safety systems.

Supervisory vehicle impact anticipation and control of safety systems

Occupant safety systems are incorporated in vehicles to meet the requirements of occupant protection. For optimum performance safety devices require tailored activation. This paper presents a supervisory control approach using predictive collision sensor information to augment the performance of safety systems. The supervisory approach determines the potential for a collision to occur, and assists in deployment decision-making. Decision-making is based on the obstacle range, and closing velocity information obtained from the anticipatory sensor, and a reference signal indicative of the host-vehicle deceleration. The multi-input supervisory control system consists of a fuzzy rule-based system, which determines the potential for a collision to occur, and deployment command generation for activation of respective safety devices.